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142 results for “Research impact”
BIP! DB: A Dataset of Impact Measures for Research Products
<h2>Overview</h2> <p>This dataset contains citation-based impact indicators (also referred as <em>measures</em>) for ~296M distinct persistent identifiers (PIDs) that correspond to various types of research products (publications, datasets, software, and other products).</p> <p>The calculated indicators are organized into categories based on the aspect of impact they capture. </p> <h3>Influence indicators</h3> <p>Reflect the "total" impact of a research product; how established it is in general.</p> <ul> <li><strong><em>Citation Count:</em></strong> The total number of citations of the product, the most well-known influence indicator.</li> <li><strong><em>PageRank score:</em> </strong>An influence indicator based on the PageRank (Page et al., 1999), a popular network analysis method. PageRank estimates the influence of each product based on its centrality in the whole citation network. It alleviates some issues of the Citation Count indicator (e.g., two products with the same number of citations can have significantly different PageRank scores if the aggregated influence of the products citing them is very different - the product receiving citations from more influential products will get a larger score). </li> </ul> <h3>Popularity indicators</h3> <p>Capture the "current" impact of a research product; how popular it currently is.</p> <ul> <li><strong><em>RAM score:</em></strong> A popularity indicator based on the RAM (Ghosh et al., 2011) method. It is essentially a Citation Count where recent citations are considered as more important. This type of "time awareness" alleviates problems of methods like PageRank, which are biased against recently published products (new products need time to receive a number of citations that can be indicative for their impact).</li> <li><strong><em>AttRank score:</em></strong><strong> </strong>A popularity indicator based on the AttRank (Kanellos et al., 2020) method. AttRank alleviates PageRank's bias against recently published products by incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to examine products which received a lot of attention recently.</li> </ul> <h3>Impulse indicators</h3> <p>Measure the initial momentum that a research product received right after its publication.</p> <ul> <li><em><strong>Incubation Citation Count (3-year CC):</strong> </em>This impulse indicator is a time-restricted version of the Citation Count, where the time window length is fixed for all products and the time window depends on the publication date of the product, i.e., only citations 3 years after each product's publication are counted.</li> </ul> <h3>FIeld-weighted indicators</h3> <p>Capture the impact of a research product relative to the average performance in its field, accounting for differences in citation practices across disciplines.</p> <ul> <li><strong>Field-Weighted Citation Impact (FWCI):</strong> A field-weighted indicator that measures how a research product performs compared to the global average in its research field. An FWCI of 1.0 indicates that the product is cited exactly as expected for similar publications in the same field; values above 1.0 indicate above-average impact, while values below 1.0 indicate below-average impact.</li> <li><strong>3-year FWCI:</strong> A time-restricted version of the FWCI that considers citations received within the first three years after publication. By limiting the citation window, this indicator captures the early relative impact of a research product, providing insight into how quickly it gains influence in its field.</li> </ul> <p>In our analysis, the expected number of citations for each research product is computed by <em>grouping them by concept, publication year, and product type and then averaging the citations within each group</em>. </p> <p><em>More details about the aforementioned impact indicators, the way they are calculated and their interpretation can be found <a href="https://bip.imsi.athenarc.gr/site/indicators">here</a> and in the respective references (Kanellos et al., 2019).</em></p> <h2>Indicator calculation levels</h2> <p>The impact indicators are calculated in two levels:</p> <ul> <li><strong>PID level: </strong> assuming that each PID corresponds to a distinct research product. Currently PIDs are DOIs, PMCIDs, and PMIDs.</li> <li><strong>OpenAIRE-id level: </strong>leveraging PID synonyms based on OpenAIRE's deduplication algorithm (Manghi et al., 2020) - each distinct article has its own OpenAIRE id.</li> </ul> <h2>Impact classes</h2> <p>Each researcj product is also assigned an impact class, reflecting its percentile rank among all products in the dataset: </p> <table style="border-collapse: collapse; width: 100%; height: 39.1876px;"><colgroup><col style="width: 33.2913%;"><col style="width: 33.2913%;"><col style="width: 33.2913%;"></colgroup> <tbody> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;"><strong>Class</strong></td> <td style="height: 19.5938px;"><strong>Percentile</strong></td> <td style="height: 19.5938px;"><strong>Description</strong></td> </tr> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;">C1</td> <td style="height: 19.5938px;">Top 0.01%</td> <td style="height: 19.5938px;">Exceptional impact</td> </tr> <tr> <td>C2</td> <td>Top 0.1%</td> <td>Very high impact</td> </tr> <tr> <td>C3</td> <td>Top 1%</td> <td>High impact</td> </tr> <tr> <td>C4</td> <td>Top 10%</td> <td>Good impact</td> </tr> <tr> <td>C5</td> <td>Rest 90%</td> <td>Remaining products</td> </tr> </tbody> </table> <h2>File structure</h2> <p>For each calculation level (PID / OpenAIRE-id) we provide five (5) compressed CSV files (one for each measure/score provided). The structure of the files differs slightly depending on the level:</p> <ul> <li> <p><strong>PID-level files:</strong> Each line follows the format:<br><code>identifier <tab> identifier_type <tab> score <tab> class</code></p> </li> <li> <p><strong>OpenAIRE-id-level files:</strong> These files contain the keyword "openaire_ids" in the filename. Each line follows the format:<br><code>identifier <tab> score <tab> class</code></p> </li> </ul> <p><em>The parameter setting of each measure is encoded in the corresponding filename. For more details on the different measures/scores see our extensive experimental study (Kanellos et al., 2019) and the configuration of AttRank in the original paper (Kanellos et al., 2020).</em></p> <h3>Topic-related files</h3> <p>In addition to the main indicator files, the dataset also includes <em>topic-level outputs</em>, providing <em>field-weighted impact indicators</em> as well <em>percentile classes</em> within the associated <em>2nd-level concepts from OpenAlex</em>. </p> <p>Specifically, we associated all research products with their 2nd level concepts from OpenAlex (using only their <em>DOIs</em>); we kept only the three most dominant concepts for each product, based on their confidence score, and only if this score was greater than 0.3.</p> <p>Since currently only the DOIs are used to associate concepts from OpenAlex to research products, all identifiers in these files refer to DOIs. </p> <ul> <li><strong>Topic-specific impact classes file:</strong> Fore each concept and indicator, precentile classes are computed and provided in <code>topic_based_impact_classes.txt</code> in the following format:</li> </ul> <p><code>identifier <tab> concept <tab> pagerank_class <tab> attrank_class <tab> 3-cc_class <tab> cc_class</code></p> <ul> <li><strong>Field-weighted indicator files:</strong> Each line follows the format:<br><code>identifier <tab> concept <tab> score</code></li> </ul> <p><em>Note that to prevent division by zero, the score column is left empty whenever the average score for a specific combination of concept, publication year, and product type equals zero.</em></p> <h2>Data sources</h2> <p>The data used to produce the citation network on which we calculated the provided measures have been gathered from the OpenAIRE Graph v10.5.0, including data from (a) <em>OpenCitations' COCI & POCI dataset</em>, (b) <em>MAG</em> (Sinha et al, 2015; Wang et al., 2019), and (c) <em>Crossref</em>. The union of all distinct citations that could be found in these sources have been considered. </p> <p>Additionally, all topic-related computations are derived from OpenAlex concepts.</p> <h2>Access and Use</h2> <p>Find our Academic Search Engine built on top of these data <a href="https://bip.imsi.athenarc.gr/">here</a>. Further note, that we also provide all calculated scores through <a href="https://bip-api.imsi.athenarc.gr/documentation">BIP! Finder's API</a>. </p> <p><em>Terms:</em> These data are provided "as is", without any warranties of any kind. The data are provided under the CC0 license.</p> <h2>Changelog</h2> <p><strong>v19.1</strong></p> <ul> <li>[major update] Added field-weighted indicators: FWCI and 3-year FWCI.</li> </ul> <p><strong>v19.0</strong></p> <ul> <li>Added PMCID as an additional type of PID.</li> </ul> <p><strong>v15.1</strong></p> <ul> <li>Fixed missing records that were unintentionally omitted in v15.0</li> <li>Ensures all popularity indicators correctly use <code>current_year = 2025</code></li> </ul> <p><strong>v12.0</strong></p> <ul> <li>Added PMIDs as an additional type of PID.</li> </ul> <p><strong>v10.0</strong></p> <ul> <li>[Major update] Introduced deduplication of research products using the latest <a href="https://graph.openaire.eu/docs/graph-production-workflow/deduplication/research-products">OpenAIRE article deduplication algorithm</a>. Each node in the citation network is now a deduplicated product having a distinct OpenAIRE id. <ul> <li>Corrected overcounting of citations caused by multiple versions of the same product.</li> <li>PID-level scores are now derived from deduplicated OpenAIRE nodes.</li> </ul> </li> <li>Added filtering rules described <a href="https://graph.openaire.eu/docs/graph-production-workflow/aggregation/non-compatible-sources/doiboost/#crossref-filtering">here</a> to remove from dataset PIDs with problematic metadata. </li> </ul> <p><strong>v9.0</strong></p> <ul> <li>[Major update] Introduced topic-specific impact classes for PID-identified products based on OpenAlex 2nd-level concepts.</li> </ul> <p><strong>v7.0</strong></p> <ul> <li>[Major update] Added impact class labels (C1-C5) for each procuct, indicating the percentile-bsaed impact levels. <ul> <li>Classes reflect relative position within the global score distribution.</li> </ul> </li> </ul> <p><strong>v5.1</strong></p> <ul> <li>[Major update] Introduced dual-level score computation: PID level and OpenAIRE ID level.</li> </ul>
Impact of urban and shipping emissions on NASA-Unified Weather Research and Forecasting model results
<p>This dataset supports Huang et al. (2019, JGR-Atmospheres): "Impact of aerosols from urban and shipping emission sources on terrestrial carbon uptake and evapotranspiration: a case study in East Asia". The file named "NUWRFout.tar.gz" contains NUWRF base and sensitivity simulation results on 31 May 2016. The file named "LIS_soil_LAI.zip" contains model grid information, soil conditions and leaf area index (LAI) at NUWRF initialization times in late May 2016.</p>
OpenUP survey on researchers' current perceptions and practices in peer review, impact measurement and dissemination of research results
<p>OpenUP project (http://openup-h2020.eu/) conducted a survey to capture current perceptions and practices in peer review, dissemination of research results and impact measurement among European researchers. The survey was coducted between 20 January and 23 February 2017. It consisted of four sections. The first section asked a series of questions on the respondents’ scientific discipline, career stage, gender and other characteristics. The following sections asked a series of questions on peer review practices, dissemination of research results and impact measurement/use of altmetrics. The questionnaire was collaboratively prepared by the OpenUP consortium. </p> <p>The survey was implemented via surveygizmo tool (https://www.surveygizmo.com/). Invitations to participate were sent to a random sample of researchers from arXiv, Pubmed and RePEc. The OpenUP team mined researchers’ contact details from these platforms. The OpenUP project team made efforts to further boost the repondent sample for certain underrepresented areas through the DARIAH website, THESIS network, EURODOC, AIMS portal, the Parthenos community and other channels. The survey targeted researchers from the EU-28, Switzerland and Norway. The goal was to get around 1,000 responses. In total, there were 976 completed response and completion rate was 72.4%. </p> <p>The attached documents include the questionnaire and the dataset. In the dataset (cvs file) the top row contains numbered questions that correspond to the numberring in the questionnaire (word file). The data was exported as an excel file, anonymised by creating respondent IDs and IP data were deleted. The file was then converted to CSV.</p> <p> </p>
Research data supporting "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains"
<p>Research data supporting the peer-reviewed article "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains" by the same authors.</p>
Mapping Building BioData.pt Indicators against the performance and impact assessment frameworks for research infrastructures of OECD, ESFRI and RI-PATHS project
<p>"Buiding BioData.pt" indicators observed in international frameworks for performance and impact assessment of research infrastructures, namely, OECD, ESFRI and RI-PATHS.</p>
Impact of Software Engineering Research in Practice: A Patent and Author Survey Analysis
<p>Dataset of the research paper: <strong>Impact of Software Engineering Research in Practice: A Patent and Author Survey Analysis</strong></p> <p>Existing work on the practical impact of software engineering (SE) research examines industrial relevance rather than adoption of study results, hence the question of how results have been practically applied remains open. To answer this and investigate the outcomes of impactful research, we performed a quantitative and qualitative analysis of 4 354 SE patents citing 1 690 SE papers published in four leading SE venues between 1975–2017. Moreover, we conducted a survey on 475 authors of 593 top-cited and awarded publications, achieving 26% response rate. Overall, researchers have equipped practitioners with various tools, processes, and methods, and improved many existing products. SE practice values knowledge-seeking research and is impacted by diverse cross-disciplinary SE areas. Practitioner-oriented publication venues appear more impactful than researcher-oriented ones, while industry-related tracks in conferences could enhance their impact. Some research works did not reach a wide footprint due to limited funding resources or unfavorable cost-benefit trade-off of the proposed solutions. The need for higher SE research funding could be corroborated through a dedicated empirical study. In general, the assessment of impact is subject to its definition. Therefore, academia and industry could jointly agree on a formal description to set a common ground for subsequent research on the topic.</p> <p>The following data files are included.</p> <ul> <li><em>./fields</em>: <ul> <li><strong>engi-fields.csv</strong>: Publication and PhD dissertation counts of main engineering branches</li> <li><strong>engi-fields-queries.txt</strong>: Queries applied to Elsevier's Scopus and Open Access Theses and Dissertations databases to retrieve the publication and dissertation counts</li> </ul> </li> <li><em>./patents</em>: <ul> <li><strong>sample-se-references-verified.csv</strong>: Manual verification of a random sample of references by software engineering (SE) patents to SE papers</li> <li><strong>se-cpc.tsv</strong>: Manually-identified SE-related Cooperative Patent Classification (CPC) categories</li> <li><strong>se-references-in-patents.csv</strong>: SE references made by SE patents to SE papers</li> <li><em>./patents/litigation</em>: <ul> <li><strong>case-values.csv</strong>: Manually-retrieved litigation damages of citing SE patents</li> <li><strong>lit-per-paper.csv</strong>: Litigation cases of citing SE patents</li> </ul> </li> <li><em>./patents/maintenance</em>: <ul> <li><strong>maint-code-fee-mapping.csv</strong>: Mapping of patent maintenance fee codes to their fee values</li> <li><strong>maint-fees.csv</strong>: Fee values of maintenance fee codes</li> <li><strong>maint-per-paper.csv</strong>: Maintenance fee events of citing SE patents</li> </ul> </li> <li><em>./patents/reports</em>: <ul> <li><strong>lit-sum-per-paper.csv</strong>: Counts and total damages of litigation cases of patent-cited SE papers</li> <li><strong>maint-sum-per-paper.csv</strong>: Counts and total values of maintenance fee events of patent-cited SE papers</li> <li><strong>patent-ref-counts.csv</strong>: SE patent citation counts of patent-cited SE papers</li> </ul> </li> </ul> </li> <li><em>./survey</em>: <ul> <li><strong>emse-top.csv</strong>: Most-cited papers of the Empirical Software Engineering (EMSE) journal</li> <li><strong>icse-bp.csv</strong>: Distinguished papers of the International Conference of Software Engineering (ICSE)</li> <li><strong>icse-mip.csv</strong>: Most influential ICSE papers</li> <li><strong>icse-top.csv</strong>: Most-cited ICSE papers</li> <li><strong>survey-questionnaire-emse.pdf</strong>: The EMSE survey questionnaire</li> <li><strong>survey-questionnaire.pdf</strong>: The ICSE, TSE, and TOSEM survey questionnaire</li> <li><strong>survey-responses.csv</strong>: The anonymized survey responses</li> <li><strong>tosem-top.csv</strong>: Most-cited papers of the ACM Transactions on Software Engineering and Methodology (TOSEM)</li> <li><strong>tse-top.csv</strong>: Most-cited papers of the IEEE Transactions on Software Engineering (TSE)</li> <li><em>./survey/manual-coding</em>: <ul> <li><strong>feedback.txt</strong>: Manual coding of survey feedback</li> <li><strong>practical-impact.csv</strong>: Manual coding of responses about practical impact of work</li> <li><strong>practical-impact-lack.csv</strong>: Manual coding of responses about lack of practical impact</li> <li><strong>research-methods.csv</strong>: Manual coding of additional research methods of surveyed papers</li> <li><strong>state-of-practice.csv</strong>: Manual coding of responses about changes in state of practice</li> </ul> </li> </ul> </li> <li><em>./venues</em>: <ul> <li><strong>se-venues.csv</strong>: Top SE venues according to Google Scholar Metrics</li> <li><strong>se-venues-impact.csv</strong>: SE patent citations and patent-based impact factors of SE venues</li> <li><strong>se-venues-scopus-queries.txt</strong>: Queries applied to Scopus to retrieve the publication counts of the SE venues</li> </ul> </li> </ul>
Polidoc.net CODEBOOK: National and Regional Manifestos and other Political Documents Collected for the Research Projects "Representation in Europe: Congruence between Preferences of Elites and Voters" (REPCONG) and "The Impact of EU Cohesion Policy on European Identification" (COHESIFY)
<p>The Political Documents Archive http://www.polidoc.net/ contains election manifestos, coalition agreements, government declarations and various other documents of political actors from developed democracies. Currently, the archive builds on a stock of more than 3000 political documents from 20 European countries. The aim of the repository is to provide political texts in order to facilitate scholarly research in different areas of comparative politics such as party competition, coalition politics, legislative decision-making or electoral behavior.</p> <p>National electoral manifestos have been collected in the course of the REPCONG project ("Representation in Europe: Policy Congruence between Citizens and Elites"), and the archive includes party manifestos for regional elections in several European democracies. Because the process of European integration resulted in a strengthening of regions in EU member states and in countries that want to join the European Union, the relevance of the regional level for political decision-making has increased during the last decades. Therefore, also the policy profiles of regional parties are required to get a full picture of democratic responsiveness in European states across all levels of the political system. The collection of regional manifestos was supported by the COHESIFY project (www.cohesify.eu), funded under the Horizon 2020 Framework Programme for Research and Innovation. The aim of COHESIFY is to study whether the European Structural and Investment Funds affect people’s support for and identification with the European project.</p> <p>The archive is freely accessible (after a simple registration) and meant to foster rigorous research in these areas by enabling scholars to produce valid and reliable findings from empirical studies of textual data rather than unnecessarily struggling to obtain and process texts.</p>
Data From: Toward a Better Data Management Plan: The Impact of DMPs on Grant Funded Research Practices
<p>Researchers from Montana State University analyzed 186 National Science Foundation (NSF) data management plans, using the Data Management Plans As a Research Tool (DART) rubric.</p>
Impacts of wildfire on stream water chemistry in the Caribou-Poker Creeks Research Watershed during the summers of 2002-2007
The hydrogeochemistry of two streams (C1 and P6) in the Caribou-Poker Creeks Research Watershed (CPCRW) during the summers of 2002-2007. The P6 sub-catchment of the CPCRW was extensively burned during the Boundary Fire of 2004. Samples were analyzed for Ca, Mg, Na, K, NO3, SO4, DOC, TDN, SUVA and conductivity.
A Slice of the Research Cake: The Impact of Open Science in Africa
<p><strong>Episode Summary: </strong></p> <p>In this episode we talk to Joy Owango, Founding Director at Training Centre in Communication. We discussed how Open Science is democratising research and access to data and publishing in Africa, the importance of ownership in research, and the challenges inherent in widespread change. </p> <p>Originally we interviewed another two guests who are involved in the Open Science movement on the African continent: Osman Aldirdiri, founding director of Open Sudan initiative and Jo Havemann, co-founder of the AfricArXiv. Due to technical difficulties we can only publish the audio from Joy Owango. However, we do have a <a href="https://drive.google.com/file/d/1sOhAaiWjAUwQlDMuaqd5YCC3SOhXDFSP/view?usp=sharing">transcript of the full interview</a> with all three guests.</p> <p><strong>Episode Links:</strong></p> <ul> <li><a href="https://info.africarxiv.org/">AfricArXiv</a> <p><a href="https://twitter.com/AfricArxiv">Twitter</a></p> </li> <li>Joy Owango <ul> <li><a href="https://www.tcc-africa.org/">Training Centre in Communication</a></li> <li><a href="https://twitter.com/JoyOwango">Twitter</a></li> </ul> </li> <li>Osman Aldirdiri <ul> <li><a href="https://blog.okfn.org/2017/03/30/open-data-day-sudan-2017-openness-for-advancing-research-and-discovery/">Open Sudan's Open Data Day</a></li> <li><a href="https://twitter.com/aldirdiri">Twitter</a></li> </ul> </li> <li>Jo Havemann <ul> <li><a href="https://access2perspectives.com/team/jo-havemann/">Access2Perspectives </a></li> </ul> </li> </ul>
India Flood Inventory-Impacts (IFI-Impacts) [1967-2023]: A multi-source national geospatial database to facilitate comprehensive flood research
<p>This repository hosts the India Flood Inventory with Impacts (IFI-Impacts) database. It contains flood event data sourced from the Indian Meteorological Department from 1967-2023. It has undergone extensive manual digitization, cleaning, and includes new information to make it suitable for computational research in hydroclimate.</p> <p>v4.0: Development of District Flood Severity Index (DFSI)</p> <p>v3.0: India Flood Inventory (IFI) 1967-2023. Updated with local government codes (LGD) for state and district. </p> <p>v1.0: India Flood Inventory (IFI) 1967-2016.</p> <p>v2.0: India Flood Inventory (IFI) 1967-2023. With impacts and district flooded area.</p> <p><strong>REFERENCES</strong></p> <p>Saharia, M., Jain, A., Baishya, R.R., Haobam, S., Sreejith, O.P., Pai, D.S., Rafieeinasab, A., 2021. India flood inventory: creation of a multi-source national geospatial database to facilitate comprehensive flood research. Nat Hazards. <a href="https://doi.org/10.1007/s11069-021-04698-6">https://doi.org/10.1007/s11069-021-04698-6</a></p> <div> <div>Saharia, M., Jain, S.K., Prakash, V., Malik, H., Sreejith, O.P., Joshi, D., 2025. A district-level flood severity index for flood management in India. Nat Hazards. <a href="https://doi.org/10.1007/s11069-025-07493-9">https://doi.org/10.1007/s11069-025-07493-9</a></div> </div>
Package and Dependency Metadata for CZI Hackathon: Mapping the Impact of Research Software in Science
<p>A collection of useful datasets extracted from <a href="https://packages.ecosyste.ms">https://packages.ecosyste.ms</a> and <a href="https://repos.ecosyste.ms/">https://repos.ecosyste.ms</a> for use at the CZI Hackathon: Mapping the Impact of Research Software in Science.</p><p>All data is provided as NDJSON (new line delimited JSON), each line represents a valid JSON object, and they are separated by newline characters. There are <a href="https://pypi.org/project/ndjson/">python</a> and <a href="https://www.rdocumentation.org/packages/ndjson/versions/0.9.0/topics/stream_in">R</a> libraries for reading these files, or you can maually read each line and parse each line as a single JSON object.</p><p>Each ndjson file has been compressed with gzip (actual command: `tar -czvf`) to reduce download size, they expand to significantly bigger files after extraction.</p><h4>Package Data</h4><p>Package names from cran, bioconductor and pypi that have been parsed by the <a href="https://github.com/chanzuckerberg/software-mentions">software-mentions</a> project (data: <a href="https://datadryad.org/stash/dataset/doi:10.5061/dryad.6wwpzgn2c">https://datadryad.org/stash/dataset/doi:10.5061/dryad.6wwpzgn2c</a>) are collected together with their latest release at time of publishing along with the names of their dependencies, those dependency names have then also been recursively fetched with latest release and dependencies until the full list of transitive dependencies is included. </p><p>Note: This approach uses a simplified method of dependency resolution, always picking the latest version of each package rather than taking into account each dependencies specific version range requirements, this is primarily due to time constraints and allows all software ecosystems to be processed in the same way. A future improvement would be to use each package ecosystem's specific dependency resolution algorithm to compute the full transitive dependency tree for each mentioned software package.</p><h4>GitHub Data</h4><p>Two different approaches were taken for collecting data for referenced GitHub mentions:</p><p>1. `github.ndjson` is metadata for each repository from GitHub, including "manifest" files which are known files that contain dependency information for a project such as requirements.txt, DESCRIPTION and package.json, parsed using <a href="https://github.com/ecosyste-ms/bibliothecary">https://github.com/ecosyste-ms/bibliothecary</a>, which may include transitive dependencies that have been discovered in a `lockfile` within the repository.</p><p>2. `github_packages.ndjson` is metadata for each package that was found on any package manager that references the GitHub url as it's repository url/source/homepage, these packages, like the cran and pypi data above, include the latest release and their direct dependencies. There may be more than one package for each GitHub URL as it is a one to many relationship. `github_packages_with_transitive.ndjson` follows the same format but also includes the extra resolved transitive dependencies of all packages using the same approach as with cran and pypi data above with the same caveats. </p><p>There are also many more ecosystems referenced in these files than just cran, bioconductor and pypi, https://packages.ecosyste.ms provides a standardized metadata format for all of them to enable comparison and simplification of automation.</p><h4>Contact</h4><p>If you would like any help, support or more data from Ecosyste.ms please do get in touch via email: hello@ecosyste.ms or open an issue on GitHub: https://github.com/ecosyste-ms/packages/issues</p>
TWIN2PIPSA - Twinning for strategic networking and impactful research and innovation in Biomedicine and Biotechnology
<p>Presentation by Prof. Cláudio M. Gomes at the 5<sup>th</sup> edition of Ciências Research & Innovation Day.</p>
Core bibliometric Covid19 and comparable research dataset and code for the study "From intent to impact: Investigating the effects of open sharing commitments"
<p>This document provides the underlying dataset for the bibliometric component for the 2022 study "From intent to impact: Investigating the effects of open sharing commitments" by Research Consulting and Science-Metrix.</p> <p>Before reproducing the study findings or re-using the underlying datasets for other purposes, please cautiously review their limitations in the study's technical annex and main report, available at: https://zenodo.org/communities/data-sharing-in-public-health-emergencies/ </p> <p>Particularly, note that there is an error rate in attribution of signatory status to journal publications and preprints; in their location within specific thematic disease-based areas; or computing of dimension such as identification of data availability statement sections; identification of data depisition mentions within data availability statement sections; or matching of preprints and journal publications.</p> <p>These error rates are expected and have been estimated, please consult the technical report for full details.</p> <p> </p> <p>Definition of data fields is provided is the table below:</p> <table> <tbody> <tr> <td>Column name </td> <td>Definition</td> </tr> <tr> <td>document_type</td> <td>preprint or journal publication</td> </tr> <tr> <td>doi</td> <td>digital object identifier</td> </tr> <tr> <td>arxiv_id</td> <td>arXiv preprint server's unique identifier for its preprints</td> </tr> <tr> <td>ssrn_id</td> <td>SSRN preprint server's unique identifier for its preprints. Note that some of these IDs are contained within the DOIs also assigned to some (but not all) SSRN preprints , in the form of "10.2139/ssrn." + 'ssrn_id'</td> </tr> <tr> <td>coalesce_id</td> <td>coalesce function applied to the DOI, arxiv_id and ssrn_id. Redundant for journal publications.</td> </tr> <tr> <td>preprint_server</td> <td>Preprint platform on which a preprint has been published, restricted to arXiv, bioRxiv, medRxiv and SSRN for this study.</td> </tr> <tr> <td>journal_title</td> <td>Publishing journal name in the case of a journal publication.</td> </tr> <tr> <td>year</td> <td>The set is restricted to 2020 and 2021 for Covid19 preprints and journal publications. HVRD journal publications restricted to 2018-2019. HVRD preprints were restricted to 2020-2021 instead, to compensate for the lac of year-normalization for preprints, and generally better control findings against the launch of medRxiv in 2019.</td> </tr> <tr> <td>publication_title</td> <td>Title of the individual journal publication or preprint, not that of the publishing journal or preprint server.</td> </tr> <tr> <td>authors</td> <td>First 100 researchers that appear as authors of a preprint or journal publication. These are not parsed and provided for qualitative validation or assessments rather than for further quantitative treatment.</td> </tr> <tr> <td>Covid19</td> <td>Journal publications or preprints are coded 1 if they has been identified as falling into this thematic area through our queries (see the technical annex), 0 otherwise</td> </tr> <tr> <td>HVRD</td> <td>Human viral respiratory disease, the thematic area considered to be the closest to Covid19. Journal publications or preprints are coded 1 if they has been identified as falling into this thematic area through our queries (see the technical annex), 0 otherwise</td> </tr> <tr> <td>Journal_sig</td> <td>Journal publications where the publishing journal and/or its publishing house are Joint Statement signatories. Coded as 1 if they are signatories, 0 if not signatory, null if status could not be determined due to insufficient metadata. Not that all preprint servers included in this study are Joint Statement signatories. This category was fully removed from the models for preprints, rather than all preprints being assigned automatic signatory status.</td> </tr> <tr> <td>RPO_sig</td> <td>Journal publications and preprints where at least one author is affiliated with at least one research performing organization that is a Joint Statement signatory. Coded as 1 ifor signatory, 0 if not signatory, null if status could not be determined due to insufficient metadata.</td> </tr> <tr> <td>Funder_sig</td> <td>Journal publications and preprints where at least one funder supporting the research is a Joint Statement signatory. Coded as 1 ifor signatory, 0 if not signatory, null if status could not be determined due to insufficient metadata. Although funding is attributed to researchers rather than publications, funding metadata is more readily available at the second level. This approach also captures the flexible usage of financial resources that researchers may make accross mulitple concurrently ongoing research projects.</td> </tr> <tr> <td>overton_norm</td> <td>Year and subfield-normalized binary score of whether the journal publications has been cited by one or more policy-related documents from the Overton database. Null scores for journal publications not covered by the database.</td> </tr> <tr> <td>overton</td> <td>Normalizations being unable for preprints, binary score of whether the preprint has been cited by one or more policy-ralated documents from the Overton database. Null scores for preprints not covered by the database.</td> </tr> <tr> <td>daswriting_binary</td> <td>Binary score capturing identification of a data availability statement in the journal publication or preprint using the queries presented in the technical annex. Null scores are for publications and preprints where records of full texts were unavailable for text mining, or were this analysis could not be performed due to licensing restrictions. </td> </tr> <tr> <td>deposition_binary</td> <td>Binary score capturing identification of a data availability statement and data deposition mention therein in the journal publication or preprint using the queries presented in the technical annex. Null scores are for publications and preprints where records of full texts were unavailable for text mining, or were this analysis could not be performed due to licensing restrictions. </td> </tr> <tr> <td>is_oa</td> <td>Binary score capturing OA or free-to-read (also so-calleod "bronze OA" and "green OA") status of journal publications. Unpaywall categories have been used in a mutually exclusive implementation, with the best (gold > hybrid>bronze>green) possible applicable category being retained. Null scores for journal publications not covered in our Unpaywall dataset. Scores of 0 denote journal publications not available under an OA or free-to-read category.</td> </tr> <tr> <td>is_gold</td> <td>as above</td> </tr> <tr> <td>is_hybrid</td> <td>as above</td> </tr> <tr> <td>is_bronze</td> <td>as above</td> </tr> <tr> <td>is_green</td> <td>as above</td> </tr> <tr> <td>matched_journal_binary</td> <td>For preprints, whether one or more matching journal publications could be identified using the queries identified in the technical, or preprint servers' own lists of preprint-journal publication matches. Null scores for preprints with insufficient metadata information to perform the matching operation.</td> </tr> <tr> <td>matched_journal_doi</td> <td>For those preprints with or more matching journal publications, the DOI(s) of the matching journal publication(s). Note that some of the maching journal publications identified do not have DOIs.</td> </tr> <tr> <td>matched_preprint_binary</td> <td>For journal publications, whether one or more matching preceding preprints could be identified using the queries identified in the technical annex, or preprint servers' own lists of preprint-journal publication matches. Null scores for journal publications without sufficient metadata to run the analysis.</td> </tr> <tr> <td>matched_preprint_id</td> <td>For those journal publications preceded with one or more arXiv, bioRxiv, medRxiv or SSRN preprints, the DOI(s), arXiv ID and/or SSRN ID of the matching preprint(s). </td> </tr> <tr> <td>hasdoi</td> <td>Only journal publications with DOIs were retained in the core quantitative analyses.</td> </tr> <tr> <td>hasacknowledgements</td> <td>Only journal publications with funding acknowledgements (to determine funding-based signatory status) were retained in the core quantitative analyses.</td> </tr> <tr> <td>funder_array</td> <td>Array (but cast as string) of names of the funders on the basis of whose idenitification signatory status has been attributed, where relevant. Null if non-signatory or unknown signatory status.</td> </tr> <tr> <td>RPO_array</td> <td>Array (but cast as string) of names of the research performing organizations on the basis of whose idenitification signatory status has been attributed, where relevant. Null if non-signatory or unknown signatory status.</td> </tr> <tr> <td>DAS_excerpt</td> <td>Journal publication or preprint text excerpt on which succesful identifcation of data availability statements and/or data deposition mentions have been made. Null both where the query could not be run at all, or where the query was negative.</td> </tr> <tr> <td>big5</td> <td>Journal publication published in a journal owned by one of the following five publishing houses: Elsevier, Sage, Springer Nature, Taylor-Francis, Wiley.</td> </tr> <tr> <td>LMIC</td> <td>Journal publication whose authors include at least one researcher affiliated with at least one institution located in a lower-middle income country as defined by the World Bank</td> </tr> <tr> <td>LIC</td> <td>Journal publication whose authors include at least one researcher affiliated with at least one institution located in a low income country as defined by the World Bank</td> </tr> <tr> <td>SouthNorth</td> <td>Journal publication whose authors include at least one researcher affiliated with at least one institution located in a upper-middle income country, a lower-middle income country, or a low income country as defined by the World Bank; as well as at least one researcher affiliated with at least one institution located in a high income country. For the purpose of this indicator, Sicnece-Metrix exceptionally includes China and Bulgaria in the list of high income countries.</td> </tr> <tr> <td>DID_allauthors_OR</td> <td>Journal publication is included in the difference-in-difference model defining signatory publication as EITHER holding journal-based signatory status OR funding-based signatory status, and where no filter has been applied to control for author-level biases.</td> </tr> <tr> <td>DID_authorcontrol_OR</td> <td>Journal publication is included in the difference-in-difference model defining signatory publication as EITHER holding journal-based signatory status OR funding-based signatory status, and where a filter has been applied to control for author-level biases.</td> </tr> <tr> <td>DID_authorcontrol_AND</td> <td>Journal publication is included in the difference-in-difference model defining signatory publication as holding journal-based signatory status AND funding-based signatory status, and where a filter has been applied to control for author-level biases.</td> </tr> <tr> <td>DID_allauthors_AND</td> <td>Journal publication is included in the difference-in-difference model defining signatory publication as holding journal-based signatory status AND funding-based signatory status, and where no filter has been applied to control for author-level biases.</td> </tr> <tr> <td>Preprint_authorcontrol</td> <td>Preprint is included in the the analytical breakdowns where a filter has been applied to control for author-level biases. Note that authors have been kept constant in preprints on the basis of their belonging to all analytical breakdowns in journal publications rather than in preprint-based groups.</td> </tr> </tbody> </table> <p> </p>
Research data related to the article "Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach"
<div><strong>Research Data related to the article "Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach" by Seibert et al. (2024) published in <em>Advances in Water Resources</em></strong></div> <div> </div> <div>Dear reader,</div> <div> </div> <div>reasearch data are provided for the research article "Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach" by Seibert et al. (2024) published in <em>Advances in Water Resources</em> (https://doi.org/10.1016/j.advwatres.2024.104763). The authors hope that the research data allows for a better understanding of the modeling workflow. The research data covers the following files:</div> <div> <ul> <li>Python scripts to create the models <ul> <li>Model scripts using FloPy (Bakker et al., 2016) are stored as .py files in './model_data/flopy_scripts/', named 'model_variant_vXYZ.py', where 'XYZ' is a wildcard for the model number. </li> <li>--> Note that model numbers correspond to the different model variants as referred to in the article, see overview below.</li> <li>The model scripts require postfix files, stored in './model_data/flopy_scripts/postfix/', a PHREEQC database file, stored in './model_data/flopy_scripts/template_database/', as well as spreadsheets that contain the initial concentrations as well as reaction rate parameters needed by PHT3D, stored as .xlsx files in './model_data/flopy_scripts/', to create the models.</li> <li>Note that the .xlsx files are used by PHT3D-FSP in the model scripts to generate relevant PHT3D input files (compare https://doi.org/10.5281/zenodo.7559750 for more details).</li> </ul> </li> <li>SEAWAT/PHT3D input files <ul> <li>Original SEAWAT and PHT3D input files, which were created with the corresponding model scripts previously (see step before).</li> <li>Input files are stored in './model_data/model_files/vXYZ/model_files/' for each model variant, where 'XYZ' is a wildcard for the model number.</li> <li>SEAWAT/PHT3D executables can directly run the model files files. Thus, the files don't need to be re-created via the previous step.</li> </ul> </li> <li>Model outputs <ul> <li>Model output data is stored as NumPy arrays in './model_data/model_files/vXYZ/npy_arrays/', where 'XYZ' is a wildcard for the model number.</li> <li>The script './model_data/flopy_scripts/template_output/pht3d_output_hpc_v006.py' was used to generate the output files.</li> <li>2-D species concentration arrays are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/species/', where 'XYZ' is a wildcard for the model number.</li> <li>Species min./max. concentration arrays are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/min_max/', where 'XYZ' is a wildcard for the model number.</li> <li>2-D water budget arrays (CH & WEL boundaries) are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/budgets/', where 'XYZ' is a wildcard for the model number.</li> <li>Model discretization information (ncol, nrow, nlay etc.) are stored in the subfolder './model_data/model_files/vXYZ/npy_arrays/discretization/', where 'XYZ' is a wildcard for the model number.</li> </ul> </li> <li>Figure files <ul> <li>Original figure files as well as the corresponding Python scripts to create the figures are stored in the subfolder'./figures'.</li> </ul> </li> </ul> <p>Numbering of the model variants is as follows:<br><br>v401 --> VAR-conservative<br>v402 --> VAR-OM<br>v403 --> VAR-C/I<br>v404 --> VAR-C/I/S<br>v405 --> VAR-C/I/P<br>v406 --> VAR-C/I/P/H<br>v407 --> VAR-C/I/P/V<br>v408 --> VAR-C/I/P-Co<br>v409 --> VAR-all<br>v410 --> VAR-all (no C)</p> </div> <div> </div> <div>Literature:</div> <div> </div> <div>Bakker, M., Post, V., Langevin, C.D., Hughes, J.D., White, J.T., Starn, J.J. and Fienen, M.N., 2016. Scripting MODFLOW model development using Python and FloPy. Groundwater, 54(5), pp.733-739. https://doi.org/10.1111/gwat.12413</div> <div> </div> <div>Seibert, S.L., Massmann, G., Meyer, R., Post, V.E.A., Greskowiak, J., 2024. Impact of mineral reactions and surface complexation on the transport of dissolved species in a subterranean estuary: Application of a comprehensive reactive transport modeling approach. Advances in Water Resources. https://doi.org/10.1016/j.advwatres.2024.104763</div> <div> </div> <div><strong>Contact one of the authors if you have further questions</strong>: Stephan L. Seibert (stephan.seibert@uol.de), Janek Greskowiak (janek.greskowiak@uol.de), Vincent E.A. Post (vincent@edinsi.nl), Rena Meyer (rena.meyer@uol.de) or Gudrun Massmann (gudrun.massmann@uol.de)</div>
Research data related to the article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems"
<p><strong>Research Data related to the article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems" by Seibert et al. (2024) published in <em>Earth's Future</em></strong></p> <p>Dear reader,</p> <p>reasearch data are provided for the research article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems" by Seibert et al. (2024) published in <em>Earth's Future</em>. The authors hope that the research data allows for a better understanding of the modeling workflow. Questions regarding the modeling approach etc. can be directed to the authors, see contact details below.</p> <p>The research data covers the following files:</p> <ul> <li>iMOD-Python (Visser and Bootsma, 2019) scripts to create the iMOD-WQ (Verkaik et al., 2021) input files for the n=566 model variants. Subfolders for each model variant and corresponding files are stored in the subfolder 'model_variants'. An overview regarding the set-up of the model variants is presented in the .xlsx spreadsheet 'model_variants_overview.xlsx' in the folder 'model_variants'.</li> <li>Base data files, used as input files to iMOD-WQ (Verkaik et al., 2021), stored in the subfolder 'imod_input'. However, in most cases no consent for re-distribution of these data sets exists, and they cannot be made freely available through this publication. Please, consider the corresponding meta-data files and/or get in touch with one of the authors for further information.</li> <li>Post-processed model output data, which was further used for model evaluation, stored in the subfolder 'model_output'.</li> <li>Figure files and the corresponding .py scripts, stored in the subfolder 'figures'.</li> </ul> <p>Meta-data files are provided with data files in the different subfolders for clarification.</p> <p>iMOD-WQ (Verkaik et al., 2021) input and .run-files were executed on the University Oldenburg High-Performance Cluster 'Rosa', funded by DFG through its Major Research Instrumentation Program, INST 184/225-1 FUGG, and the Ministry of Science and Culture (MWK) of the Lower Saxony State.</p> <p>Further information on the iMOD suite can be found here: https://deltares.github.io/iMOD-Documentation/</p> <p>The DFG is thanked for SALTSA project funding (DFG project number MA 3274/9-1) within the Priority Programme ‘Regional Sea Level Change and Society (SeaLevel)’. Research related to this article further benefited from funding of the projects WAKOS (BMBF; support code 01LR2003E) and the DFG research unit FOR 5094: The dynamic deep subsurface of high-energy beaches (DynaDeep).</p> <p>Literature:</p> <p>Verkaik, J., Hughes, J. D., van Walsum, P. E. V., Oude Essink, G. H. P., Lin, H. X., & Bierkens, M. F. P. (2021). Distributed memory parallel groundwater modeling for the Netherlands Hydrological Instrument. Environmental Modelling & Software, 143, p.105092.</p> <p>Visser, M., & Bootsma, H. (2019). iMOD-Python: Work with iMOD MODFLOW models in Python. Retrieved from https://imod.xyz/</p> <p>Seibert, S. L., Greskowiak, J., Oude Essink, G. H. P., & Massmann, G. (2024). Understanding climate change and anthropogenic impacts on the salinization of low‐lying coastal groundwater systems. Earth's Future, 12, e2024EF004737. https://doi.org/10.1029/2024EF004737<br><br><strong>Contact one of the authors if you have further questions</strong>: Stephan L. Seibert (stephan.seibert@uol.de), Janek Greskowiak (janek.greskowiak@uol.de), Gualbert H.P. Oude Essink (Gualbert.OudeEssink@deltares.nl) or Gudrun Massmann (gudrun.massmann@uol.de)</p>
Research integrity practices and environmental impact of research: Questionnaire of the survey conducted at two French institutions
<p>This dataset is a questionnaire designed to evaluate researchers' understanding, application, and perception of responsible research principles, and to identify strengths and weaknesses in their implementation.<br>It contains about thirty questions and covers various and sometimes complex topics: responsible and inclusive research practices, public trust in science, climate crisis, reduction of GHG emissions caused by research activities, research integrity, researchers' engagement in the public sphere.</p> <p>It is divided into 6 sections:<br>- Relations between science and society <br>- Conducting research in a changing world <br>- Ethics and research integrity <br>- Engaged research <br>- Science communication </p> <p>To enable comparative analysis, it incorporates questions from other surveys which are cited as references.</p>
The impact of the COVID-19 pandemic on scientific research in the life sciences
<p>This repository contains the code to replicate the analysis for the paper: "The impact of the COVID-19 pandemic on scientific research in the fife sciences." Detailed instructions on how to run the code are available in the README.md</p>
WRF output for the Geophysical Research Letters publication "Potential Impacts of Radio Occultation Data Assimilation on Forecast Skill of Tropical Cyclone Formation in the Western North Pacific"
<p>This dataset is the Weather Research and Forecasting (WRF) model output for the <em>Geophysical Research Letters</em> publication entitled "Potential Impacts of Radio Occultation Data Assimilation on Forecast Skill of Tropical Cyclone Formation in the Western North Pacific". The dataset includes the azimuthal-averaged parameters with 0.2°resolution, three-day forecast, and two experiments for all cases analyzed in the publication. Due to the data size, only the variables used in the figures are uploaded (i.e., relative humidity, relative vorticity, temperature, and water vapor mixing ratio). Detailed information, composite calculation, and model settings can be found in the publication.</p>
Global patterns of taxonomic uncertainty and its impacts on biodiversity research
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