1
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Rius C, Liu Y, Sixto-Costoya A, Valderrama-Zurián JC, Lucas-Dominguez R. State of open science in cancer research. Clin Transl Oncol 2024:10.1007/s12094-024-03468-7. [PMID: 38635076 DOI: 10.1007/s12094-024-03468-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 03/15/2024] [Indexed: 04/19/2024]
Abstract
PURPOSE This study has been focused on assessing the Open Science scenario of cancer research during the period 2011-2021, in terms of the derived scientific publications and raw data dissemination. METHODS A cancer search equation was executed in the Science Citation Index-Expanded, collecting the papers signed by at least one Spanish institution. The same search strategy was performed in the Data Citation Index to describe dataset diffusion. RESULTS 50,822 papers were recovered, 71% of which belong to first and second quartile journals. 59% of the articles were published in Open Access (OA) journals. The Open Access model and international collaboration positively conditioned the number of citations received. Among the most productive journals stood out Plos One, Cancers, and Clinical and Translational Oncology. 2693 genomics, proteomics and metabolomics datasets were retrieved, being Gene Expression Omnibus the favoured repository. CONCLUSIONS There has been an increase in oncology publications in Open Access. Most were published in first quartile journals and received higher citations than non-Open Access articles, as well as when oncological investigation was performed between international research teams, being relevant in the context of Open Science. Genetic repositories have been the preferred for sharing oncology datasets. Further investigation of research and data sharing in oncology is needed, supported by stronger Open Science policies, to achieve better data sharing practices among three scientific main pillars: researchers, publishers, and scientific organizations.
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Affiliation(s)
- Cristina Rius
- UISYS Group, Department of History of Science and Information Science, Faculty of Medicine and Dentistry, University of Valencia, Valencia, Spain
- Unit associated with the Interuniversity Institute for Advanced Research on the Evaluation of Science and the University (INAECU) UC3M-UAM, Madrid, Spain
- Spanish National Center for Cardiovascular Research (CNIC), Madrid, Spain
- CIBERCV, Madrid, Spain
| | - Yiming Liu
- UISYS Group, Department of History of Science and Information Science, Faculty of Medicine and Dentistry, University of Valencia, Valencia, Spain
- Unit associated with the Interuniversity Institute for Advanced Research on the Evaluation of Science and the University (INAECU) UC3M-UAM, Madrid, Spain
| | - Andrea Sixto-Costoya
- UISYS Group, Department of History of Science and Information Science, Faculty of Medicine and Dentistry, University of Valencia, Valencia, Spain
- Unit associated with the Interuniversity Institute for Advanced Research on the Evaluation of Science and the University (INAECU) UC3M-UAM, Madrid, Spain
- Department of Social Work and Social Services, Faculty of Social Sciences, Universitat de València, Valencia, Spain
| | - Juan Carlos Valderrama-Zurián
- UISYS Group, Department of History of Science and Information Science, Faculty of Medicine and Dentistry, University of Valencia, Valencia, Spain
- Unit associated with the Interuniversity Institute for Advanced Research on the Evaluation of Science and the University (INAECU) UC3M-UAM, Madrid, Spain
| | - Rut Lucas-Dominguez
- UISYS Group, Department of History of Science and Information Science, Faculty of Medicine and Dentistry, University of Valencia, Valencia, Spain.
- Unit associated with the Interuniversity Institute for Advanced Research on the Evaluation of Science and the University (INAECU) UC3M-UAM, Madrid, Spain.
- CIBERONC, Valencia, Spain.
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2
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Desai MA, Pasquetto IV, Jacobs AZ, Card D. An archival perspective on pretraining data. PATTERNS (NEW YORK, N.Y.) 2024; 5:100966. [PMID: 38645763 PMCID: PMC11026960 DOI: 10.1016/j.patter.2024.100966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Alongside an explosion in research and development related to large language models, there has been a concomitant rise in the creation of pretraining datasets-massive collections of text, typically scraped from the web. Drawing on the field of archival studies, we analyze pretraining datasets as informal archives-heterogeneous collections of diverse material that mediate access to knowledge. We use this framework to identify impacts of pretraining data creation and use beyond directly shaping model behavior and reveal how choices about what is included in pretraining data necessarily involve subjective decisions about values. In doing so, the archival perspective helps us identify opportunities for researchers who study the social impacts of technology to contribute to confronting the challenges and trade-offs that arise in creating pretraining datasets at this scale.
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Affiliation(s)
- Meera A. Desai
- School of Information, University of Michigan, Ann Arbor, MI, USA
| | - Irene V. Pasquetto
- College of Information Studies, University of Maryland, College Park, MD, USA
| | | | - Dallas Card
- School of Information, University of Michigan, Ann Arbor, MI, USA
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3
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Deng CH, Naithani S, Kumari S, Cobo-Simón I, Quezada-Rodríguez EH, Skrabisova M, Gladman N, Correll MJ, Sikiru AB, Afuwape OO, Marrano A, Rebollo I, Zhang W, Jung S. Genotype and phenotype data standardization, utilization and integration in the big data era for agricultural sciences. Database (Oxford) 2023; 2023:baad088. [PMID: 38079567 PMCID: PMC10712715 DOI: 10.1093/database/baad088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 10/17/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023]
Abstract
Large-scale genotype and phenotype data have been increasingly generated to identify genetic markers, understand gene function and evolution and facilitate genomic selection. These datasets hold immense value for both current and future studies, as they are vital for crop breeding, yield improvement and overall agricultural sustainability. However, integrating these datasets from heterogeneous sources presents significant challenges and hinders their effective utilization. We established the Genotype-Phenotype Working Group in November 2021 as a part of the AgBioData Consortium (https://www.agbiodata.org) to review current data types and resources that support archiving, analysis and visualization of genotype and phenotype data to understand the needs and challenges of the plant genomic research community. For 2021-22, we identified different types of datasets and examined metadata annotations related to experimental design/methods/sample collection, etc. Furthermore, we thoroughly reviewed publicly funded repositories for raw and processed data as well as secondary databases and knowledgebases that enable the integration of heterogeneous data in the context of the genome browser, pathway networks and tissue-specific gene expression. Based on our survey, we recommend a need for (i) additional infrastructural support for archiving many new data types, (ii) development of community standards for data annotation and formatting, (iii) resources for biocuration and (iv) analysis and visualization tools to connect genotype data with phenotype data to enhance knowledge synthesis and to foster translational research. Although this paper only covers the data and resources relevant to the plant research community, we expect that similar issues and needs are shared by researchers working on animals. Database URL: https://www.agbiodata.org.
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Affiliation(s)
- Cecilia H Deng
- Molecular and Digital Breeding, New Cultivar Innovation, The New Zealand Institute for Plant and Food Research Limited, 120 Mt Albert Road, Auckland 1025, New Zealand
| | - Sushma Naithani
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, USA
| | - Sunita Kumari
- Cold Spring Harbor Laboratory, 1 Bungtown Rd, Cold Spring Harbor, New York, NY 11724, USA
| | - Irene Cobo-Simón
- Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA
- Institute of Forest Science (ICIFOR-INIA, CSIC), Madrid, Spain
| | - Elsa H Quezada-Rodríguez
- Departamento de Producción Agrícola y Animal, Universidad Autónoma Metropolitana-Xochimilco, Ciudad de México, México
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - Maria Skrabisova
- Department of Biochemistry, Faculty of Science, Palacky University, Olomouc, Czech Republic
| | - Nick Gladman
- Cold Spring Harbor Laboratory, 1 Bungtown Rd, Cold Spring Harbor, New York, NY 11724, USA
- U.S. Department of Agriculture-Agricultural Research Service, NEA Robert W. Holley Center for Agriculture and Health, Cornell University, Ithaca, NY 14853, USA
| | - Melanie J Correll
- Agricultural and Biological Engineering Department, University of Florida, 1741 Museum Rd, Gainesville, FL 32611, USA
| | | | | | - Annarita Marrano
- Phoenix Bioinformatics, 39899 Balentine Drive, Suite 200, Newark, CA 94560, USA
| | | | - Wentao Zhang
- National Research Council Canada, 110 Gymnasium Pl, Saskatoon, Saskatchewan S7N 0W9, Canada
| | - Sook Jung
- Department of Horticulture, Washington State University, 303c Plant Sciences Building, Pullman, WA 99164-6414, USA
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4
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Kocak B, Yardimci AH, Yuzkan S, Keles A, Altun O, Bulut E, Bayrak ON, Okumus AA. Transparency in Artificial Intelligence Research: a Systematic Review of Availability Items Related to Open Science in Radiology and Nuclear Medicine. Acad Radiol 2023; 30:2254-2266. [PMID: 36526532 DOI: 10.1016/j.acra.2022.11.030] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/21/2022] [Accepted: 11/22/2022] [Indexed: 12/15/2022]
Abstract
RATIONALE AND OBJECTIVES Reproducibility of artificial intelligence (AI) research has become a growing concern. One of the fundamental reasons is the lack of transparency in data, code, and model. In this work, we aimed to systematically review the radiology and nuclear medicine papers on AI in terms of transparency and open science. MATERIALS AND METHODS A systematic literature search was performed in PubMed to identify original research studies on AI. The search was restricted to studies published in Q1 and Q2 journals that are also indexed on the Web of Science. A random sampling of the literature was performed. Besides six baseline study characteristics, a total of five availability items were evaluated. Two groups of independent readers including eight readers participated in the study. Inter-rater agreement was analyzed. Disagreements were resolved with consensus. RESULTS Following eligibility criteria, we included a final set of 194 papers. The raw data was available in about one-fifth of the papers (34/194; 18%). However, the authors made their private data available only in one paper (1/161; 1%). About one-tenth of the papers made their pre-modeling (25/194; 13%), modeling (28/194; 14%), or post-modeling files (15/194; 8%) available. Most of the papers (189/194; 97%) did not attempt to create a ready-to-use system for real-world usage. Data origin, use of deep learning, and external validation had statistically significantly different distributions. The use of private data alone was negatively associated with the availability of at least one item (p<0.001). CONCLUSION Overall rates of availability for items were poor, leaving room for substantial improvement.
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Affiliation(s)
- Burak Kocak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey.
| | - Aytul Hande Yardimci
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey
| | - Sabahattin Yuzkan
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey
| | - Ali Keles
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey
| | - Omer Altun
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey
| | - Elif Bulut
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey
| | - Osman Nuri Bayrak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey
| | - Ahmet Arda Okumus
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey
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5
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Ortiz-de-Urbina-Criado M, Abella A, García-Luna D. Open data-set identifier for open innovation and knowledge management. JOURNAL OF KNOWLEDGE MANAGEMENT 2023. [DOI: 10.1108/jkm-07-2022-0514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Purpose
This paper aims to highlight the importance of open data and the role that knowledge management and open innovation can play in its identification and use. Open data has great potential to create social and economic value, but its main problem is that it is often not easily reusable. The aim of this paper is to propose a unique identifier for open data-sets that would facilitate search and access to them and help to reduce heterogeneity in the publication of data in open data portals.
Design/methodology/approach
Considering a model of the impact process of open data reuse and based on the digital object identifier system, this paper develops a proposal of a unique identifier for open data-sets called Open Data-set Identifier (OpenDatId).
Findings
This paper presents some examples of the application and advantages of OpenDatId. For example, users can easily consult the available content catalogues, search the data in an automated way and examine the content for reuse. It is also possible to find out where this data comes from, solving the problems caused by the increasingly frequent federation of data in open data portals and enabling the creation of additional services based on open data.
Originality/value
From an integrated perspective of knowledge management and open innovation, this paper presents a new unique identifier for open data-sets (OpenDatId) and a new concept for data-set, the FAIR Open Data-sets.
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6
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Sadeh Y, Denejkina A, Karyotaki E, Lenferink LIM, Kassam-Adams N. Opportunities for improving data sharing and FAIR data practices to advance global mental health. Glob Ment Health (Camb) 2023; 10:e14. [PMID: 37860102 PMCID: PMC10581864 DOI: 10.1017/gmh.2023.7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 01/24/2023] [Accepted: 02/23/2023] [Indexed: 03/06/2023] Open
Abstract
It is crucial to optimize global mental health research to address the high burden of mental health challenges and mental illness for individuals and societies. Data sharing and reuse have demonstrated value for advancing science and accelerating knowledge development. The FAIR (Findable, Accessible, Interoperable, and Reusable) Guiding Principles for scientific data provide a framework to improve the transparency, efficiency, and impact of research. In this review, we describe ethical and equity considerations in data sharing and reuse, delineate the FAIR principles as they apply to mental health research, and consider the current state of FAIR data practices in global mental health research, identifying challenges and opportunities. We describe noteworthy examples of collaborative efforts, often across disciplinary and national boundaries, to improve Findability and Accessibility of global mental health data, as well as efforts to create integrated data resources and tools that improve Interoperability and Reusability. Based on this review, we suggest a vision for the future of FAIR global mental health research and suggest practical steps for researchers with regard to study planning, data preservation and indexing, machine-actionable metadata, data reuse to advance science and improve equity, metrics and recognition.
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Affiliation(s)
- Yaara Sadeh
- Center for Injury Research and Prevention, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Trauma Data Institute, Lovingston, VA, USA
| | - Anna Denejkina
- Graduate Research School, Western Sydney University, Penrith, NSW, Australia
- Translational Health Research Institute, Sydney, Australia
- Young and Resilient Research Centre, Sydney, Australia
| | - Eirini Karyotaki
- Department of Clinical, Neuro- and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Amsterdam Public Health Institute, Amsterdam, Netherlands
| | - Lonneke I. M. Lenferink
- Department of Psychology, Health & Technology, University of Twente, Enschede, Netherlands
- Department of Clinical Psychology, Utrecht University, Utrecht, Netherlands
- Department of Clinical Psychology and Experimental Psychopathology, University of Groningen, Groningen, Netherlands
| | - Nancy Kassam-Adams
- Center for Injury Research and Prevention, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Trauma Data Institute, Lovingston, VA, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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7
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Marsh DE, St. Andre S, Wagner T, Bell JA. Attitudes and uses of archival materials among science-based anthropologists. ARCHIVAL SCIENCE 2023; 23:1-25. [PMID: 36785781 PMCID: PMC9909654 DOI: 10.1007/s10502-023-09411-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/12/2023] [Indexed: 02/11/2023]
Abstract
While archival user studies have largely focused on humanities (and adjacent) scholars, this paper focuses on anthropologists engaged in scientific research. Based on qualitative results from an open-ended survey, we investigate how science-based anthropologists perceive and use archives in their work. We ask: How are science-based anthropologists and archaeologists reusing archival data in their research? What difficulties or barriers do they encounter in reusing archival data in scientific contexts? What attitudes or understandings about archival research are held by science-based anthropologists and archaeologists? Our findings primarily add to the body of literature about user experience in archives and more broadly to the emerging literature on archival data reuse. Major findings include (1) barriers and gatekeeping legacies that impact archival research and the ability of researchers to reuse data and (2) mixed perceptions about archives among researchers. We also discuss suggestions made by these communities of practice, and the ways that barriers to archival data reuse may stem from a lack of knowledge about core archival and information infrastructures among researcher communities. Together, this research showcases possible (re)uses of important primary source data in archives among scientific communities but highlights that barriers to access and misperceptions create a gap in exploiting that potential. We argue for a "re-imagining" of anthropological archives as relevant to contemporary communities and scientific pursuits toward a richer scientific research environment.
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Affiliation(s)
- Diana E. Marsh
- Department of Anthropology, College of Information Studies, University of Maryland, College Park, USA
| | | | - Travis Wagner
- College of Information Studies, University of Maryland, College Park, USA
| | - Joshua A. Bell
- National Museum of Natural History, Smithsonian Institution, Washington DC, USA
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8
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Akdeniz E, Borschewski KE, Breuer J, Voronin Y. Sharing social media data: The role of past experiences, attitudes, norms, and perceived behavioral control. Front Big Data 2023; 5:971974. [PMID: 36726996 PMCID: PMC9885192 DOI: 10.3389/fdata.2022.971974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 12/26/2022] [Indexed: 01/18/2023] Open
Abstract
Social media data (SMD) have become an important data source in the social sciences. The purpose of this paper is to investigate the experiences and practices of researchers working with SMD in their research and gain insights into researchers' sharing behavior and influencing factors for their decisions. To achieve these aims, we conducted a survey study among researchers working with SMD. The questionnaire covered different topics related to accessing, (re)using, and sharing SMD. To examine attitudes toward data sharing, perceived subjective norms, and perceived behavioral control, we used questions based on the Theory of Planned Behavior (TPB). We employed a combination of qualitative and quantitative analyses. The results of the qualitative analysis show that the main reasons for not sharing SMD were that sharing was not considered or needed, as well as legal and ethical challenges. The quantitative analyses reveal that there are differences in the relative importance of past sharing and reuse experiences, experienced challenges, attitudes, subjective norms, and perceived behavioral control as predictors of future SMD sharing intentions, depending on the way the data should be shared (publicly, with restricted access, or upon personal request). Importantly, the TPB variables have predictive power for all types of SMD sharing.
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Affiliation(s)
- Esra Akdeniz
- Data Services for the Social Sciences, GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany,*Correspondence: Esra Akdeniz ✉
| | - Kerrin Emilia Borschewski
- Data Services for the Social Sciences, GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany
| | - Johannes Breuer
- Survey Data Curation, GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany,Center for Advanced Internet Studies (CAIS), GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany
| | - Yevhen Voronin
- Data Services for the Social Sciences, GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany
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9
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Mittal D, Mease R, Kuner T, Flor H, Kuner R, Andoh J. Data management strategy for a collaborative research center. Gigascience 2022; 12:giad049. [PMID: 37401720 PMCID: PMC10318494 DOI: 10.1093/gigascience/giad049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 02/20/2023] [Accepted: 06/11/2023] [Indexed: 07/05/2023] Open
Abstract
The importance of effective research data management (RDM) strategies to support the generation of Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience data grows with each advance in data acquisition techniques and research methods. To maximize the impact of diverse research strategies, multidisciplinary, large-scale neuroscience research consortia face a number of unsolved challenges in RDM. While open science principles are largely accepted, it is practically difficult for researchers to prioritize RDM over other pressing demands. The implementation of a coherent, executable RDM plan for consortia spanning animal, human, and clinical studies is becoming increasingly challenging. Here, we present an RDM strategy implemented for the Heidelberg Collaborative Research Consortium. Our consortium combines basic and clinical research in diverse populations (animals and humans) and produces highly heterogeneous and multimodal research data (e.g., neurophysiology, neuroimaging, genetics, behavior). We present a concrete strategy for initiating early-stage RDM and FAIR data generation for large-scale collaborative research consortia, with a focus on sustainable solutions that incentivize incremental RDM while respecting research-specific requirements.
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Affiliation(s)
- Deepti Mittal
- Institute of Pharmacology, Heidelberg University, 69120 Heidelberg, Germany
| | - Rebecca Mease
- Institute of Physiology and Pathophysiology, Heidelberg University, 69120 Heidelberg, Germany
| | - Thomas Kuner
- Institute for Anatomy and Cell Biology, Heidelberg University, 69120 Mannheim, Germany
| | - Herta Flor
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
| | - Rohini Kuner
- Institute of Pharmacology, Heidelberg University, 69120 Heidelberg, Germany
| | - Jamila Andoh
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
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10
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A Method to Derive the Characteristic and Kinetic Parameters of 1,1-Bis(tert-butylperoxy)cyclohexane from DSC Measurements. Processes (Basel) 2022. [DOI: 10.3390/pr10051026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
A differential scanning calorimetry (DSC) experiment was carried out to determine the thermal characteristics of harmful substances. Most experimenters only use the results of measurement and rarely conduct in-depth research on the variety of information behind the measurement. This study used Wolfram’s Mathematica as a DSC measurement research tool to plot the peak curve and derive the characteristic parameters graphically for 1,1-Bis(tert-butylperoxy)cyclohexane. The research steps included raw data cleansing, peak curve normalization, characteristic parameter derivation, and total reaction heat calculation. The kinetic parameters of individual data were derived through the Borchardt and Daniels method, and the autocatalytic model was also verified. We applied the derived characteristic parameters to simulate the peak curve through the Gaussian curve model, which can be used for estimating the peak curve of other heating rates. The derived kinetic parameters were used to observe the effects on the peak curve. The simulation can be used to plan the test results at other rates in a similar temperature range and can also be used to explore the influence of different kinetic parameters on the configuration of the shape of the peak curve and a preliminary model test of materials for materials DSC research.
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11
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Wen B, Hu PJH, Ebrahimi M, Chen H. Key Factors Affecting User Adoption of Open-Access Data Repositories in Intelligence and Security Informatics: An Affordance Perspective. ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS 2022. [DOI: 10.1145/3460823] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Rich, diverse cybersecurity data are critical for efforts by the
intelligence and security informatics (ISI)
community. Although
open-access data repositories (OADRs)
provide tremendous benefits for ISI researchers and practitioners, determinants of their adoption remain understudied. Drawing on affordance theory and extant ISI literature, this study proposes a factor model to explain how the essential and unique affordances of an OADR (i.e., relevance, accessibility, and integration) affect individual professionals' intentions to use and collaborate with AZSecure, a major OADR. A survey study designed to test the model and hypotheses reveals that the effects of affordances on ISI professionals' intentions to use and collaborate are mediated by perceived usefulness and ease of use, which then jointly determine their perceived value. This study advances ISI research by specifying three important affordances of OADRs; it also contributes to extant technology adoption literature by scrutinizing and affirming the interplay of essential user acceptance and value perceptions to explain ISI professionals' adoptions of OADRs.
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Affiliation(s)
- Bo Wen
- Department of Operations and Information Systems, David Eccles School of Business, University of Utah, UT, USA
| | - Paul Jen-Hwa Hu
- Department of Operations and Information Systems, David Eccles School of Business, University of Utah, UT, USA
| | - Mohammadreza Ebrahimi
- School of Information Systems and Management, Muma College of Business, University of South Florida, FL, USA
| | - Hsinchun Chen
- Department of Management Information Systems, University of Arizona, AZ, USA
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12
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Hemphill L, Pienta A, Lafia S, Akmon D, Bleckley DA. How do properties of data, their curation, and their funding relate to reuse? J Assoc Inf Sci Technol 2022; 73:1432-1444. [PMID: 36246529 PMCID: PMC9542848 DOI: 10.1002/asi.24646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 02/11/2022] [Accepted: 03/09/2022] [Indexed: 11/30/2022]
Abstract
Despite large public investments in facilitating the secondary use of data, there is little information about the specific factors that predict data's reuse. Using data download logs from the Inter‐university Consortium for Political and Social Research (ICPSR), this study examines how data properties, curation decisions, and repository funding models relate to data reuse. We find that datasets deposited by institutions, subject to many curatorial tasks, and whose access and preservation is funded externally, are used more often. Our findings confirm that investments in data collection, curation, and preservation are associated with more data reuse.
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Affiliation(s)
- Libby Hemphill
- Inter‐university Consortium for Political and Social Research (ICPSR) University of Michigan Ann Arbor Michigan
- School of Information (UMSI) University of Michigan Ann Arbor Michigan
| | - Amy Pienta
- Inter‐university Consortium for Political and Social Research (ICPSR) University of Michigan Ann Arbor Michigan
| | - Sara Lafia
- Inter‐university Consortium for Political and Social Research (ICPSR) University of Michigan Ann Arbor Michigan
| | - Dharma Akmon
- Inter‐university Consortium for Political and Social Research (ICPSR) University of Michigan Ann Arbor Michigan
| | - David A. Bleckley
- Inter‐university Consortium for Political and Social Research (ICPSR) University of Michigan Ann Arbor Michigan
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13
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Thomer AK. Integrative data reuse at scientifically significant sites: Case studies at Yellowstone National Park and the La Brea Tar Pits. J Assoc Inf Sci Technol 2022. [DOI: 10.1002/asi.24620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Andrea K. Thomer
- School of Information University of Michigan Ann Arbor Michigan USA
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14
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Li C, Zhou Y, Zheng X, Zhang Z, Jiang L, Li Z, Wang P, Li J, Xu S, Wang Z. Tracing the footsteps of open research data in China. LEARNED PUBLISHING 2022. [DOI: 10.1002/leap.1439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Chengzan Li
- Computer Network Information Center Chinese Academy of Sciences Beijing China
| | - Yuanchun Zhou
- Computer Network Information Center Chinese Academy of Sciences Beijing China
| | - Xiaohuan Zheng
- Administrative Office Chinese Academy of Sciences Beijing China
| | - Zeyu Zhang
- Computer Network Information Center Chinese Academy of Sciences Beijing China
| | - Lulu Jiang
- Computer Network Information Center Chinese Academy of Sciences Beijing China
| | - Zongwen Li
- Computer Network Information Center Chinese Academy of Sciences Beijing China
| | - Pengyao Wang
- Computer Network Information Center Chinese Academy of Sciences Beijing China
| | - Jianhui Li
- Computer Network Information Center Chinese Academy of Sciences Beijing China
| | - Songyuan Xu
- Computer Network Information Center Chinese Academy of Sciences Beijing China
- University of Chinese Academy of Sciences Beijing China
| | - Zhanjie Wang
- Computer Network Information Center Chinese Academy of Sciences Beijing China
- University of Chinese Academy of Sciences Beijing China
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15
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Kenfield AS, Woolcott L, Thompson S, Kelly EJ, Shiri A, Muglia C, Masood K, Chapman J, Jefferson D, Morales ME. Toward a definition of digital object reuse. DIGITAL LIBRARY PERSPECTIVES 2022. [DOI: 10.1108/dlp-06-2021-0044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
The purpose of this paper is to present conceptual definitions for digital object use and reuse. Typically, assessment of digital repository content struggles to go beyond traditional usage metrics such as clicks, views or downloads. This is problematic for galleries, libraries, archives, museums and repositories (GLAMR) practitioners because use assessment does not tell a nuanced story of how users engage with digital content and objects.
Design/methodology/approach
This paper reviews prior research and literature aimed at defining use and reuse of digital content in GLAMR contexts and builds off of this group’s previous research to devise a new model for defining use and reuse called the use-reuse matrix.
Findings
This paper presents the use-reuse matrix, which visually represents eight categories and numerous examples of use and reuse. Additionally, the paper explores the concept of “permeability” and its bearing on the matrix. It concludes with the next steps for future research and application in the development of the Digital Content Reuse Assessment Framework Toolkit (D-CRAFT).
Practical implications
The authors developed this model and definitions to inform D-CRAFT, an Institute of Museum and Library Services National Leadership Grant project. This toolkit is being developed to help practitioners assess reuse at their own institutions.
Originality/value
To the best of the authors’ knowledge, this paper is one of the first to propose distinct definitions that describe and differentiate between digital object use and reuse in the context of assessing digital collections and data.
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Sarkar D, Bortolamiol S, Gogarten JF, Hartter J, Hou R, Kagoro W, Omeja P, Tumwesigye C, Chapman CA. Exploring multiple dimensions of conservation success: Long‐term wildlife trends, anti‐poaching efforts and revenue sharing in Kibale National Park, Uganda. Anim Conserv 2022. [DOI: 10.1111/acv.12765] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- D. Sarkar
- Department of Geography and Environmental Studies Carleton University Ottawa Canada
- Department of Geography University College Cork Cork Ireland
| | - S. Bortolamiol
- Geo212 Paris France
- UMR 7533 Laboratoire Dynamiques Sociales et Recomposition des Espaces CNRS Aubervilliers France
| | - J. F. Gogarten
- Viral Evolution and Epidemiology of Highly Pathogenic Microorganisms Robert Koch Institute Berlin Germany
- Applied Zoology and Nature Conservation ‐ University of Greifswald Greifswald Germany
| | - J. Hartter
- Environmental Studies Program University of Colorado Boulder Boulder Colorado USA
| | - R. Hou
- Shaanxi Key Laboratory for Animal Conservation Northwest University Xi’an China
| | - W. Kagoro
- Uganda Wildlife Authority Kampala Uganda
| | - P. Omeja
- Makerere University Biological Field Station Fort Portal Uganda
| | | | - C. A. Chapman
- Shaanxi Key Laboratory for Animal Conservation Northwest University Xi’an China
- Wilson Center Washington DC USA
- Department of Anthropology George Washington University Washington DC USA
- School of Life Sciences University of KwaZulu‐Natal Pietermaritzburg South Africa
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Shraim R, Nieuwenhuis BPS. The search for Schizosaccharomyces fission yeasts in environmental meta-transcriptomes. Yeast 2021; 39:83-94. [PMID: 34967063 DOI: 10.1002/yea.3689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 12/09/2021] [Accepted: 12/23/2021] [Indexed: 11/07/2022] Open
Abstract
Fission yeast is an important model organism in evolutionary genetics and cell biology research. Nevertheless, most research is limited to a single laboratory strain and knowledge of its natural occurrence is limited, which reduces our understanding of its life history and hinders isolation of new strains from nature. Understanding the natural diversity of fission yeast can provide insight into its genetic and phenotypic diversity and the evolutionary processes that shaped these. Here we aimed to identify candidate natural habitats of fission yeasts by searching through a large collection of publicly available environmental metatranscriptomic datasets. Using a custom pipeline, we processed over 13,000 NCBI SRA accessions, from a wide range of 34 different environmental categories. Overall, we found a very low abundance of putative yeast transcripts, with most fission yeast signatures coming from the categories of 'food' and 'terrestrial arthropods'. Additionally, a signal could be found in a variety of marine and fresh aquatic habitats. Our results do not provide a conclusive answer on the natural habitat of fission yeasts, but our analysis further narrows the range of locations where fission yeasts naturally occur.
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Affiliation(s)
- Rasha Shraim
- The SFI Centre for Research Training in Genomics Data Sciences, National University of Ireland Galway and Department of Public Health and Primary Care, School of Medicine, Trinity College Dublin, Republic of Ireland.,Division of Evolutionary Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Germany
| | - Bart P S Nieuwenhuis
- Division of Evolutionary Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Germany
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Nevin J, Lees M, Groth P. The non-linear impact of data handling on network diffusion models. PATTERNS (NEW YORK, N.Y.) 2021; 2:100397. [PMID: 34950910 PMCID: PMC8672192 DOI: 10.1016/j.patter.2021.100397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 09/23/2021] [Accepted: 11/02/2021] [Indexed: 12/04/2022]
Abstract
Many computational models rely on real-world data, and the steps required in moving from data collection, to data preparation, to model calibration, and input are becoming increasingly complex. Errors in data can lead to errors in model output that might invalidate conclusions in extreme cases. While the challenge of errors in data collection have been analyzed in the literature, here we highlight the importance of data handling in the modeling and simulation process, and how particular data handling errors can lead to errors in model output. We develop a framework for assessing the impact of potential data errors for models of spreading processes on networks, a broad class of models that capture many important real-world phenomena (e.g., epidemics, rumor spread, etc.). We focus on the susceptible-infected-removed (SIR) and Threshold models and examine how systematic errors in data handling impact the predicted spread of a virus (or information). Our results demonstrate that data handling errors can have significant impact on model conclusions especially in critical regions of a system.
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Affiliation(s)
- James Nevin
- University of Amsterdam, Amsterdam, the Netherlands
| | - Michael Lees
- University of Amsterdam, Amsterdam, the Netherlands
| | - Paul Groth
- University of Amsterdam, Amsterdam, the Netherlands
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Encoding Collective Knowledge, Instructing Data Reusers: The Collaborative Fixation of a Digital Scientific Data Set. Comput Support Coop Work 2021; 30:463-505. [PMID: 34840429 PMCID: PMC8608782 DOI: 10.1007/s10606-021-09407-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/30/2021] [Indexed: 11/07/2022]
Abstract
This article provides a novel perspective on the use and reuse of scientific data by providing a chronological ethnographic account and analysis of how a team of researchers prepared an astronomical catalogue (a table of measured properties of galaxies) for public release. Whereas much existing work on data reuse has focused on information about data (such as metadata), whose form or lack has been described as a hurdle for reusing data successfully, I describe how data makers tried to instruct users through the processed data themselves. The fixation of this catalogue was a negotiation, resulting in what was acceptable to team members and coherent with the diverse data uses pertinent to their completed work. It was through preparing their catalogue as an ‘instructing data object’ that this team seeked to encode its members’ knowledge of how the data were processed and to make it consequential for users by devising methodical ways to structure anticipated uses. These methods included introducing redundancies that would help users to self-correct mistaken uses, selectively deleting data, and deflecting accountability through making notational choices. They dwell on an understanding of knowledge not as exclusively propositional (such as the belief in propositions), but as embedded in witnessable activities and the products of these activities. I discuss the implications of this account for philosophical notions of collective knowledge and for theorizing coordinative artifacts in CSCW. Eventually, I identify a tension between ‘using algorithms’ and ‘doing science’ in preparing data sets and show how it was resolved in this case.
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Paullada A, Raji ID, Bender EM, Denton E, Hanna A. Data and its (dis)contents: A survey of dataset development and use in machine learning research. PATTERNS (NEW YORK, N.Y.) 2021; 2:100336. [PMID: 34820643 PMCID: PMC8600147 DOI: 10.1016/j.patter.2021.100336] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
In this work, we survey a breadth of literature that has revealed the limitations of predominant practices for dataset collection and use in the field of machine learning. We cover studies that critically review the design and development of datasets with a focus on negative societal impacts and poor outcomes for system performance. We also cover approaches to filtering and augmenting data and modeling techniques aimed at mitigating the impact of bias in datasets. Finally, we discuss works that have studied data practices, cultures, and disciplinary norms and discuss implications for the legal, ethical, and functional challenges the field continues to face. Based on these findings, we advocate for the use of both qualitative and quantitative approaches to more carefully document and analyze datasets during the creation and usage phases.
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Affiliation(s)
| | | | - Emily M. Bender
- Department of Linguistics, University of Washington, Seattle, WA, USA
| | | | - Alex Hanna
- Google Research, New York, NY, USA
- Google Research, San Francisco, CA, USA
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21
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Khan N, Thelwall M, Kousha K. Are data repositories fettered? A survey of current practices, challenges and future technologies. ONLINE INFORMATION REVIEW 2021. [DOI: 10.1108/oir-04-2021-0204] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeThe purpose of this study is to explore current practices, challenges and technological needs of different data repositories.Design/methodology/approachAn online survey was designed for data repository managers, and contact information from the re3data, a data repository registry, was collected to disseminate the survey.FindingsIn total, 189 responses were received, including 47% discipline specific and 34% institutional data repositories. A total of 71% of the repositories reporting their software used bespoke technical frameworks, with DSpace, EPrint and Dataverse being commonly used by institutional repositories. Of repository managers, 32% reported tracking secondary data reuse while 50% would like to. Among data reuse metrics, citation counts were considered extremely important by the majority, followed by links to the data from other websites and download counts. Despite their perceived usefulness, repository managers struggle to track dataset citations. Most repository managers support dataset and metadata quality checks via librarians, subject specialists or information professionals. A lack of engagement from users and a lack of human resources are the top two challenges, and outreach is the most common motivator mentioned by repositories across all groups. Ensuring findable, accessible, interoperable and reusable (FAIR) data (49%), providing user support for research (36%) and developing best practices (29%) are the top three priorities for repository managers. The main recommendations for future repository systems are as follows: integration and interoperability between data and systems (30%), better research data management (RDM) tools (19%), tools that allow computation without downloading datasets (16%) and automated systems (16%).Originality/valueThis study identifies the current challenges and needs for improving data repository functionalities and user experiences.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0204
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Igumbor JO, Bosire EN, Vicente-Crespo M, Igumbor EU, Olalekan UA, Chirwa TF, Kinyanjui SM, Kyobutungi C, Fonn S. Considerations for an integrated population health databank in Africa: lessons from global best practices. Wellcome Open Res 2021; 6:214. [PMID: 35224211 PMCID: PMC8844538 DOI: 10.12688/wellcomeopenres.17000.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/12/2021] [Indexed: 12/17/2022] Open
Abstract
Background: The rising digitisation and proliferation of data sources and repositories cannot be ignored. This trend expands opportunities to integrate and share population health data. Such platforms have many benefits, including the potential to efficiently translate information arising from such data to evidence needed to address complex global health challenges. There are pockets of quality data on the continent that may benefit from greater integration. Integration of data sources is however under-explored in Africa. The aim of this article is to identify the requirements and provide practical recommendations for developing a multi-consortia public and population health data-sharing framework for Africa. Methods: We conducted a narrative review of global best practices and policies on data sharing and its optimisation. We searched eight databases for publications and undertook an iterative snowballing search of articles cited in the identified publications. The Leximancer software © enabled content analysis and selection of a sample of the most relevant articles for detailed review. Themes were developed through immersion in the extracts of selected articles using inductive thematic analysis. We also performed interviews with public and population health stakeholders in Africa to gather their experiences, perceptions, and expectations of data sharing. Results: Our findings described global stakeholder experiences on research data sharing. We identified some challenges and measures to harness available resources and incentivise data sharing. We further highlight progress made by the different groups in Africa and identified the infrastructural requirements and considerations when implementing data sharing platforms. Furthermore, the review suggests key reforms required, particularly in the areas of consenting, privacy protection, data ownership, governance, and data access. Conclusions: The findings underscore the critical role of inclusion, social justice, public good, data security, accountability, legislation, reciprocity, and mutual respect in developing a responsive, ethical, durable, and integrated research data sharing ecosystem.
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Affiliation(s)
- Jude O. Igumbor
- School of Public Health, University of the Witwatersrand, Johannesburg, Gauteng, 2193, South Africa
| | - Edna N. Bosire
- School of Public Health, University of the Witwatersrand, Johannesburg, Gauteng, 2193, South Africa
| | - Marta Vicente-Crespo
- School of Public Health, University of the Witwatersrand, Johannesburg, Gauteng, 2193, South Africa
- African Population and Health Research Centre, Nairobi, Kenya
| | - Ehimario U. Igumbor
- Nigeria Centre for Disease Control, Abuja, Nigeria
- School of Public Health, University of the Western Cape, Cape Town, Western Cape, South Africa
| | - Uthman A. Olalekan
- Warwick-Centre for Applied Health Research and Delivery (WCAHRD), Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Tobias F. Chirwa
- School of Public Health, University of the Witwatersrand, Johannesburg, Gauteng, 2193, South Africa
| | | | | | - Sharon Fonn
- School of Public Health, University of the Witwatersrand, Johannesburg, Gauteng, 2193, South Africa
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Macdonald JA, Francis LM, Skouteris H, Youssef GJ, Graeme LG, Williams J, Fletcher RJ, Knight T, Milgrom J, Di Manno L, Olsson CA, Greenwood CJ. Cohort profile: the Men and Parenting Pathways (MAPP) Study: a longitudinal Australian cohort study of men's mental health and well-being at the normative age for first-time fatherhood. BMJ Open 2021; 11:e047909. [PMID: 34315795 PMCID: PMC8317085 DOI: 10.1136/bmjopen-2020-047909] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
PURPOSE The Men and Parenting Pathways (MAPP) Study is a prospective investigation of men's mental health and well-being across the normative age for transitioning to fatherhood. This includes trajectories and outcomes for men who do and do not become fathers across five annual waves of the study. PARTICIPANTS Australian resident, English-speaking men aged 28-32 years at baseline were eligible. Recruitment was over a 2-year period (2015-2017) via social and traditional media and through engagement with study partners. Eight hundred and eighteen eligible men consented to participate. Of these, 664 men completed the first online survey of whom 608 consented to ongoing participation. Of the ongoing sample, 83% have participated in at least two of the first three annual online surveys. FINDINGS TO DATE Three waves of data collection are complete. The first longitudinal analysis of MAPP data, published in 2020, identified five profiles that characterise men's patterns of depressive symptom severity and presentations of anger. Profiles indicating pronounced anger and depressive symptoms were associated with fathers' lack of perceived social support, and problems with coparenting and bonding with infants. In a second study, MAPP data were combined with three other Australian cohorts in a meta-analysis of associations between fathers' self-reported sleep problems up to 3 years postpartum and symptoms of depression, anxiety and stress. Adjusted meta-analytic associations between paternal sleep and mental health risk ranged from 0.25 to 0.37. FUTURE PLANS MAPP is an ongoing cohort study. Waves 4 and 5 data will be ready for analyses at the end of 2021. Future investigations will include crossed-lagged and trajectory analyses that assess inter-relatedness and changing social networks, mental health, work and family life. A nested study of COVID-19 pandemic-related mental health and coping will add two further waves of data collection in a subsample of MAPP participants.
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Affiliation(s)
- Jacqui A Macdonald
- Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health, Deakin University, Geelong, Victoria, Australia
- Centre for Adolescent Health, Murdoch Childrens Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria, Australia
| | - Lauren M Francis
- Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health, Deakin University, Geelong, Victoria, Australia
| | - Helen Skouteris
- Health and Social Care Unit, School of Public Health and Preventive Medicine, Monash University, Clayton, Victoria, Australia
- Warwick Business School, University of Warwick, Coventry, West Midlands, UK
| | - George J Youssef
- Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health, Deakin University, Geelong, Victoria, Australia
- Centre for Adolescent Health, Murdoch Childrens Research Institute, Parkville, Victoria, Australia
| | - Liam G Graeme
- Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health, Deakin University, Geelong, Victoria, Australia
| | - Joanne Williams
- Department of Health Sciences and Biostatistics, Swinburne University of Technology-Hawthorn Campus, Hawthorn, Victoria, Australia
- School of Health and Social Development, Deakin University Faculty of Health, Burwood, Victoria, Australia
| | - Richard J Fletcher
- Family Action Centre, The University of Newcastle, Callaghan, New South Wales, Australia
| | - Tess Knight
- Cairnmillar Institute, Camberwell, Victoria, Australia
| | - Jeannette Milgrom
- Parent-Infant Research Institute, Austin Health, Heidelberg, Victoria, Australia
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Victoria, Australia
| | - Laura Di Manno
- Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health, Deakin University, Geelong, Victoria, Australia
| | - Craig A Olsson
- Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health, Deakin University, Geelong, Victoria, Australia
- Centre for Adolescent Health, Murdoch Childrens Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria, Australia
| | - Christopher J Greenwood
- Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health, Deakin University, Geelong, Victoria, Australia
- Centre for Adolescent Health, Murdoch Childrens Research Institute, Parkville, Victoria, Australia
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Krug RM, Petchey OL. Metadata Made Easy: Develop and Use Domain-Specific Metadata Schemes by following the dmdScheme approach. Ecol Evol 2021; 11:9174-9181. [PMID: 34306613 PMCID: PMC8293710 DOI: 10.1002/ece3.7764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 04/22/2021] [Accepted: 05/14/2021] [Indexed: 11/24/2022] Open
Abstract
Metadata plays an essential role in the long-term preservation, reuse, and interoperability of data. Nevertheless, creating useful metadata can be sufficiently difficult and weakly enough incentivized that many datasets may be accompanied by little or no metadata. One key challenge is, therefore, how to make metadata creation easier and more valuable. We present a solution that involves creating domain-specific metadata schemes that are as complex as necessary and as simple as possible. These goals are achieved by co-development between a metadata expert and the researchers (i.e., the data creators). The final product is a bespoke metadata scheme into which researchers can enter information (and validate it) via the simplest of interfaces: a web browser application and a spreadsheet.We provide the R package dmdScheme (dmdScheme: An R package for working with domain specific MetaData schemes (Version v0.9.22), 2019) for creating a template domain-specific scheme. We describe how to create a domain-specific scheme from this template, including the iterative co-development process, and the simple methods for using the scheme, and simple methods for quality assessment, improvement, and validation.The process of developing a metadata scheme following the outlined approach was successful, resulting in a metadata scheme which is used for the data generated in our research group. The validation quickly identifies forgotten metadata, as well as inconsistent metadata, therefore improving the quality of the metadata. Multiple output formats are available, including XML.Making the provision of metadata easier while also ensuring high quality must be a priority for data curation initiatives. We show how both objectives are achieved by close collaboration between metadata experts and researchers to create domain-specific schemes. A near-future priority is to provide methods to interface domain-specific schemes with general metadata schemes, such as the Ecological Metadata Language, to increase interoperability.
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Affiliation(s)
- Rainer M. Krug
- Department of Evolutionary Biology and Environmental StudiesUniversity of ZürichZurichSwitzerland
| | - Owen L. Petchey
- Department of Evolutionary Biology and Environmental StudiesUniversity of ZürichZurichSwitzerland
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25
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Imker HJ, Luong H, Mischo WH, Schlembach MC, Wiley C. An examination of data reuse practices within highly cited articles of faculty at a research university. JOURNAL OF ACADEMIC LIBRARIANSHIP 2021. [DOI: 10.1016/j.acalib.2021.102369] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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26
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Kaplan NE, Baker KS, Karasti H. Long live the data! Embedded data management at a long‐term ecological research site. Ecosphere 2021. [DOI: 10.1002/ecs2.3493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Nicole E. Kaplan
- Rangeland Resources and Systems Research Unit United States Department of Agriculture, Agricultural Research Service Fort Collins Colorado80526USA
| | - Karen S. Baker
- School of Information Sciences University of Illinois Urbana Champaign Champaign Illinois61820USA
| | - Helena Karasti
- IT University of Copenhagen Rued Langgaards Vej 7 Copenhagen2300Denmark
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27
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Kim Y. A study of the roles of metadata standard and data repository in science, technology, engineering and mathematics researchers' data reuse. ONLINE INFORMATION REVIEW 2021. [DOI: 10.1108/oir-09-2020-0431] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeThis research investigates how the availabilities of both metadata standards and data repositories influence researchers' data reuse intentions either directly or indirectly as mediated by the norms of data reuse and their attitudes toward data reuse.Design/methodology/approachThe theory of planned behavior (TPB) was employed to develop the research model of researchers' data reuse intentions, focusing on the roles of metadata standards, data repositories and norms of data reuse. The proposed research model was evaluated using the structural equation modeling (SEM) method based on the survey responses received from 811 STEM (science, technology, engineering and mathematics) researchers in the United States.FindingsThis research found that the availabilities of both metadata standards and data repositories significantly affect STEM researchers' norm of data reuse, which influences their data reuse intentions as mediated by their attitudes toward data reuse. This research also found that both the availability of data repositories and the norm of data reuse have a direct influence on data reuse intentions and that norm of data reuse significantly increases the effect of attitude toward data reuse on data reuse intention as a moderator.Research limitations/implicationsThe modified model of TPB provides a new perspective in apprehending the roles of resource facilitating conditions such as the availabilities of metadata standards and data repositories in an individual's attitude, norm and their behavioral intention to conduct a certain behavior.Practical implicationsThis study suggests that scientific communities need to develop more supportive metadata standards and data repositories by considering their roles in enhancing the community norm of data reuse, which eventually lead to data reuse behaviors.Originality/valueThis study sheds light on the mechanism of metadata standard and data repository in researchers' data reuse behaviors through their community norm of data reuse; this can help scientific communities and academic institutions to better support researchers in their data sharing and reuse behaviors.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-09-2020-0431
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28
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On Earth as It Is in Heaven: Proxy Measurements to Assess Sustainable Development Goals at the Company Level through CSR Indicators. SUSTAINABILITY 2021. [DOI: 10.3390/su13020914] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In recent years, we have witnessed dramatic changes in the following two regards: First, Corporate Social Responsibility (CSR) practices have become ubiquitous in companies, and second, the challenge that Sustainable Development (SD) presents to society and to the planet has been illustrated in the United Nations’ Sustainable Development Goals (SDGs). In this context, this study aims to identify the extent to which companies address the SDGs through their CSR practices. We conducted the present research by first performing a content analysis to determine the communalities between CSR practices and the SDGs. Then, we performed a quantitative analysis to assess the performance of various companies in terms of common aspects of CSR and SDGs. The main findings are as follows: (1) Not all SDGs are related to CSR practices; (2) companies perform differently in their CSR practices and, consequently, make different contributions to the SDGs; and (3) there is little difference among company profiles regarding their performances in CSR practices towards the SDGs. The main contributions of this study are, first, that it provides a new perspective on the relationship between CSR and SD and, second, the creation of a list of what can be considered the minimum requirements for CSR practices if the SDGs are to be reached.
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29
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Borgman CL, Wofford MF, Golshan MS, Darch PT. Collaborative qualitative research at scale: Reflections on 20 years of acquiring global data and making data global. J Assoc Inf Sci Technol 2021. [DOI: 10.1002/asi.24439] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Christine L. Borgman
- Center for Knowledge Infrastructures University of California, Los Angeles (UCLA) Los Angeles California USA
| | - Morgan F. Wofford
- Center for Knowledge Infrastructures University of California, Los Angeles (UCLA) Los Angeles California USA
| | - Milena S. Golshan
- Center for Knowledge Infrastructures University of California, Los Angeles (UCLA) Los Angeles California USA
| | - Peter T. Darch
- Center for Knowledge Infrastructures University of California, Los Angeles (UCLA) Los Angeles California USA
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Koesten L, Vougiouklis P, Simperl E, Groth P. Dataset Reuse: Toward Translating Principles to Practice. PATTERNS (NEW YORK, N.Y.) 2020; 1:100136. [PMID: 33294873 PMCID: PMC7691392 DOI: 10.1016/j.patter.2020.100136] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 08/28/2020] [Accepted: 10/12/2020] [Indexed: 10/28/2022]
Abstract
The web provides access to millions of datasets that can have additional impact when used beyond their original context. We have little empirical insight into what makes a dataset more reusable than others and which of the existing guidelines and frameworks, if any, make a difference. In this paper, we explore potential reuse features through a literature review and present a case study on datasets on GitHub, a popular open platform for sharing code and data. We describe a corpus of more than 1.4 million data files, from over 65,000 repositories. Using GitHub's engagement metrics as proxies for dataset reuse, we relate them to reuse features from the literature and devise an initial model, using deep neural networks, to predict a dataset's reusability. This demonstrates the practical gap between principles and actionable insights that allow data publishers and tools designers to implement functionalities that provably facilitate reuse.
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Affiliation(s)
| | | | | | - Paul Groth
- University of Amsterdam, Amsterdam 1090 GH, the Netherlands
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31
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Sielemann K, Hafner A, Pucker B. The reuse of public datasets in the life sciences: potential risks and rewards. PeerJ 2020; 8:e9954. [PMID: 33024631 PMCID: PMC7518187 DOI: 10.7717/peerj.9954] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 08/25/2020] [Indexed: 12/13/2022] Open
Abstract
The 'big data' revolution has enabled novel types of analyses in the life sciences, facilitated by public sharing and reuse of datasets. Here, we review the prodigious potential of reusing publicly available datasets and the associated challenges, limitations and risks. Possible solutions to issues and research integrity considerations are also discussed. Due to the prominence, abundance and wide distribution of sequencing data, we focus on the reuse of publicly available sequence datasets. We define 'successful reuse' as the use of previously published data to enable novel scientific findings. By using selected examples of successful reuse from different disciplines, we illustrate the enormous potential of the practice, while acknowledging the respective limitations and risks. A checklist to determine the reuse value and potential of a particular dataset is also provided. The open discussion of data reuse and the establishment of this practice as a norm has the potential to benefit all stakeholders in the life sciences.
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Affiliation(s)
- Katharina Sielemann
- Genetics and Genomics of Plants, Center for Biotechnology (CeBiTec) & Faculty of Biology, Bielefeld University, Bielefeld, Germany
- Graduate School DILS, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Bielefeld University, Bielefeld, Germany
| | - Alenka Hafner
- Genetics and Genomics of Plants, Center for Biotechnology (CeBiTec) & Faculty of Biology, Bielefeld University, Bielefeld, Germany
- Current Affiliation: Intercollege Graduate Degree Program in Plant Biology, Penn State University, University Park, State College, PA, United States of America
| | - Boas Pucker
- Genetics and Genomics of Plants, Center for Biotechnology (CeBiTec) & Faculty of Biology, Bielefeld University, Bielefeld, Germany
- Evolution and Diversity, Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom
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Bafeta A, Bobe J, Clucas J, Gonsalves PP, Gruson-Daniel C, Hudson KL, Klein A, Krishnakumar A, McCollister-Slipp A, Lindner AB, Misevic D, Naslund JA, Nebeker C, Nikolaidis A, Pasquetto I, Sanchez G, Schapira M, Scheininger T, Schoeller F, Sólon Heinsfeld A, Taddei F. Ten simple rules for open human health research. PLoS Comput Biol 2020; 16:e1007846. [PMID: 32881878 PMCID: PMC7470254 DOI: 10.1371/journal.pcbi.1007846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Aïda Bafeta
- Center for Research and Interdisciplinarity (CRI), Université de Paris, INSERM U1284, Paris, France
| | - Jason Bobe
- Institute for Next Generation Healthcare, New York, New York, United States of America
| | - Jon Clucas
- MATTER Lab, Child Mind Institute, New York, New York, United States of America
| | | | - Célya Gruson-Daniel
- COSTECH, Université de Technologie de Compiègne, Compiègne, France; LabCMO, Université du Québec à Montréal, Université Laval, Montreal, Canada
| | - Kathy L. Hudson
- Hudson Works LLC, Washington, District of Columbia, United States of America
| | - Arno Klein
- MATTER Lab, Child Mind Institute, New York, New York, United States of America
| | - Anirudh Krishnakumar
- Center for Research and Interdisciplinarity (CRI), Université de Paris, INSERM U1284, Paris, France
| | | | - Ariel B. Lindner
- Center for Research and Interdisciplinarity (CRI), Université de Paris, INSERM U1284, Paris, France
| | - Dusan Misevic
- Center for Research and Interdisciplinarity (CRI), Université de Paris, INSERM U1284, Paris, France
| | - John A. Naslund
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Camille Nebeker
- Department of Family Medicine and Public Health, School of Medicine, University of California San Diego, San Diego, California, United States of America
| | - Aki Nikolaidis
- Center for the Developing Brain, Child Mind Institute, New York, New York, United States of America
| | - Irene Pasquetto
- Harvard Kennedy School, Harvard University, Cambridge, Massachusetts, United States of America
| | | | - Matthieu Schapira
- Structural Genomics Consortium and Department of Pharmacology & Toxicology, University of Toronto, Toronto, Canada
| | - Tohar Scheininger
- Healthy Brain Network, Child Mind Institute, New York, New York, United States of America
| | - Félix Schoeller
- Center for Research and Interdisciplinarity (CRI), Université de Paris, INSERM U1284, Paris, France
| | - Anibal Sólon Heinsfeld
- Center for the Developing Brain, Child Mind Institute, New York, New York, United States of America
| | - François Taddei
- Center for Research and Interdisciplinarity (CRI), Université de Paris, INSERM U1284, Paris, France
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33
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Late E, Kekäläinen J. Use and users of a social science research data archive. PLoS One 2020; 15:e0233455. [PMID: 32760066 PMCID: PMC7410240 DOI: 10.1371/journal.pone.0233455] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 05/05/2020] [Indexed: 12/04/2022] Open
Abstract
This study focuses on the use and users of Finnish social science research data archive. Study is based on enriched user data of the archive from years 2015–2018. Study investigates the number and type of downloaded datasets, the number of citations for data, the demographics of data downloaders and the purposes data are downloaded for. Datasets were downloaded from the archive 10346 times. Majority of the downloaded datasets are quantitative. Quantitative datasets are also more often cited, but the number of citations vary and does not always correlate with the number of downloads. Use of the archive varies by user’s country, organization, and discipline. Datasets from the archive were downloaded most often for study work, bachelor’s and master’s theses, and research purposes. It is likely that reusing research data will increase in the near future as more data will become available, scholars are more informed about research data management, and data citation practices are established.
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Affiliation(s)
- Elina Late
- Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland
- * E-mail:
| | - Jaana Kekäläinen
- Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland
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Gregory K. A dataset describing data discovery and reuse practices in research. Sci Data 2020; 7:232. [PMID: 32661229 PMCID: PMC7359296 DOI: 10.1038/s41597-020-0569-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 06/12/2020] [Indexed: 11/24/2022] Open
Abstract
This paper presents a dataset produced from the largest known survey examining how researchers and support professionals discover, make sense of and reuse secondary research data. 1677 respondents in 105 countries representing a variety of disciplinary domains, professional roles and stages in their academic careers completed the survey. The results represent the data needs, sources and strategies used to locate data, and the criteria employed in data evaluation of these respondents. The data detailed in this paper have the potential to be reused to inform the development of data discovery systems, data repositories, training activities and policies for a variety of general and specific user communities. Measurement(s) | data discovery behaviours • data reuse behaviours • data management | Technology Type(s) | Survey • Self-Report |
Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12445034
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Affiliation(s)
- Kathleen Gregory
- Data Archiving and Networked Services, Royal Netherlands Academy of Arts & Sciences, Anna van Saksenlaan 51, 2593 HW, Den Haag, The Netherlands.
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35
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Alter-Value in Data Reuse: Non-Designated Communities and Creative Processes. DATA SCIENCE JOURNAL 2020. [DOI: 10.5334/dsj-2020-023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
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36
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Gonzalez-Beltran AN, Masuzzo P, Ampe C, Bakker GJ, Besson S, Eibl RH, Friedl P, Gunzer M, Kittisopikul M, Dévédec SEL, Leo S, Moore J, Paran Y, Prilusky J, Rocca-Serra P, Roudot P, Schuster M, Sergeant G, Strömblad S, Swedlow JR, van Erp M, Van Troys M, Zaritsky A, Sansone SA, Martens L. Community standards for open cell migration data. Gigascience 2020; 9:giaa041. [PMID: 32396199 PMCID: PMC7317087 DOI: 10.1093/gigascience/giaa041] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Revised: 04/02/2020] [Accepted: 04/02/2020] [Indexed: 01/08/2023] Open
Abstract
Cell migration research has become a high-content field. However, the quantitative information encapsulated in these complex and high-dimensional datasets is not fully exploited owing to the diversity of experimental protocols and non-standardized output formats. In addition, typically the datasets are not open for reuse. Making the data open and Findable, Accessible, Interoperable, and Reusable (FAIR) will enable meta-analysis, data integration, and data mining. Standardized data formats and controlled vocabularies are essential for building a suitable infrastructure for that purpose but are not available in the cell migration domain. We here present standardization efforts by the Cell Migration Standardisation Organisation (CMSO), an open community-driven organization to facilitate the development of standards for cell migration data. This work will foster the development of improved algorithms and tools and enable secondary analysis of public datasets, ultimately unlocking new knowledge of the complex biological process of cell migration.
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Affiliation(s)
- Alejandra N Gonzalez-Beltran
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, Oxford OX1 3QG, Oxford, UK
| | - Paola Masuzzo
- VIB-UGent Center for Medical Biotechnology, VIB, A. Baertsoenkaai 3, B-9000, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, A. Baertsoenkaai 3, B-9000, Ghent, Belgium
- Institute for Globally Distributed Open Research and Education (IGDORE), Kabupaten Gianyar, Bali 80571, Indonesia
| | - Christophe Ampe
- Department of Biomolecular Medicine, Ghent University, A. Baertsoenkaai 3, B-9000, Ghent, Belgium
| | - Gert-Jan Bakker
- Department of Cell Biology, Radboud Institute for Molecular Life Sciences, Geert Grooteplein 28 6525 GA Nijmegen, The Netherlands
| | - Sébastien Besson
- Centre for Gene Regulation & Expression & Division of Computational Biology, University of Dundee, School of Life Sciences, Dow St Dundee DD1 5EH, Scotland, UK
| | - Robert H Eibl
- German Cancer Research Center, DKFZ Alumni Association, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Peter Friedl
- Department of Cell Biology, Radboud Institute for Molecular Life Sciences, Geert Grooteplein 28 6525 GA Nijmegen, The Netherlands
- David H. Koch Center for Applied Genitourinary Medicine, UT MD Anderson Cancer Center, 6767 Bertner Ave, Mitchell Basic Science Research Building, 77030 Houston, TX, USA
- Cancer Genomics Center, Universiteitsweg 100, 3584 CG Utrecht, The Netherlands
| | - Matthias Gunzer
- Institute for Experimental Immunology and Imaging, University Hospital, University Duisburg-Essen, Universitätsstr. 2, 45141 Essen, Germany
- Leibniz Institute for Analytical Sciences, ISAS, Bunsen-Kirchhoff-Straße 11, 44139 Dortmund, Germany
| | - Mark Kittisopikul
- Department of Biophysics, UT Southwestern Medical Center, 5323 Harry Hines Blvd. Dallas, TX 75390, USA
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, 303 E. Chicago Ave, Chicago, IL 60611, USA
| | - Sylvia E Le Dévédec
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, PO box 9502 2300 RA Leiden, The Netherlands
| | - Simone Leo
- Centre for Gene Regulation & Expression & Division of Computational Biology, University of Dundee, School of Life Sciences, Dow St Dundee DD1 5EH, Scotland, UK
- Center for Advanced Studies, Research, and Development in Sardinia (CRS4), Loc. Piscina Manna, Edificio 1, 09050 Pula (CA) , Italy
| | - Josh Moore
- Centre for Gene Regulation & Expression & Division of Computational Biology, University of Dundee, School of Life Sciences, Dow St Dundee DD1 5EH, Scotland, UK
| | - Yael Paran
- IDEA Bio-Medical Ltd, 2 Prof. Bergman St., Rehovot 76705, Israel
| | - Jaime Prilusky
- Life Science Core Facilities, Weizmann Institute of Science, P.O. Box 26 Rehovot 76100, Israel
| | - Philippe Rocca-Serra
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, Oxford OX1 3QG, Oxford, UK
| | - Philippe Roudot
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, 5323 Harry Hines Blvd. Dallas, TX 75390, USA
| | - Marc Schuster
- Institute for Experimental Immunology and Imaging, University Hospital, University Duisburg-Essen, Universitätsstr. 2, 45141 Essen, Germany
| | - Gwendolien Sergeant
- Department of Biomolecular Medicine, Ghent University, A. Baertsoenkaai 3, B-9000, Ghent, Belgium
| | - Staffan Strömblad
- Department of Biosciences and Nutrition, Karolinska Institutet, Neo, SE-141 83 Huddinge, Sweden
| | - Jason R Swedlow
- Centre for Gene Regulation & Expression & Division of Computational Biology, University of Dundee, School of Life Sciences, Dow St Dundee DD1 5EH, Scotland, UK
| | - Merijn van Erp
- Department of Cell Biology, Radboud Institute for Molecular Life Sciences, Geert Grooteplein 28 6525 GA Nijmegen, The Netherlands
| | - Marleen Van Troys
- Department of Biomolecular Medicine, Ghent University, A. Baertsoenkaai 3, B-9000, Ghent, Belgium
| | - Assaf Zaritsky
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, P.O.B. 653, 8410501 Beer-Sheva, Israel
| | - Susanna-Assunta Sansone
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, Oxford OX1 3QG, Oxford, UK
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, A. Baertsoenkaai 3, B-9000, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, A. Baertsoenkaai 3, B-9000, Ghent, Belgium
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Vollmer S, Mateen BA, Bohner G, Király FJ, Ghani R, Jonsson P, Cumbers S, Jonas A, McAllister KSL, Myles P, Granger D, Birse M, Branson R, Moons KGM, Collins GS, Ioannidis JPA, Holmes C, Hemingway H. Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness. BMJ 2020; 368:l6927. [PMID: 32198138 DOI: 10.1136/bmj.l6927] [Citation(s) in RCA: 155] [Impact Index Per Article: 38.8] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Sebastian Vollmer
- Alan Turing Institute, Kings Cross, London, UK
- Departments of Mathematics and Statistics, University of Warwick, Coventry, UK
| | - Bilal A Mateen
- Alan Turing Institute, Kings Cross, London, UK
- Warwick Medical School, University of Warwick, Coventry, UK
- Kings College Hospital, Denmark Hill, London, UK
| | - Gergo Bohner
- Alan Turing Institute, Kings Cross, London, UK
- Departments of Mathematics and Statistics, University of Warwick, Coventry, UK
| | - Franz J Király
- Alan Turing Institute, Kings Cross, London, UK
- Department of Statistical Science, University College London, London, UK
| | | | - Pall Jonsson
- Science Policy and Research, National Institute for Health and Care Excellence, Manchester, UK
| | - Sarah Cumbers
- Health and Social Care Directorate, National Institute for Health and Care Excellence, London, UK
| | - Adrian Jonas
- Data and Analytics Group, National Institute for Health and Care Excellence, London, UK
| | | | - Puja Myles
- Clinical Practice Research Datalink, Medicines and Healthcare products Regulatory Agency, London, UK
| | - David Granger
- Medicines and Healthcare products Regulatory Agency, London, UK
| | - Mark Birse
- Medicines and Healthcare products Regulatory Agency, London, UK
| | - Richard Branson
- Medicines and Healthcare products Regulatory Agency, London, UK
| | - Karel G M Moons
- Julius Centre for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, Netherlands
| | - Gary S Collins
- UK EQUATOR Centre, Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK
| | - John P A Ioannidis
- Meta-Research Innovation Centre at Stanford, Stanford University, Stanford, CA, USA
| | - Chris Holmes
- Alan Turing Institute, Kings Cross, London, UK
- Department of Statistics, University of Oxford, Oxford OX1 3LB, UK
| | - Harry Hemingway
- Health Data Research UK London, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
- National Institute for Health Research, University College London Hospitals Biomedical Research Centre, University College London, London, UK
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38
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Darch PT, Sands AE, Borgman CL, Golshan MS. Library cultures of data curation: Adventures in astronomy. J Assoc Inf Sci Technol 2020. [DOI: 10.1002/asi.24345] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Peter T. Darch
- School of Information Sciences University of Illinois at Urbana‐Champaign Urbana Illinois
| | - Ashley E. Sands
- Department of Information Studies University of California Los Angeles Los Angeles California
| | - Christine L. Borgman
- Department of Information Studies University of California Los Angeles Los Angeles California
| | - Milena S. Golshan
- Department of Information Studies University of California Los Angeles Los Angeles California
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Schaduangrat N, Lampa S, Simeon S, Gleeson MP, Spjuth O, Nantasenamat C. Towards reproducible computational drug discovery. J Cheminform 2020; 12:9. [PMID: 33430992 PMCID: PMC6988305 DOI: 10.1186/s13321-020-0408-x] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 01/02/2020] [Indexed: 12/11/2022] Open
Abstract
The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.
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Affiliation(s)
- Nalini Schaduangrat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, 10700, Bangkok, Thailand
| | - Samuel Lampa
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden
| | - Saw Simeon
- Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart University, 10900, Bangkok, Thailand
| | - Matthew Paul Gleeson
- Department of Biomedical Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, 10520, Bangkok, Thailand.
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden.
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, 10700, Bangkok, Thailand.
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40
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Groth P, Cousijn H, Clark T, Goble C. FAIR Data Reuse – the Path through Data Citation. DATA INTELLIGENCE 2020. [DOI: 10.1162/dint_a_00030] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
One of the key goals of the FAIR guiding principles is defined by its final principle – to optimize data sets for reuse by both humans and machines. To do so, data providers need to implement and support consistent machine readable metadata to describe their data sets. This can seem like a daunting task for data providers, whether it is determining what level of detail should be provided in the provenance metadata or figuring out what common shared vocabularies should be used. Additionally, for existing data sets it is often unclear what steps should be taken to enable maximal, appropriate reuse. Data citation already plays an important role in making data findable and accessible, providing persistent and unique identifiers plus metadata on over 16 million data sets. In this paper, we discuss how data citation and its underlying infrastructures, in particular associated metadata, provide an important pathway for enabling FAIR data reuse.
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Affiliation(s)
- Paul Groth
- Informatics Institute, University of Amsterdam, Amsterdam 1090 GH, The Netherlands
| | | | - Tim Clark
- Data Science Institute, University of Virginia, Charlottesville, VA 22903-1738, USA
| | - Carole Goble
- Department of Computer Science, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
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41
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Wist J. HastaLaVista, a web-based user interface for NMR-based untargeted metabolic profiling analysis in biomedical sciences: towards a new publication standard. J Cheminform 2019; 11:75. [PMID: 33430999 PMCID: PMC6896291 DOI: 10.1186/s13321-019-0399-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 11/27/2019] [Indexed: 02/19/2023] Open
Abstract
Metabolic profiling has been shown to be useful to improve our understanding of complex metabolic processes. Shared data are key to the analysis and validation of metabolic profiling and untargeted spectral analysis and may increase the pace of new discovery. Improving the existing portfolio of open software may increase the fraction of shared data by decreasing the amount of effort required to publish them in a manner that is useful to others. However, a weakness of open software, when compared to commercial ones, is the lack of user-friendly graphical interface that may discourage inexperienced researchers. Here, a web-browser-oriented solution is presented and demonstrated for metabolic profiling analysis that combines the power of R for back-end statistical analyses and of JavaScript for front-end visualisations and user interactivity. This unique combination of statistical programming and web-browser visualisation brings enhanced data interoperability and interactivity into the open source realm. It is exemplified by characterizing the extent to which bariatric surgery perturbs the metabolisms of rats, showing the value of the approach in iterative analysis by the end-user to establish a deeper understanding of the system perturbation. HastaLaVista is available at: (https://github.com/jwist/hastaLaVista, 10.5281/zenodo.3544800) under MIT license. The approach described in this manuscript can be extended to connect the interface to other scripting languages such as Python, and to create interfaces for other types of data analysis.
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Affiliation(s)
- Julien Wist
- Chemistry Department, Universidad del Valle, Cali, 76001, Valle del Cauca, Colombia.
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42
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Cordell GA. Cyberecoethnopharmacolomics. JOURNAL OF ETHNOPHARMACOLOGY 2019; 244:112134. [PMID: 31377262 DOI: 10.1016/j.jep.2019.112134] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Revised: 07/24/2019] [Accepted: 07/31/2019] [Indexed: 06/10/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Development of a new term which describes the contemporary, composite, constituent sciences of ethnopharmacology. AIM OF THE STUDY To discuss the polysyllabic term cyberecoethnopharmacolomics in the context of the future of ethnopharmacology in global health care. MATERIALS AND METHODS Literature background and assessment from the prior literature, diverse databases, and personal discussions. RESULTS The profiles and literature background with contemporary and future thoughts regarding the concepts and practices of cyber-, eco-, ethno-, pharmacol-, and -omics, and their impact in ethnopharmacology for the future are presented in the context of integrated health care systems. CONCLUSIONS Ethnopharmacology has a major role to play in global health care if the relevant sciences and cutting-edge technologies can coalesce synergistically as a responsive, evidence-based health care practice.
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Affiliation(s)
- Geoffrey A Cordell
- Natural Products Inc., Evanston, IL, USA; Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, FL, 32610, USA.
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44
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Borgman CL, Scharnhorst A, Golshan MS. Digital data archives as knowledge infrastructures: Mediating data sharing and reuse. J Assoc Inf Sci Technol 2019. [DOI: 10.1002/asi.24172] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Christine L. Borgman
- Center for Knowledge Infrastructures, Department of Information StudiesUniversity of California Los Angeles CA
| | | | - Milena S. Golshan
- Center for Knowledge Infrastructures, Department of Information StudiesUniversity of California Los Angeles CA
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Zaritsky A. Sharing and reusing cell image data. Mol Biol Cell 2018; 29:1274-1280. [PMID: 29851565 PMCID: PMC5994892 DOI: 10.1091/mbc.e17-10-0606] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Revised: 04/02/2018] [Accepted: 04/06/2018] [Indexed: 01/19/2023] Open
Abstract
The rapid growth in content and complexity of cell image data creates an opportunity for synergy between experimental and computational scientists. Sharing microscopy data enables computational scientists to develop algorithms and tools for data analysis, integration, and mining. These tools can be applied by experimentalists to promote hypothesis-generation and discovery. We are now at the dawn of this revolution: infrastructure is being developed for data standardization, deposition, sharing, and analysis; some journals and funding agencies mandate data deposition; data journals publish high-content microscopy data sets; quantification becomes standard in scientific publications; new analytic tools are being developed and dispatched to the community; and huge data sets are being generated by individual labs and philanthropic initiatives. In this Perspective, I reflect on sharing and reusing cell image data and the opportunities that will come along with it.
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Curty RG, Crowston K, Specht A, Grant BW, Dalton ED. Attitudes and norms affecting scientists' data reuse. PLoS One 2017; 12:e0189288. [PMID: 29281658 PMCID: PMC5744933 DOI: 10.1371/journal.pone.0189288] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2016] [Accepted: 11/23/2017] [Indexed: 11/28/2022] Open
Abstract
The value of sharing scientific research data is widely appreciated, but factors that hinder or prompt the reuse of data remain poorly understood. Using the Theory of Reasoned Action, we test the relationship between the beliefs and attitudes of scientists towards data reuse, and their self-reported data reuse behaviour. To do so, we used existing responses to selected questions from a worldwide survey of scientists developed and administered by the DataONE Usability and Assessment Working Group (thus practicing data reuse ourselves). Results show that the perceived efficacy and efficiency of data reuse are strong predictors of reuse behaviour, and that the perceived importance of data reuse corresponds to greater reuse. Expressed lack of trust in existing data and perceived norms against data reuse were not found to be major impediments for reuse contrary to our expectations. We found that reported use of models and remotely-sensed data was associated with greater reuse. The results suggest that data reuse would be encouraged and normalized by demonstration of its value. We offer some theoretical and practical suggestions that could help to legitimize investment and policies in favor of data sharing.
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Affiliation(s)
- Renata Gonçalves Curty
- Departamento de Ciência da Informação, Universidade Estadual de Londrina, Londrina, PR, Brazil
| | - Kevin Crowston
- School of Information Studies, Syracuse University, Syracuse, NY, United States of America
| | - Alison Specht
- Centre for the Synthesis and Analysis of Biodiversity, Foundation for the Research on Biodiversity, Aix-en-Provence, France.,School for Earth and Environmental Sciences, the University of Queensland, Lucia, Queensland, Australia
| | - Bruce W Grant
- Departments of Biology and Environmental Science, Widener University, Chester, PA, United States of America
| | - Elizabeth D Dalton
- Department of Communication Studies, Middle Tennessee State University; Murfreesboro, TN, United States of America
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Abstract
Building on achievements and experience gained through the EU project DAMOCLES and international data management during the International Polar Year, ACCESS, data management was implemented using the same platform as used for DAMOCLES. A metadata-driven approach through which all datasets are properly described with discovery and use metadata was chosen in order to simplify data management and data usage. The system provides automated submission and checking of datasets, search and download as well as visualisation and transformation on user demand and metadata export. Long-term management of ACCESS climate datasets is done within the context of the Arctic Data Centre. This ensures visibility of ACCESS datasets in the context of WMO and GEOSS catalogues. Challenges with ACCESS data management have mainly been cultural with the consequence that the system has been underutilised within the duration of the project duration.
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Affiliation(s)
- Øystein Godøy
- Norwegian Meteorological Institute, P.O. Box 43, Blinderen, 0313 Oslo, Norway
| | - Bard Saadatnejad
- Norwegian Meteorological Institute, P.O. Box 43, Blinderen, 0313 Oslo, Norway
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Neff G, Tanweer A, Fiore-Gartland B, Osburn L. Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies and Data Science. BIG DATA 2017; 5:85-97. [PMID: 28632445 PMCID: PMC5515123 DOI: 10.1089/big.2016.0050] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
What would data science look like if its key critics were engaged to help improve it, and how might critiques of data science improve with an approach that considers the day-to-day practices of data science? This article argues for scholars to bridge the conversations that seek to critique data science and those that seek to advance data science practice to identify and create the social and organizational arrangements necessary for a more ethical data science. We summarize four critiques that are commonly made in critical data studies: data are inherently interpretive, data are inextricable from context, data are mediated through the sociomaterial arrangements that produce them, and data serve as a medium for the negotiation and communication of values. We present qualitative research with academic data scientists, "data for good" projects, and specialized cross-disciplinary engineering teams to show evidence of these critiques in the day-to-day experience of data scientists as they acknowledge and grapple with the complexities of their work. Using ethnographic vignettes from two large multiresearcher field sites, we develop a set of concepts for analyzing and advancing the practice of data science and improving critical data studies, including (1) communication is central to the data science endeavor; (2) making sense of data is a collective process; (3) data are starting, not end points, and (4) data are sets of stories. We conclude with two calls to action for researchers and practitioners in data science and critical data studies alike. First, creating opportunities for bringing social scientific and humanistic expertise into data science practice simultaneously will advance both data science and critical data studies. Second, practitioners should leverage the insights from critical data studies to build new kinds of organizational arrangements, which we argue will help advance a more ethical data science. Engaging the insights of critical data studies will improve data science. Careful attention to the practices of data science will improve scholarly critiques. Genuine collaborative conversations between these different communities will help push for more ethical, and better, ways of knowing in increasingly datum-saturated societies.
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Affiliation(s)
- Gina Neff
- Oxford Internet Institute, University of Oxford, Oxford, United Kingdom
| | - Anissa Tanweer
- Department of Communication, University of Washington, Seattle, Washington
| | | | - Laura Osburn
- Department of Construction Management, University of Washington, Seattle, Washington
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