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Research Data Management and data protection in the Social Sciences [Workshop recording]
<p>This online workshop organized by The Austrian Social Science Data Archive (AUSSDA) focused on the Research Data Management basics, Data Management Plans and common data protection issues in the Social Sciences.</p> <p>The first part of the workshop was dedicated to RDM basics and Data Management Plans (DMPs). In many projects, DMPs are mandatory deliverables that need to be submitted at the beginning of a project and are updated throughout the project life cycle. During the workshop, it was explained which aspect funders expect to be part of DMPs in Social Sciences and how researchers can benefit from (writing) these documents.</p> <p>In the second part of the workshop, data protection issues that are common in Social Sciences were addressed and how they can be handled. In particular, differences in the curation of quantitative and qualitative data need in order to comply with data protection regulations in general and AUSSDA deposit guidelines in particular. Presentation on how AUSSDA scans quantitative data for potential data protection violations using STATA and gives participants the opportunity to test the code on their own data and devices.</p> <p>The video is available on<a href="https://www.youtube.com/watch?v=DhiL9J-Iwqg"> the CESSDA Training YouTube channel</a>.</p> <p> </p>
Different facets of the same niche: integrating citizen science and scientific survey data to predict biological invasion risk under multiple global change drivers
<p>Raw data (occurrences and environmental predictors) used in the manuscript "Different facets of the same niche: integrating citizen science and scientific survey data to predict biological invasion risk under multiple global change drivers"</p>
Data and figures for "Atlas of Science Collaboration, 1971–2020"
<p><strong>Abstract</strong></p><p>The evolving landscape of interinstitutional collaborative research across 15 natural science disciplines is explored using the open data sourced from OpenAlex. This extensive exploration spans the years from 1971 to 2020, facilitating a thorough investigation of leading scientific output producers and their collaborative relationships based on coauthorships. The findings are visually presented on world maps and other diagrams, offering a clear and insightful portrayal of notable variations in both national and international collaboration patterns across various fields and time periods. These visual representations serve as valuable resources for science policymakers, diplomats and institutional researchers, providing them with a comprehensive overview of global collaboration and aiding their intuitive grasp of the evolving nature of these partnerships over time.</p><p> </p><p><strong>Intended Readership</strong></p><ul><li>The booklet, entitled<i> '</i><a href="https://arxiv.org/abs/2308.16810"><i>Atlas of Science Collaboration</i></a><i>'</i>, aims to offer a broad overview of international and interinstitutional research collaboration, shedding light on its present status and evolution on a global scale. While it might not delve into intricate scholarly or academic data analysis, it remains a valuable resource for those seeking a general understanding of the collaborative relationships that have been established between research institutions in the world of science.</li><li>The intended readership including science and technology (S&T) policymakers and diplomats, government research and development (R&D) agencies, international organisations, S&T think tanks, as well as institutional research divisions of universities or R&D institutions.</li></ul><p> </p><p><strong>Data Source</strong></p><ul><li>The<i> </i><a href="https://arxiv.org/abs/2308.16810"><i>Atlas of Science Collaboration</i></a><i> </i>is based on data retrieved from <a href="https://docs.openalex.org/">OpenAlex</a>, a free and open (the CC0 license) catalogue of the world's scholarly papers, researchers, journals and institutions. Launched in January 2022, OpenAlex replaced <a href="https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/">Microsoft Academic Graph (MAG)</a>, which retired at the beginning of 2022.</li><li>OpenAlex collects information on scientific publications, including journal articles, non-journal articles, preprints, conference papers, books and datasets—hereafter collectively referred to as 'works'—from various platforms such as <a href="https://www.crossref.org/">Crossref</a>, <a href="https://orcid.org/">ORCID</a>, <a href="https://ror.org/">ROR</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/">PubMed</a>, preprint servers like <a href="https://arxiv.org/">arXiv</a>, and institutional or disciplinary repositories like <a href="https://zenodo.org/">Zenodo</a>. For comparison with other scholarly data sources such as <a href="https://www.scopus.com/">Scopus</a>, <a href="https://clarivate.com/products/scientific-and-academic-research/research-discovery-and-workflow-solutions/webofscience-platform/">Web of Science</a> and <a href="https://www.dimensions.ai/">Dimensions</a>, please refer to <a href="https://openalex.org/about#comparison">OpenAlex's website</a>.</li><li>OpenAlex offers extensive coverage of meta-information across a diverse spectrum of works, encompassing not only journal publications but also non-journal works, non-English works and contributions from the Global South. This attribute proves beneficial by providing a more precise augmentation of the extent of R&D activities, along with their associated scholarly outputs. This is especially crucial in fields where journals are not the predominant channel for disseminating research outcomes. Furthermore, OpenAlex effectively captures outputs in the preprint format, which might persist for varying durations, spanning from months to years or even indefinitely, without necessarily transitioning into journal publications.</li><li>The present edition (August 2023) of the<i> </i><a href="https://arxiv.org/abs/2308.16810"><i>Atlas of Science Collaboration</i></a><i> </i>was compiled using data obtained via the <a href="https://docs.openalex.org/how-to-use-the-api/api-overview">OpenAlex API</a> during the period from the 12th to the 15th of August 2023. It is essential to note that OpenAlex is an ongoing project, continuously updating its data and improving its system. Consequently, the visualisations in this booklet may not provide the most comprehensive view or accurate data. Expect more accurate results when acquiring data in the future as OpenAlex undergoes further upgrades. Revised editions of the<i> Atlas of Science Collaboration </i>may be made available on <a href="https://zenodo.org/">Zenodo</a> or other open platforms beyond this release.</li></ul><p> </p><p><strong>R&D Disciplines</strong></p><ul><li>In this current edition, the primary focus centres around the level-1 'concepts' listed in the following table sourced from the OpenAlex classification, as previously explored in <a href="https://doi.org/10.48550/arXiv.2211.04429">Okamura (2023)</a>. Each level-1 concept is accompanied by 'related concepts', which can offer a finer or broader delineation compared to the level-1 concept. Using this characteristic, an enhanced notion of R&D discipline is constructed by including all associated subconcepts of level 2 or higher for each of the 15 level-1 concepts. For instance, our defined discipline of 'Artificial Intelligence' includes OpenAlex's level-2 concepts of '<a href="https://explore.openalex.org/concepts/C50644808">Artificial Neural Network</a>' and '<a href="https://explore.openalex.org/concepts/C108583219">Deep Learning</a>', but not the level-0 concepts of '<a href="https://explore.openalex.org/concepts/C41008148">Computer Science</a>' or '<a href="https://explore.openalex.org/concepts/C33923547">Mathematics</a>'.</li></ul><p> </p><p> OpenAlex Concept / Identifier / Discipline Code </p><ol><li>Artificial intelligence / <a href="https://explore.openalex.org/concepts/C154945302">C154945302</a> / "ai"</li><li>Quantum mechanics / <a href="https://explore.openalex.org/concepts/C62520636">C62520636</a> / "quantum"</li><li>Biotechnology / <a href="https://explore.openalex.org/concepts/C150903083">C150903083</a> / "bio"</li><li>Nanotechnology / <a href="https://explore.openalex.org/concepts/C171250308">C171250308</a> / "nano"</li><li>Agricultural engineering / <a href="https://explore.openalex.org/concepts/C88463610">C88463610</a> / "agri"</li><li>Particle physics / <a href="https://explore.openalex.org/concepts/C109214941">C109214941</a> / "particle"</li><li>Aerospace engineering / <a href="https://explore.openalex.org/concepts/C146978453">C146978453</a> / "aerospace"</li><li>Nuclear engineering / <a href="https://explore.openalex.org/concepts/C116915560">C116915560</a> / "nuclear"</li><li>Marine engineering / <a href="https://explore.openalex.org/concepts/c199104240">C199104240</a> / "marine"</li><li>Neuroscience / <a href="https://explore.openalex.org/concepts/c169760540">C169760540</a> / "neuro"</li><li>Condensed matter physics / <a href="https://explore.openalex.org/concepts/C26873012">C26873012</a> / "condensed"</li><li>Environmental engineering / <a href="https://explore.openalex.org/concepts/C87717796">C87717796</a> / "envi"</li><li>Earth science / <a href="https://explore.openalex.org/concepts/c1965285">C1965285</a> / "earth"</li><li>Astronomy / <a href="https://explore.openalex.org/concepts/c1276947">C1276947</a> / "astro"</li><li>Pure mathematics / <a href="https://explore.openalex.org/concepts/C202444582">C202444582</a> / "math"</li></ol><p> </p><p><strong>Analysis and Visualisation</strong></p><ul><li>First, the<i> World Map of Science Collaboration</i> ('<strong>wmap_bilat</strong>' folder) divides the period from 1971 to 2020 into four intervals: 1971–1990, 1991–2000, 2001–2010 and 2011–2020. For each period and discipline, bubbles represent the top 199 research institutions in terms of work production. Additionally, for the top 50 research institutions, their locations are connected on the world map using great circle curves (the shortest route between them) to illustrate bilateral coauthorship relationships. Coauthorship relationships with fewer than five coauthored papers are not displayed. The background world map utilises the world data from the <a href="https://cran.r-project.org/package=maps">maps</a> package in R. The connection visualisation between two research institutions leverages the gcIntermediate() function from the <a href="https://cran.r-project.org/package=geosphere">geosphere</a> package in R. The sizes of the bubbles are proportional to the volume of work and can be compared across the different period panels.</li><li>Second, the<i> Top 30 Productive Institutions on the World Map </i>('<strong>wmap_topinst</strong>' folder) displays the leading 30 institutions in terms of work production on the World Map for each discipline and the three respective periods: 1991–2000, 2001–2010 and 2011–2020. The background world map employs the world data from the <a href="https://cran.r-project.org/package=maps">maps</a> package in R along with the <a href="https://cran.r-project.org/package=ggplot2">ggplot2</a> package16 in R. The sizes of the bubbles are proportional to the volume of work, standardised within each period panel, and cannot be compared across panels.</li><li>Third, the<i> Interregional Collaboration Matrix Diagram </i>('<strong>halfmat</strong>' folder) exhibits a half-matrix diagram at the country level for each discipline and the three respective periods: 1991–2000, 2001–2010 and 2011–2020. It counts the number of bilateral coauthorship relationships represented on the World Map. Each bubble's size (area) displayed in the matrix cell is proportional to the number of bilateral coauthorship relationships. This edition particularly focuses on five pivotal parties: the US, China, EU27, the UK and Japan. These parties were specifically selected due to their substantial contributions to work production across all scientific fields from 1971 to 2020. These choices also align with the nations acclaimed as the 'Big 5' science nations (the US, China, Germany, the UK and Japan) in the <a href="https://www.nature.com/articles/d41586-022-00569-7"><i>Nature Index</i></a>. Please note that the Matrix Diagram only takes into account the top 50 institutions in terms of work production for each period and discipline. Therefore, if a cell shows zero (as small dots), it does not necessarily imply the absence of coauthorship relationships for the corresponding bilateral pair.</li><li>Forth, the<i> Interinstitutional Collaboration Dendrogram </i>('<strong>cdend</strong>' folder) elucidates the development and evolution of interinstitutional research collaboration clusters spanning the last five decades. This is accomplished through hierarchical clustering analysis of institutions, considering the top 50 institutions in terms of work production across the four periods: 1971–1990, 1991–2000, 2001–2010 and 2011–2020.<ul><li>The method used for hierarchical clustering analysis is the same as developed in <a href="https://doi.org/10.48550/arXiv.2211.04429">Okamura (2023)</a>. The distance between institutions X and Y is defined as the number of works with nationalities from both X and Y divided by the total number of works with nationalities from at least one of X and Y, subtracted from 1. Hierarchical clustering analysis was performed on the distance matrix using the hclust function implemented in R with the ward.D2 option (i.e. the original Ward's method) specified.</li><li>The method of dendrogram visualisation is primarily derived from an example detailed on the <a href="https://cran.r-project.org/web/packages/dendextend/vignettes/dendextend.html">dendextend website</a>. Circular dendrograms were created using the <a href="https://cran.r-project.org/package=dendextend">dendextend</a> and <a href="https://cran.r-project.org/package=circlize">circlize</a> packages in R. As one moves inward from the outer edge of the circle towards its centre, institutions or clusters of institutions that are in closer proximity to each other merge earlier.</li><li>To indicate the country where the institutions are located, the country names are included at the beginning of the terms of research institutions, using the two-letter <a href="https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2">ISO3166-1alpha-2</a> code. The accompanied circularised bar graphs represent the number of works for the institutions involved. <a href="https://ror.org/">ROR</a>s are used as the canonical identifiers of the research institutions. Readers of this booklet in PDF format can click on the ROR-based URL ('https://ror.org/...') in the diagrams to view the corresponding ROR webpage from their browser.</li></ul></li><li>Additionally, for each discipline and the respective periods of 1971–1990, 1991–2000, 2001–2010 and 2011–2020, the top 100 institutions in terms of work production are displayed in tabular format ('<strong>table</strong>' folder), showing their respective country codes and production volumes. If multiple research institutions have equal production volumes during each period, they are organised alphabetically by country codes and then by organisation names. Even if distinct rankings are shown, they lack significance and are treated as ties.</li></ul><p> </p><p><strong>Important Notes</strong></p><ul><li>It is worth reiterating that the data from OpenAlex used to compile the <a href="https://arxiv.org/abs/2308.16810"><i>Atlas of Science Collaboration</i></a>, even when incorporating bibliometric data related to past works, lacks consistent finality. As of the data acquisition for this version (August 2023), OpenAlex encompassed information regarding approximately 240 million works, with an additional influx of about 50,000 new data entries related to works being added daily. Furthermore, for a substantial portion of these works, information regarding the corresponding institution to which the authors belong remains unknown. As a result, should the same analyses as those embedded within this booklet be replicated in the future, although the qualitative extent of change remains uncertain, it is undeniable that quantitatively distinct data will be acquired. Nonetheless, for individuals seeking an understanding of the global scope and evolution of international and interinstitutional collaborative research, the potential availability of this booklet or an enhanced, continuously updated evidence base holds inherent value.</li><li>Further, it is worth reiterating that the term 'works' encompasses a wide variety of scholarly publications. The analyses conducted in the compilation of this booklet do not take into consideration whether these works are peer-reviewed articles or not, nor do they encompass considerations of their prominence, impact or quality. It is emphasised that the primary intent behind the visualisations in this booklet is to quantitatively capture the momentum of scholarly knowledge production outputs from diverse research institutions, and to identify how productive institutions collaborate internationally and interinstitutionally. Caution must be exercised, with acknowledgment that relying solely on the quantity of scholarly output produced by institutions falls short in encompassing discussions about their research potential, contributions to academia, or their relative superiority or inferiority. Further, it is recommended to consider the limitations discussed in <a href="https://doi.org/10.48550/arXiv.2211.04429">Okamura (2023)</a> when using this booklet.</li></ul><p> </p><p><strong>Miscellaneous</strong></p><ul><li>It is important to note that some research institutions may encounter difficulties in accurately assessing the actual production volume at the institutional level within each analysis period due to challenges related to name disambiguation and the influence of historical organisational changes in bibliometric databases.</li><li>For the Interinstitutional Collaboration Dendrograms and the rankings of the top 100 productive institutions, entities like universities and R&D institutions are primarily identified using the nomenclature employed in OpenAlex. However, certain portions have been presented through abbreviations or acronyms, both for illustrative purposes and to effectively accommodate limited space. For instance, 'University of' is abbreviated as 'U.', 'Institution' and 'Institute' as 'Inst', 'National Laboratory' as 'NL', and 'Science' and 'Technology' as 'Sci' and 'Tech', correspondingly, among others. Should readers possess more fitting suggestions for abbreviations specific to particular organisations, or any other ideas aimed at enhancing the content of this booklet, we would greatly appreciate their input.</li></ul><p> </p>
Science, Technology & Society Eurobarometers 1993 - 2021: Trend Data Collection
<p>These files contain structured collections of Eurobarometer (EB) survey data from 1993 to 2021, focusing on European citizen’s views on science and technology (S&T). The primary aim of these data collections is to facilitate research on trends over time regarding people’s knowledge, perception and attitudes towards S&T. The European Union has collected extensive survey data from the general public over the past 50 years through its official polling instrument, the Eurobarometer. Its general goal is monitoring the state of public opinion on diverse subjects and issues throughout Europe, one of which is S&T. The data collection files, provided in the folder ‘EB_data_csv’, include data from seven different Eurobarometer surveys. These surveys were selected because their raw datasets were openly accessible through Open EU Datasets. To ensure sufficient data points for plotting specific trends over time, we included only survey questions that appeared in at least three different EB surveys. </p>
Data from Citizen science data reveal regional heterogeneity in phenological response to climate in the large milkweed bug, Oncopeltus fasciatus
These data include annotations for life stage, mating behavior, and plant part occupancy of large milkweed bug observations in North America as well as information about climate and environment.
Lake ice surveys, 1874-2022, Adirondack Long-Term Ecological Monitoring Program Project No. 8 by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative.
The objective of this dataset is to document ice-in and ice-out dates on several lakes on the State University of New York College of Environmental Science and Forestry's Huntington Wildlife Forest (HWF). Lakes include: Arbutus, Catlin, Deer, Military, Rich, Wolf and Lodo Pond; some records exist for Long Pond and other water bodies but they are not included here except in some comment fields.
Return on Investment Metrics for Data Repositories in Earth and Environmental Sciences
Despite a growing recognition of the importance of data to the economy and to science, investment in repositories to manage and disseminate that data in easily accessible and understandable ways is scarce. Keeping repository services active and up-to-date for a long time period is difficult due to this funding situation. As a result, repositories must continually provide proof of their value, their Return on Investment (ROI) to their sponsors; yet doing so has always been difficult, problematic and not always successful. In this work, an analysis of approaches for assessing the ROI of several scientific data repositories has identified various techniques that repositories use to report on the impact and value of their data products and services. A survey of selected repositories rated the set of metrics identified and rated each by its importance as well as the ease with which the metric could be measured. The discussion is broken down into considerations for calculating costs, perceived value of repositories and suggested metrics that would allow a repository to calculate an ROI. The authors, representatives of environmental data repositories, concluded that easily obtainable data use metrics, such as data downloads, etc., have limited value while more informative analyses would require additional resources.
Data for Research Assessment in the Transition to Open Science. 2019 EUA Open Science and Access Survey Results
<p>This database refers to the data collected by the European University Association (EUA) for its Open Science and Access Survey 2019, which gathered responses from universities and higher education institutions across Europe. The full report published by the association is available at <a href="https://eua.eu/resources/publications/888:research-assessment-in-the-transition-to-open-science.html">https://eua.eu/resources/publications/888:research-assessment-in-the-transition-to-open-science.html</a>.</p> <p>The data included in this database refers only to those universities and higher education institutions that accepted their data to be available in open access (n=174). All information that could lead to the identification of individual universities and higher education institutions was removed from the database (cf. cells highlighted in red). The following files are available:</p> <ul> <li>2019 EUA Open Science and Access Survey</li> <li>Database in the following formats: .xlsx (Microsoft Excel)</li> <li>Survey Codebook: includes information on all the variables and their coding.</li> </ul>
Data Science in Biomedicine - Web of Science datasets
<p>Datasets from the Web of Science search for the number of publications associated with the topics "Data Science", "Big Data" and "Cloud Computing" from 2004 to 2019 in 9 different countries.</p>
Hubei STEC Data through CORS stations for DOY 059 and 061 of the year 2018 which used in (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City, manuscript submitted to Earth and Space Science Journal AGU)
<p>Manuscript submitted to Earth and Space Science AGU entitled with <br> (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City)<br> by: Mohamed Freeshah, Xiaohong Zhang, Xiaodong Ren, Jun Chen, and Zhibo Zhao</p> <p>The STEC data inside two compressed folders named as stec059 and stec061, respectively.<br> The STEC file name has the CORS station name for the first forth letters and next three numbers epresent the Day of the year.<br> For example:<br> ES010590.18STEC<br> ES01 is the station name<br> 059 is the day of year (DOY), 2018</p>
Data Release: Aurorasaurus Science Products Inventory & Survey Results (2014-2019)
<p>Aurorasaurus is an eight-year-old project: the first and only citizen science initiative that tracks auroras around the world via reports on our website, mobile apps, and social media. (See Kosar et al., (2018). Aurorasaurus Real-Time Citizen Science Aurora Data (Version v1.0) [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.1255196">http://doi.org/10.5281/zenodo.1255196</a>.)</p> <p>At the American Geophysical Union Fall Meeting 2019, MacDonald and Brandt (2019) gave a talk entitled "Towards developing appropriate and diverse metrics for citizen science – a case study." In the presentation, they examined our 2015 user survey, used more recent metrics to assess Aurorasaurus' current status, and began to lay a framework for their next round of evaluation. </p> <p>In order to frame the process, MacDonald and Brandt (2019) utilized the Science Products and Data Practices inventories created by Wiggins et al., (2018). The authors constructed lists of items and practices that should be present in citizen science projects. While the Science Products and Data Practices inventories are excellent for quantitative analysis, MacDonald and Brandt (2019) wanted to include qualitative analysis of past evaluation as well. To that end, they informally and retrospectively mapped Aurorasaurus' 2015 survey questions to the BASIK framework.</p> <p>Aurorasaurus is publishing this dataset as a case study for how the Wiggins et al. framework can be applied and combined with other systems.</p>
Biological soil covers: data on lichen, bryophyte and algae coverage in soils gathered by SoilSkin citizen science program using eBryoSoil app for smartphones
<p>Biological soil covers (BSC) are small-sized topsoil communities composed mainly by lichens, bryophytes and algae that cover the terrestrial surface and play an essential role in maintaining the quality of the soil. However, little is known about their distribution, conservation, and ecosystem functions. The SoilSkin citizen science project aims to expand the scientific knowledge about the distribution of biological soil covers as an important step to evaluate the vulnerability of soil ecosystems of the Iberian Peninsula in the face of global change.</p> <p>The project has a dedicated free of charge app for smartphones (eBryoSoil, available at Google Play <a href="https://play.google.com/store/apps/details?id=com.omarfiz.ebryosoil&hl=ca&gl=US">https://play.google.com/store/apps/details?id=com.omarfiz.ebryosoil&hl=ca&gl=US</a>) that is designed to obtain information about the coverage of the BSC communities. To use this app, users must select a sampling location and capture the three soil pictures required to complete a transect. These photographs are taken at a 27 cm distance from the soil, in a straight line with 15 meters of distance between each picture. After the acquisition of each image, users can quantify the coverage percentage of biological soil covers and select the type of habitat where the transect took place. The transect is complete when all three pictures and their respective information are uploaded.</p> <p>The data presented here contains the records from SoilSkin participants, which mainly include a characterization of the cover patterns of biological soil covers, the type of habitat and the coordinates where each record was taken. The data set is composed by 279 unique records taken by 37 unique users from 28/11/2019 to 12/12/2020, across the Iberian Peninsula. These records specifically detail the percentage of cover occupied by three types of lichen growth forms (crustose, foliose and fruticose); liverworts; two types of moss growth forms (acrocarpous and pleurocarpous); algae; and soil. Moreover, each record also contains a description of the main type of habitat where the transect took place, that was selected from a list contained in the app with the following habitats:</p> <ul> <li>Dense forest - Habitat characterized by trees of more than 2 meters tall and canopy over 60%.</li> <li>Open forest – Habitat characterized by trees with more than 2 meters tall and a canopy below 60%.</li> <li>Shrubland – Habitat characterized by woody vegetation with less than 2 meters tall.</li> <li>Grassland – Habitat characterized by herbaceous plants.</li> <li>Agricultural land – Habitat characterized by temporary or woody crops.</li> <li>Coastal habitat – Habitat characterized by a landscape where land is in contact with the sea, creating a visibly different landscape from inner terrestrial one’s.</li> <li>Urban green spaces – Habitat characterized by a landscape in which man-made structures are present.</li> </ul> <p>The database was revised to correct any possible mistakes (e.g., miscalculation of total percentages; habitat missing in some registers; removal of invalid registers).</p> <p>The data file contains the following columns:</p> <ul> <li>Date: numerical variable indicating the “day”/”month”/”year” when the register was generated.</li> <li>User_ID: categorical variable with the identification number of the user who gathered the record.</li> <li>Transect: categorical variable with the identification of the number of the transect.</li> <li>Photo_number: numeric variable that takes values of 1, 2 or 3 and corresponds with the identification of the photographs within each transect.</li> <li>Photo_label: character string with the identification of the photograph from each record.</li> <li>Register_localization: categorical variable with the identification of the geographic area where the record was done.</li> <li>Latitude: integer, variable indicating the latitude of the sampling location in decimal degrees.</li> <li>Longitude: integer, variable indicating the longitude of the sampling location in decimal degrees.</li> <li>Accuracy: integer, variable indicating the accuracy of the coordinates given by the GPS.</li> <li>Habitat_type: categorical variable with the description of the main type of habitat of the sampling location.</li> <li>Lichen_Crustose: integer, variable indicating the percentage of crustose lichen cover quantified in the record.</li> <li>Lichen_Foliose: integer, variable indicating the percentage of foliose lichen cover quantified in the record.</li> <li>Lichen_Fruticose: integer, variable indicating the percentage of fruticose lichen cover quantified in the record.</li> <li>Total_lichen: integer, variable indicating the sum of all lichen coverage quantified in the record.</li> <li>Liverwort: integer, variable indicating the percentage of liverwort cover quantified in the record.</li> <li>Moss_Acrocarpous: integer, variable indicating the percentage of acrocarpous moss cover quantified in the record.</li> <li>Moss_Pleurocarpous: integer, variable indicating the percentage of pleurocarpous moss cover quantified in the record.</li> <li>Total_ moss: integer, variable indicating the sum of all moss coverage quantified in the record.</li> <li>Algae: integer, variable indicating the percentage of algae cover quantified in the record.</li> <li>Soil: integer, variable indicating the percentage of soil visible in the record.</li> </ul> <p> </p>
Austrian Science Fund (FWF) Publication Cost Data 2015
<p>Following the approach for the datasets in 2013 (http://dx.doi.org/10.6084/m9.figshare.988754) and 2014 (https://dx.doi.org/10.6084/m9.figshare.1378610.v14), the Austrian Science Fund (FWF) is making the publication costs spent in 2015 (esp. for Open Access) publically available.</p> <p>The dataset includes payments for publications of authors funded by the Austrian Science Fund (FWF) via following programmes:</p> <p>"Peer-Reviewed Publications": https://www.fwf.ac.at/en/research-funding/fwf-programmes/peer-reviewed-publications/</p> <p>"Stand-Alone Publications": https://www.fwf.ac.at/en/research-funding/fwf-programmes/stand-alone-publications/</p>
Austrian Science Fund (FWF) Publication Cost Data 2013
<p>Following the Wellcome Trust, the Austrian Science Fund (FWF) makes its publication costs spent in 2013 (esp. for Open Access) publically available.</p> <p>An aggregated version of this dataset is already published in FWF's annual report 2013 (p. 86), http://www.fwf.ac.at/de/public_relations/publikationen/jahresberichte/fwf-jahresbericht-2013.pdf</p> <p>The dataset includes payments for journal articles of authors funded by the Austrian Science Fund (FWF) via the program ‘Peer Reviewed Publications’, http://www.fwf.ac.at/en/projects/peer-reviewed_publications.html</p> <p>The dataset distinguishes between three publication cost categories:</p> <p>(1) Gold Open Access and (2) Hybrid Open Access are defined by FWF's Open Access Policy, see: http://www.fwf.ac.at/en/public_relations/oai/index.html</p> <p>(3) ‘Other publication costs’ are defined as additional costs to subscription prices charged by subscription journals (i.e. submission fees, colour charges, page charges, figure charges, table charges, supplemental charges).</p> <p>Based on this data and the data of Web of Science (WoS), we roughly estimate for 2013 a share for Gold and Hybrid OA of around 33% of all articles (incl. reviews) that result from projects supported by the FWF and which are listed in Web of Science.</p> <p>The analysis of this dataset together with the report “Developing an Effective Market for Open Access Article Processing Charges” (http://www.wellcome.ac.uk/About-us/Policy/Spotlight-issues/Open-access/Guides/WTP054773.htm) will lead to an adaption of the FWF’s Open Access policy within the next months. One model, for example, for a cost-neutral Hybrid Open Access is already in place: http://ioppublishing.org/newsDetails/Austria-open-acces</p>
Supplementary material 10: Institutional collection dashboard: specimens from the collection of the California Academy of Sciences (CAS) from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts showing only specimens from the collection of the California Academy of Sciences. This page shows data from species-rank treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 interactive dashboard charts.
Dataset - Understanding the software and data used in the social sciences
<p>This is a repository for a UKRI Economic and Social Research Council (ESRC) funded project to understand the software used to analyse social sciences data.</p><p>Any software produced has been made available under a BSD 2-Clause license and any data and other non-software derivative is made available under a CC-BY 4.0 International License. Note that the software that analysed the survey is provided for illustrative purposes - it will not work on the decoupled anonymised data set.</p><p>Exceptions to this are:</p><ul><li>Data from the UKRI ESRC is mostly made available under a <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> Licence.</li><li>Data from Gateway to Research is made available under an <a href="https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">Open Government Licence</a> (Version 3.0).</li></ul><h2>Contents</h2><ul><li>Survey data & analysis: esrc_data-survey-analysis-data.zip</li><li>Other data: esrc_data-other-data.zip</li><li>Transcripts: esrc_data-transcripts.zip</li><li>Data Management Plan: esrc_data-dmp.zip</li></ul><h3>Survey data & analysis</h3><p>The survey ran from 3rd February 2022 to 6th March 2023 during which 168 responses were received. Of these responses, three were removed because they were supplied by people from outside the UK without a clear indication of involvement with the UK or associated infrastructure. A fourth response was removed as both came from the same person which leaves us with 164 responses in the data.</p><p>The survey responses, Question (Q) Q1-Q16, have been decoupled from the demographic data, Q17-Q23. Questions Q24-Q28 are for follow-up and have been removed from the data. The institutions (Q17) and funding sources (Q18) have been provided in a separate file as this could be used to identify respondents. Q17, Q18 and Q19-Q23 have all been independently shuffled.</p><p>The data has been made available as Comma Separated Values (CSV) with the question number as the header of each column and the encoded responses in the column below. To see what the question and the responses correspond to you will have to consult the survey-results-key.csv which decodes the question and responses accordingly. </p><p><strong>A pdf copy of the survey questions is </strong><a href="https://github.com/softwaresaved/esrc-software-study/blob/main/Docs/esrc-survey.pdf"><strong>available</strong></a><strong> on GitHub.</strong></p><p>The survey data has been decoupled into:</p><ul><li>survey-results-key.csv - maps a question number and the responses to the actual question values.</li><li>q1-16-survey-results.csv- the non-demographic component of the survey responses (Q1-Q16).</li><li>q19-23-demographics.csv - the demographic part of the survey (Q19-Q21, Q23).</li><li>q17-institutions.csv - the institution/location of the respondent (Q17).</li><li>q18-funding.csv - funding sources within the last 5 years (Q18).</li></ul><p>Please note the code that has been used to do the analysis will not run with the decoupled survey data. </p><h3>Other data files included</h3><ul><li>CleanedLocations.csv - normalised version of the institutions that the survey respondents volunteered.</li><li>DTPs.csv - information on the UKRI Doctoral Training Partnerships (DTPs) scaped from the UKRI <a href="https://esrc.ukri.org/skills-and-careers/doctoral-training/doctoral-training-partnerships/doctoral-training-partnership-dtp-contacts/">DTP contacts</a> web page in October 2021.</li><li>projectsearch-1646403729132.csv.gz - data snapshot from the <a href="https://gtr.ukri.org/">UKRI Gateway to Research</a> released on the 24th February 2022 made available under an <a href="https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">Open Government Licence</a>.</li><li>locations.csv - latitude and longitude for the institutions in the cleaned locations.</li><li>subjects.csv - research classifications for the ESRC projects for the 24th February data snapshot.</li><li>topics.csv - topic classification for the ESRC projects for the 24th February data snapshot.</li></ul><h3>Interview transcripts</h3><p>The interview transcripts have been anonymised and converted to markdown so that it's easier to process in general. List of interview transcripts:</p><ul><li>1269794877.md</li><li>1578450175.md</li><li>1792505583.md</li><li>2964377624.md</li><li>3270614512.md</li><li>40983347262.md</li><li>4288358080.md</li><li>4561769548.md</li><li>4938919540.md</li><li>5037840428.md</li><li>5766299900.md</li><li>5996360861.md</li><li>6422621713.md</li><li>6776362537.md</li><li>7183719943.md</li><li>7227322280.md</li><li>7336263536.md</li><li>75909371872.md</li><li>7869268779.md</li><li>8031500357.md</li><li>9253010492.md</li></ul><h3>Data Management Plan</h3><p>The study's Data Management Plan is provided in PDF format and shows the different data sets used throughout the duration of the study and where they have been deposited, as well as how long the SSI will keep these records. </p>
SUPPLEMENTARY DATA TO: Using a citizen science approach to assess nanoplastics pollution in remote high-altitude glaciers
<p>This is the repository of the supplementary data, and it contains the following files: </p> <p>Raw data files as the original output of TD-PTR-ToF-MS for all the samples, all the blanks, all the spikes and all the calibration runs (.h5 files in three zip arcives)</p> <p>Polymer library files (a zip archive including csv files.</p> <p>A data analysis file including raw data, blank subtraction and LOD correction of all measurements (xlsx file).</p> <p>A fingerprinting result file for each plastic type (xlsx file)</p> <p>A data analysis file after plastic fingerprinting (xlsx file). </p> <p> </p> <p> </p>
Data supporting research on journalistic production on science by press offices of universities and university centers in Rio Grande do Sul (2016)
<p>These data are part of a final paper called Scientific Journalism in Community Higher Education Institutions in Rio Grande do Sul. The work was developed at the University of Vale do Taquari - Univates, between 2016 and 2017. The data is in Portuguese. The abstract of the paper is available below. Science occupies an important place in today's society. It is what has allowed us to reach our current stages of intellectual development and also to achieve memorable feats as a species. Science is produced, to a large extent, in the academic environment. This monograph focuses its efforts on trying to understand how 15 universities and university centers in the state of Rio Grande do Sul, partners in the Consortium of Community Universities of Rio Grande do Sul (Comung), carry out the dissemination of their academic production. It is understood that it is important to disseminate scientific information to the population so that individuals can critically evaluate the actions developed in the field of science. The general objective of this study is to investigate the production of scientific news in higher education institutions linked to Comung, as well as to characterize the relationships between the actors and processes related to the journalistic dissemination of science produced in these same institutions. The qualitative and quantitative analysis of the scientific dissemination texts was directed to the news published by the organizations, through an exploratory study that mapped all the production of the press offices between January and August 2016. Subsequently, a qualitative analysis of the discourse of press officers was carried out, after applying a questionnaire. Throughout the investigation, the hypothesis that the institutions disseminate their scientific production was confirmed, but they do so in markedly different ways. The study is available at this link: https://www.univates.br/bdu/items/4cdf0835-1fbe-425a-a216-2a511e9aabf8. </p>
Data from "The academic impact of Open Science: a scoping review"
<p>These files include the data from the "The societal impact of Open Science - a scoping review", part of a series of studies conducetd within the PathOS Horizon Europe project on the academic, economic, and societal impacts of Open Science. This study was conducted in two phases. In phase 1 an academic database search was conducted. For phase 2 an automatic snowball search was performed based on results from phase 1 ( and grey literature was searched manually.</p> <p>The upload contains five files:</p> <ol> <li>Main file with extracted information for the 485 studies included in the review ("academic_impact_included_all_data.csv").</li> <li>Excel file documenting the grey literature search ("Grey_Literature_Search.xlsx").</li> <li>R project for the snowball search ("academic_impact_snowball.zip").</li> <li>R project for cleaning data and producing summaries and figures ("academic_impact_stats.zip").</li> <li>Excel file mapping the frascati codes used in file (1) to their textual representations ("Frascati definitions.xlsx")</li> </ol> <p>For more details on the methods see the <a href="https://osf.io/m4rnc">protocol</a> and its <a href="https://osf.io/3b6xj">addendum</a>. For the background, results and discussion see the <a href="../records/7883699">deliverable</a> (reporting on phase 1) and the pre-print.</p>
Data and code for: "Global Sampling Decline Erodes Science Potential of Natural History Collections"
<p># GBIF Specimen Data Analysis and Forecasting<br><br>## Version 2 - modified date ranges for figures 1 and 2 in response to reviewer comments</p> <p>This repository contains the code and data for analysing and forecasting trends in Global Biodiversity Information Facility (GBIF) specimen records across three major taxonomic groups: Chordata, Arthropoda, and Plantae. <br>The analysis pipeline includes data cleaning, anomaly detection, primary analyses, and forecasting based on historical database snapshots.</p> <p>These scripts and data correspond to analyses in the following manuscript:</p> <p>Global Sampling Decline Erodes Science Potential of Natural History Collections</p> <p>Authors:<br>Owen Forbes<br>Andrew G. Young<br>Peter H. Thrall</p> <p><br>## Repository Structure</p> <p>The repository consists of three main Quarto (.qmd) scripts and associated data files:</p> <p>1. `1_DataCleaning_Forbes-et-al_2025.qmd`: Data cleaning and anomaly detection<br>2. `2_PrimaryAnalyses_Forbes-et-al_2025.qmd`: Primary analyses and visualisation<br>3. `3_SnapshotsForecasting_Forbes-et-al_2025.qmd`: Historical snapshot analysis and forecasting</p> <p>## Requirements</p> <p>- R (version 4.3.2 or later)<br>- Required R packages:<br> - tidyverse (v2.0.0) - for data manipulation and visualization<br> - readr (v2.1.5) - for reading CSV/TSV files<br> - ggplot2 (v3.4.0 or v3.5.0) - for creating visualizations<br> - rnaturalearth (v1.0.1) - for accessing natural earth map data<br> - dplyr (v1.1.0 or v1.1.4) - for data manipulation<br> - countrycode (v1.6.0) - for converting country names and codes<br> - spdep (v1.3-3) - for spatial dependence modeling<br> - sp (v1.6-0 or v2.1-3) - for spatial data manipulation<br> - sf (v1.0-15 or v1.0-16) - for simple features access<br> - data.table (v1.14.8) - for fast aggregation of large data<br> - lubridate (v1.9.2) - for date-time manipulation<br> - viridis (v0.6.3) - for color palettes<br> - gridExtra (v2.3) - for arranging multiple plots<br> - ggpubr (v0.6.0) - for creating publication-ready plots<br> - zoo (v1.8-12) - for time series, including moving averages<br> - scales (v1.3.0) - for graphical scales<br> - forecast (v8.22.0) - for ARIMA forecast models<br> - purrr (v1.0.2) - for mapping custom forecast function onto each dataset<br> - arrow - for working with parquet files</p> <p>Install these packages before running the scripts.</p> <p>## How to Use</p> <p>1. Download this repository to your local machine.<br>2. Set your working directory to the location of the scripts.<br>3. Download raw datasets from GBIF (as required)<br>4. Ensure all required R packages are installed.<br>5. Run the scripts in RStudio or your preferred R environment.</p> <p>### Data Cleaning (`1_DataCleaning_Forbes-et-al_2025.qmd`)</p> <p>This script cleans the raw GBIF data and identifies anomalies. It produces files containing indexes of dataset records to be removed, which are used in subsequent analyses.</p> <p>**Note**: The raw GBIF exported datasets for contemporary records are not included in this repository due to file size constraints. Download them from the GBIF links provided in the script and place them in the `data/` directory.</p> <p>### Primary Analyses (`2_PrimaryAnalyses_Forbes-et-al_2025.qmd`)</p> <p>This script performs the main analyses and generates visualisations. It uses the outputs from the data cleaning script to filter anomalous records.</p> <p>To reproduce all analysis stages from the original raw .csv files:<br>- Start at the chunks labelled "DATA LOAD AND FILTERING".<br>- Run the pipeline for non-spatial analyses before spatial analyses.<br>- Due to memory constraints, it's recommended to run analyses for one taxonomic group and one analysis stream at a time.</p> <p>To skip to plot generation:<br>- Navigate to sections tagged as "@! SKIP TO PLOTTING !@".<br>- Ensure all required analysis output files are in the `data/` directory.</p> <p>### Forecasting (`3_SnapshotsForecasting_Forbes-et-al_2025.qmd`)</p> <p>This script analyses historical GBIF database snapshots and forecasts future growth. It uses the cleaned snapshot data produced by the data cleaning script.</p> <p>## Data Files</p> <p>### GBIF Exports - Raw Data (not included on Zenodo due to file size, please download directly from GBIF)<br>- `0016915-240425142415019.csv` for Chordata - https://www.gbif.org/occurrence/download/0016915-240425142415019</p> <p>- `0016914-240425142415019.csv` for Plantae - https://www.gbif.org/occurrence/download/0016914-240425142415019 </p> <p>- `0016913-240425142415019.csv` for Arthropoda - https://www.gbif.org/occurrence/download/0016913-240425142415019</p> <p>### Included Data Files</p> <p>#### Raw Data<br>- `GBIF_snapshots.parquet` # Historical snapshots RAW dataset (arrow/parquet format)<br>- `GBIF_integer_to_datasetKey.tsv` # Mapping old dataset IDs onto new datasetKey field</p> <p>#### Contemporary Datasets - data cleaning outputs<br>- `chordata_counts_to_highlight_030724` # List of anomalous Chordata dataset + year indexes to filter<br>- `arthropoda_counts_to_highlight_OG_030724` # List of anomalous Arthropoda dataset + year indexes to filter<br>- `plantae_counts_to_highlight_030724` # List of anomalous Plantae dataset + year indexes to filter</p> <p>#### Cleaned Snapshots<br>- `plantae_snapshots_filter_threshold_IN_040924` # Cleaned Plantae snapshots<br>- `arthropoda_snapshots_filter_threshold_IN_040924` # Cleaned Arthropoda snapshots<br>- `chordata_snapshots_filter_threshold_IN_040924` # Cleaned Chordata snapshots<br>- `gbif_dates_df_anomaly_filtered_090724` # Anomaly-filtered snapshots (combined dataset)<br>- `gbif_dates_df_anomalies_highlighted_090724` # Anomalies highlighted snapshots (combined dataset)</p> <p>#### Analysis Outputs - for skipping straight to plot/figure generation<br>- `arthropoda_specimens_per_year_080724` # Arthropoda specimen counts per year<br>- `arthropoda_unique_species_per_year_080724` # Arthropoda unique species counts per year<br>- `arthropoda_grid_counts_080724` # Arthropoda grid counts<br>- `chordata_specimens_per_year_080724` # Chordata specimen counts per year<br>- `chordata_unique_species_per_year_080724` # Chordata unique species counts per year<br>- `chordata_grid_counts_080724` # Chordata grid counts<br>- `plantae_specimens_per_year_080724` # Plantae specimen counts per year<br>- `plantae_unique_species_per_year_080724` # Plantae unique species counts per year<br>- `plantae_grid_counts_080724` # Plantae grid counts<br>- `chordata_continent_count_080724` # Chordata continent-specific counts<br>- `arthropoda_continent_count_080724` # Arthropoda continent-specific counts<br>- `plantae_continent_count_080724` # Plantae continent-specific counts</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.