Discovering Ecologically Relevant Knowledge from Published Studies through Geosemantic Searching

OPTIMETA – Strengthening the Open Access publishing system through open citations and spatiotemporal metadata

The BMBF project OPTIMETA aims to strengthen the Open Access publishing system by connecting open citations and spatiotemporal metadata from open access journals with openly accessible data sources. For this purpose, we will extend Open Journal Systems (OJS) to give it functionalities for collecting and distributing open data by developing two OJS plugins for capturing citation networks and articles' spatial and temporal properties as machine-readable and accessible metadata. We will ensure the target group-orientated design of the plugins by performing a comprehensive needs analysis for key stakeholders: the editors or operators of OA journals and the researchers, as authors and readers of articles. The developments will be designed and tested in cooperation with several independent journals and OA publishers. Overall, OPTIMETA supports the attraction of independent OA journals as publication venues by substantially improving the discoverability and visibility of OA publications through enrichment and interlinking of article metadata.

Extracting and modeling geographic information from scientific articles

Scientific articles often contain relevant geographic information such as where field work was performed or where patients were treated. Most often, this information appears in the full-text article contents as a description in natural language including place names, with no accompanying machine-readable geographic metadata. Automatically extracting this geographic information could help conduct meta-analyses, find geographical research gaps, and retrieve articles using spatial search criteria. Research on this problem is still in its infancy, with many works manually processing corpora for locations and few cross-domain studies. In this paper, we develop a fully automatic pipeline to extract and represent relevant locations from scientific articles, applying it to two varied corpora. We obtain good performance, with full pipeline precision of 0.84 for an environmental corpus, and 0.78 for a biomedical corpus. Our results can be visualized as simple global maps, allowing human annotators to both explore corpus patterns in space and triage results for downstream analysis. Future work should not only focus on improving individual pipeline components, but also be informed by user needs derived from the potential spatial analysis and exploration of such corpora.

Known unknowns: Filling the gaps in scientific knowledge production in the Caatinga

The Caatinga is an ecologically unique semi-arid region of northeast Brazil characterized by high levels of endemism and severe anthropogenic threats from agricultural development and climate change. It is also one of the least known biomes in Brazil due to a combination of inadequate investment, low regional research capacity and difficult working conditions. However, while the lack of scientific knowledge of the Caatinga is well known, the spatial and temporal distribution of knowledge production has not been investigated. This is important because such biases undermine the development of effective conservation policy and practice and increase the uncertainty associated with conservation actions. Here, we map the geography of conservation knowledge production in the Caatinga and use an innovative hurdle model to identify the presumptive factors driving these patterns. Our analysis revealed strong geographic patterns, with research sites concentrated in the east of the region and in areas close to roads and research centres. There was also a positive association between conservation knowledge production and risk of desertification, indicating that conservation scientists are responding to conservation challenges faced by Caatinga’s fauna and flora arising from climate change. Our results also highlight the pivotal role of pioneer scientists (those who develop research sites in previously unstudied/understudied areas) in determining the future geographic patterns of knowledge production. We conclude our article with a brief discussion of potential policies for increasing the spatial representativeness of conservation research in this remarkable ecosystem.

Mining location information from life- and earth-sciences studies to facilitate knowledge discovery

Location information in published studies represents an untapped resource for literature discovery, applicable to a range of domains. The ability to easily discover scientific articles from specific places, nearby locales, or similar (but geographically separate) areas worldwide is important for advancing science and addressing global sustainability challenges. However, the thematic and not geographic nature of current search tools makes location-based searches challenging and inefficient. Manually geolocating studies is labor intensive, and place-name recognition algorithms have performed poorly due to prevalence of irrelevant place names in scientific articles. These challenges have hindered past efforts to create map-based literature search tools. Thus, automated approaches are needed to sustain article georeferencing efforts. Common pattern-matching algorithms (parsers) can be used to identify and extract geographic coordinates from the text of published articles. Pattern-matching algorithms (geoparsers) were developed using regular expressions and lexical parsing and tested their performance against sets of full-text articles from multiple journals that were manually scanned for coordinates. Both geoparsers performed well at recognizing and extracting coordinates from articles with accuracy ranging from 85.1% to 100%, and the lexical geoparser performing marginally better. Omission errors (i.e. missed coordinates) were 0% to 14.9% for the regular expression geoparser and 0% to 10.3% for the lexical geoparser. Only a single commission error (i.e. erroneous coordinate) was encountered with the lexical geoparser. The ability to automatically identify and extract location information from published studies opens new possibilities for transforming scientific literature discovery and supporting novel research.

Advances and trends in bibliographic research: Examples of new technological applications for the cataloguing of the georeferenced library heritage

In the age of digital archives and online data consultation, bibliographic research is considered as a key tool for supporting scientific research and study. The online catalogue allows the achievement of more ambitious aims and global interest thanks to its ability to associate data relating to the geographic contextualization of the catalogued editorial products (deduced from the title and content) with the search for more traditional bibliographic data through the inclusion of a specific and standardized ‘field’. Successively, the locations identified by the cataloguer are georeferenced by using GIS applications, which allows the simultaneous view of the distribution of global and local geographical contexts specific for each item owned by a library, archive or museum. The usefulness of such an application lies in the possibility for the library to have a greater awareness of its collection, thus permitting the acquisition of an additional element of evaluation in the management and planning of purchases and donations. In this way, the ability to filter the information from OPAC search will be combined with the basic research carried out by the user by selecting only the libraries in possession of works related to a specific geographical context, involved in different specific studies (literature, landscape, environment). Although this ability is still limited to a few specific studies, the use of tools that allow an overview of the geographical distribution of places could represent an operating standard through the definition of a special protocol. These tools are now used mostly in experimental studies in which the use of open source software has enabled the creation of maps. This paper shows the state of the art of the applications worldwide presenting experimental case studies (i.e. Coos Bay, Oregon; Basilicata, Italy) and also suggests different applications in the field of national and international protocols of library cataloguing.

Synthesis in land change science: methodological patterns, challenges, and guidelines

Global and regional economic and environmental changes are increasingly influencing local land-use, livelihoods, and ecosystems. At the same time, cumulative local land changes are driving global and regional changes in biodiversity and the environment. To understand the causes and consequences of these changes, land change science (LCS) draws on a wide array synthetic and meta-study techniques to generate global and regional knowledge from local case studies of land change. Here, we review the characteristics and applications of synthesis methods in LCS and assess the current state of synthetic research based on a meta-analysis of synthesis studies from 1995 to 2012. Publication of synthesis research is accelerating, with a clear trend toward increasingly sophisticated and quantitative methods, including meta-analysis. Detailed trends in synthesis objectives, methods, and land change phenomena and world regions most commonly studied are presented. Significant challenges to successful synthesis research in LCS are also identified, including issues of interpretability and comparability across case-studies and the limits of and biases in the geographic coverage of case studies. Nevertheless, synthesis methods based on local case studies will remain essential for generating systematic global and regional understanding of local land change for the foreseeable future, and multiple opportunities exist to accelerate and enhance the reliability of synthetic LCS research in the future. Demand for global and regional knowledge generation will continue to grow to support adaptation and mitigation policies consistent with both the local realities and regional and global environmental and economic contexts of land change.