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113 results for “Reuse”
Reuse streams at UK waste facilities (HWRCs)
<div> <p>The UK recycles thousands of working and repairable devices every week at its waste facilities. But fixing or reusing them instead could reduce waste, lower emissions and save households money. </p> <p>Through a citizen science project, we investigated what reuse streams are currently available at household waste and recycling centres (HWRCs) and the challenges of making reuse more widely available.</p> <p>This dataset contains records for all HWRCs across the UK and details of any publicly-communicated reuse streams at each site. It is accompanied by a narrative report. Last updated October 2024.</p> </div>
Dataset Reuse Indicators Datasets
<p>This dataset contains two files. </p> <p>1) A python pickle file (github_dataset.zip) that contains Github repositories with datasets. Specifically, using Google’s public dataset copy of Github and the BigQuery service to build a list of repositories that have a CSV or XLSX or XLS file. We then used the GitHub API to collect nformation about each repository in this list. The resulting dataset consists of 87936 repositories that contain at least a CSV, XLSX or XLS file, alongside with information about their features (e.g. number of open and closed issues and license) from GitHub. This corpus had more than two million data files. We then excluded those files withless then ten rows, which was the case for 65537 repositories with a total of 1,467,240 data files.</p> <p>2) A python pickle file (processed_dataset.zip) containing the feature information necessary to train a machine learning model to predict reuse on these Github datasets</p> <p>Source code can be found at: <a href="https://github.com/laurakoesten/Dataset-Reuse-Indicators">https://github.com/laurakoesten/Dataset-Reuse-Indicators</a></p> <p>For a full description of the content see:</p> <p>Koesten, Laura and Vougiouklis, Pavlos and Simperl, Elena and Groth, Paul, Dataset Reuse: Translating Principles to Practice. Available at SSRN: <a href="https://ssrn.com/abstract=3589836">https://ssrn.com/abstract=3589836</a> or <a href="https://dx.doi.org/10.2139/ssrn.3589836">http://dx.doi.org/10.2139/ssrn.3589836</a></p>
Cultural heritage adaptive reuse in Salerno: challenges and solutions. Dataset
<p>Dataset analysed in Pintossi, N., Ikiz Kaya, D., Pereira Roders, A. (2023). Cultural heritage adaptive reuse in Salerno: Challenges and solutions. City, Culture and Society, 100505. https://doi.org/10.1016/j.ccs.2023.100505</p> <ul> <li>Date of data collection: 27/11/2018</li> <li>Geographic location of data collection: Salerno, Italy. The venue of the data collection is <em>Salone dei marmi, Palazzo di Città</em>, via Roma, 84121 Salerno, Italy </li> <li>Activity of data collection: Historic Urban Landscape workshop 2 - Salerno. Held in Salerno, Italy, on 26-27/11/2018</li> <li>Aim of data collection: Multi-scale, participatory identification of challenges entailed in the adaptive reuse of cultural heritage and solutions </li> <li>Methods for collection/generation of data: see the methodology section in Pintossi, N., Ikiz Kaya, D., Pereira Roders, A. (2023). Cultural heritage adaptive reuse in Salerno: Challenges and solutions. City, Culture and Society, 100505. https://doi.org/10.1016/j.ccs.2023.100505</li> <li>Researchers facilitating roundtable discussion and writing down paper version of data: Marco Acri, Gaia Daldanise, Gamze Dane, Cristina Garzillo, Antonia Gravagnuolo, Lu Lu, Nadia Pintossi, and Ruba Saleh</li> <li>Researcher translating to English, transcribing data in the digital tabular dataset, and cleaning the data: Nadia Pintossi</li> <li>Original language of the data: English, Italian, and mix of English and Italian</li> </ul>
Credit for software creators: An adapted illustration from The Turing Way: Shared under CC-BY 4.0 for reuse
<p>Illustration adapted from <strong>The Turing Way Community, & Scriberia. (2023). Illustrations from The Turing Way: Shared under CC-BY 4.0 for reuse. Zenodo. <a href="https://doi.org/10.5281/zenodo.8169292">https://doi.org/10.5281/zenodo.8169292</a></strong>.</p> <p>The illustration has been adapted by Stephan Druskat (subsumed as co-author in <em>The Turing Way Community</em>):</p> <ul> <li>The speech bubble has been changed to read "More credit" instead of "More credit<strong>s</strong>". (One character removed and alignment adapted.)</li> <li>The inscription on the purse has been changed to read "Credit" instead of "Credit<strong>s</strong>". (One character removed and alignment adapted.)</li> </ul>
Supplementary dataset for "Potential of Wastewater Reuse to Alleviate Water Scarcity under Future Warming Scenarios"
<p>The folder contains water gap data relative to the paper:<br>Kahn, M., Sangiorgio, M., and Rosa, L. (2025) Potential of wastewater reuse to alleviate water scarcity under future warming scenarios. Environmental Research Letters, 20, 034012<br>https://doi.org/10.1088/1748-9326/adb31d</p> <p>All water gaps data are in km3/yr.</p> <p><br>Gridded data(NetCDF at 0.5°)</p> <ul> <li>baseline (2001-2010) <ul> <li>Water_gap_baseline_no_wastewater_reuse: Water gap under baseline climate scenario with no wastewater reuse.</li> <li>Water_gap_baseline_treated_wastewater_reuse: Water gap under baseline climate scenario with treated wastewater reuse.</li> <li>Water_gap_baseline_full_wastewater_reuse: Water gap under baseline climate scenario with full wastewater reuse.</li> </ul> </li> </ul> <p> </p> <ul> <li>1.5°C warming <ul> <li>Water_gap_15_no_wastewater_reuse: Water gap under 1.5°C warming scenario with no wastewater reuse.</li> <li>Water_gap_15_treated_wastewater_reuse: Water gap under 1.5°C warming scenario with treated wastewater reuse.</li> <li>Water_gap_15_full_wastewater_reuse: Water gap under 1.5°C warming scenario with full wastewater reuse.</li> </ul> </li> </ul> <p> </p> <ul> <li>3°C warming (5 models + average) <ul> <li>Water_gap_3_no_wastewater_reuse: Water gap under 3°C warming scenario with no wastewater reuse.</li> <li>Water_gap_3_treated_wastewater_reuse: Water gap under 3°C warming scenario with treated wastewater reuse.</li> <li>Water_gap_3_full_wastewater_reuse: Water gap under 3°C warming scenario with full wastewater reuse.</li> </ul> </li> </ul> <p> </p> <p>Aggregated data (.xlsx)</p> <ul> <li>country_level_water_gaps.xlsx: Water gap aggregated by country for all the considered scenarios.</li> <li>city_water_gaps.xlsx: 0.5° pixels with populations greater than 5,000,000 and non-zero water gaps corresponding to urban center.</li> <li>seasonal_variations.xlsx: Monthly water gaps of the 5 most water scarce countries.</li> </ul> <p><br>Note: the global water gap obtained by summing all the countries is not completely equivalent to the sum of all the pixels because some pixels' center is outside the polygon of the corresponding country.</p>
[ITU AI/ML Challenge 2021] Dataset IEEE 802.11ax Spatial Reuse
<p>This dataset has been created for the problem statement ITU-ML5G-PS-004 of the ITU AI/ML Challenge (2021 edition). More information can be found here: <a href="https://challenge.aiforgood.itu.int/">https://challenge.aiforgood.itu.int/</a> and <a href="https://www.upf.edu/web/wnrg/2021-edition">https://www.upf.edu/web/wnrg/2021-edition</a>. </p> <p>The dataset contains the information of 3.000 IEEE 802.11ax deployments (divided into two different scenarios) at which the Basic Service Set (BSS) of interest applies different possible OBSS/PD thresholds in the context of the Spatial Reuse (SR) operation. In total, 21 OBSS/PD values are considered for each deployment, and some of the deployments include data from different STA locations. The provided files are expected to be used for training Machine Learning (ML) and Federated Learning (FL) algorithms.</p> <p>More specifically, the dataset is divided as follows:</p> <ul> <li><strong>Scenario 1:</strong> 1,000 different deployments with 2-6 APs and 1 STA per AP. A minimum distance limitation is applied, so that each AP different from AP_A is located at a minimum distance of 10 meters from that one. Each deployment is simulated for each of the 21 possible OBSS/PD thresholds in 11ax. Two files are considered: <ol> <li><a href="https://zenodo.org/api/files/a153e748-7ff5-419d-a7a0-ab7d962d9ccb/output_11ax_sr_simulations_sce1.txt">output_11ax_sr_simulations_sce1.txt</a>: contains the output generated by the simulator for the deployments in Scenario 1.</li> <li><a href="https://zenodo.org/api/files/a153e748-7ff5-419d-a7a0-ab7d962d9ccb/simulator_input_files_sce1.zip">simulator_input_files_sce1.zip</a>: contains the input files used by the simulator to simulate the deployments in Scenario 1.</li> </ol> </li> <li><strong>Scenario 2: </strong>1,000 different deployments with 2-6 APs and 1-4 STAs per AP. No distance limitation is applied. Each deployment is simulated for each of the 21 possible OBSS/PD thresholds in 11ax. Two files are considered: <ol> <li><a href="https://zenodo.org/api/files/a153e748-7ff5-419d-a7a0-ab7d962d9ccb/output_11ax_sr_simulations_sce2.txt">output_11ax_sr_simulations_sce2.txt</a>: contains the output generated by the simulator for the deployments in Scenario 2.</li> <li><a href="https://zenodo.org/api/files/a153e748-7ff5-419d-a7a0-ab7d962d9ccb/simulator_input_files_sce2.zip">simulator_input_files_sce2.zip</a>: contains the input files used by the simulator to simulate the deployments in Scenario 2.</li> </ol> </li> <li><strong>Scenario 3: </strong>1,000 different deployments with 2-6 APs and 1-4 STAs per AP. No distance limitation is applied. Each deployment is simulated for each of the 21 possible OBSS/PD thresholds in 11ax, and for up to 20 different locations of different STAs of the BSS of interest ("BSS_A"). Two files are considered: <ol> <li><a href="https://zenodo.org/api/files/8057ea42-dd8c-4a02-9fe2-0e3bde8b3db0/output_11ax_sr_simulations_sce3.txt">output_11ax_sr_simulations_sce3.txt</a>: contains the output generated by the simulator for the deployments in Scenario 3.</li> <li><a href="https://zenodo.org/api/files/8057ea42-dd8c-4a02-9fe2-0e3bde8b3db0/simulator_input_files_sce3.zip">simulator_input_files_sce3.zip</a>: contains the input files used by the simulator to simulate the deployments in Scenario 3.</li> </ol> </li> <li><strong>Test:</strong> 1,000 different deployments with 2-6 APs and 1-4 STAs per AP. No distance limitation is applied. A random OBSS/PD threshold is applied. Two files are considered: <ol> <li><a href="https://zenodo.org/api/files/22d1265c-0dea-4bf8-a223-e61c45deb076/output_11ax_sr_simulations_test.txt?versionId=91ccbe70-a821-4601-8db7-1763883dc984">output_11ax_sr_simulations_test.txt</a>: contains the output generated by the simulator for the evaluation deployments. The label (throughput) has been replaced with "0s".</li> <li><a href="https://zenodo.org/api/files/8057ea42-dd8c-4a02-9fe2-0e3bde8b3db0/simulator_input_files_test.zip?versionId=b1581c0b-419c-422b-bb2d-4dbf77f05dd2">simulator_input_files_test.zip</a>: contains the input files used by the simulator to simulate the evaluation deployments.</li> </ol> </li> </ul>
Improving access to and reuse of research results, publications and data for scientific purposes - Stakeholders' consultations results
<p>The data sets were created via data collection effort for the Horizon Europe-funded "study to evaluate the effects of the EU copyright framework on research and the effects of potential interventions and to identify and present relevant provisions for research in EU data and digital legislation, with a focus on rights and obligations". The study was contacted by DG RTD. </p> <p>This research project supports Action 2 objectives of the European Research Area (ERA) Policy Agenda 2022-2024, which aims to propose an EU legislative and regulatory framework for copyright and data that is fit for research. The report provides a comprehensive analysis of barriers to the access and reuse of publicly funded research, including scientific publications and data. It assesses existing EU copyright legislation and EU data and digital legislation. It also assesses regulatory frameworks and national initiatives and identifies potential areas for improvement.</p> <p>Using a methodological, evidence-based approach (including the survey results posted in this repository), the study presents possible legislative and non-legislative measures to improve the current EU copyright and data framework and align it with the needs of scientific research and open research data principles. </p> <p>The data sets include the raw data of the three surveys (survey 1 targeted at researchers, survey 2 targeted at research-performing organisations, and survey 3 targeted at publishers). All surveys have two major parts: one concerning copyright legislation and another concerning data and digital legislation. In addition, we provide interview notes, they are also organised into two parts: one concerning copyright legislation and another concerning data and digital legislation. </p> <p>The data collection effort was partially supported by our colleagues from the Institute for Information Law (IVIR) and KU Leuven CiTIP. </p>
Examining LGBTQ+-related Concepts in the Semantic Web: Link Discovery, Concept Drift, Ambiguity, and Multilingual Information Reuse
<div> <h1>Examining LGBTQ+-related Concepts in the Semantic Web</h1> </div> <div> <h2>Introduction</h2> </div> <p>Welcome to the project. We study the links between LGBTQ+ ontologies and structured vocabularies. More specifically, we focus on GSSO, Homosaurus, QLIT, and Wikidata. The code is free for use with the license GPL 3,0. You can resue/extend the code for free as long as you give credits to us in your publication/data. Citation information will be added after the corresponding paper gets accepted. The paper is under submission and will be included soon. </p> <p>If you would like to extend this work, you may want to contact the experts in the acknowledgement before releasing your data/code about legal and ethical issues. The DOI for this version is 10.5281/zenodo.12684870. The latest code can be found at https://github.com/Multilingual-LGBTQIA-Vocabularies/Examing_LGBTQ_Concepts. </p> <p>To reproduce the results or extend our work, you need to take the following steps.</p> <div> <h2>Step 1: Preparing the data</h2> </div> <p>In this project, the following datasets were used:</p> <ul> <li>QLIT: version 1.0</li> <li>Homosaurus: version 3.5 and version 2.3</li> <li>Wikidata: retrieved from the SPARQL Endpoint (<a href="https://query.wikidata.org/sparql" rel="nofollow">https://query.wikidata.org/sparql</a>) and processed between 5th May and 8th May, 2024.</li> <li>GSSO: we used gsso.owl (version 2.0.10) obtained from its Github (<a href="https://github.com/Superraptor/GSSO">https://github.com/Superraptor/GSSO</a>).</li> <li>LCSH was obtained from the official website: <a href="https://id.loc.gov/authorities/subjects.html" rel="nofollow">https://id.loc.gov/authorities/subjects.html</a> on 9th May, 2024. The LCSH data was converted to its HDT format.</li> </ul> <p>Please put the corresponding files in the following folders (and change its names where necessary) to make sure that the Python scripts can find your code.</p> <ul> <li>./data/GSSO/gsso.owl</li> <li>./data/Homosaurus/v2.ttl and ./data/Homosaurus/v3.ttl</li> <li>./data/LCSH/lcsh.hdt (we used its HDT format for fast query and analysis). The original file is also attached: subjects.skosrdf.nt.</li> <li>./data/QLIT/Qlit-v1.ttl</li> </ul> <p>The case of Wikidata is more complicated. The following scripts were used for the retrival of data. These scripts are all in the folder ./data/wikidata/</p> <ul> <li>We used the Wikidata SPARQL endpoint: <a href="https://query.wikidata.org/" rel="nofollow">https://query.wikidata.org/</a></li> </ul> <p>The following relations from Wikidata were used while extracting triples.</p> <ul> <li>Wikidata - GSSO: <a href="http://www.wikidata.org/prop/direct/P9827" rel="nofollow">http://www.wikidata.org/prop/direct/P9827</a></li> <li>Wikidata - Homosaurus 2: <a href="http://www.wikidata.org/prop/direct/P6417" rel="nofollow">http://www.wikidata.org/prop/direct/P6417</a></li> <li>Wikidata - Homosaurus 3: <a href="http://www.wikidata.org/prop/direct/P10192" rel="nofollow">http://www.wikidata.org/prop/direct/P10192</a></li> <li>Wikidata - LCSH: <a href="http://www.wikidata.org/prop/direct/P244" rel="nofollow">http://www.wikidata.org/prop/direct/P244</a></li> </ul> <p>The generated files are:</p> <ul> <li>'wikidata-homosaurus-v2-links.nt'</li> <li>'wikidata-homosaurus-v3-links.nt'</li> <li>'wikidata-gsso-links.nt'</li> <li>'wikidata-qlit-links.nt'</li> <li>'wikidata-lcsh-links-all.nt'</li> </ul> <p>Please note that the case of Wikdiata-LCSH is more complicated: there are so many links that are nothing to do with the entities in our scope. We restrict it to only entities in the scope of this paper. See below for more details.</p> <p>You can find all the scripts in the corresponding folder in the data folder.</p> <p>All the SPARQL queries used can be found in the folder ./SPARQL/</p> <p>Note! For GSSO, the following two mistakes were corrected while preprocessing:</p> <ul> <li><a href="https://www.wikidata.org/wiki/Q1823134" rel="nofollow">https://www.wikidata.org/wiki/Q1823134</a> should not be used as a relation. We have replaced it with <a href="http://www.wikidata.org/prop/direct/P244" rel="nofollow">http://www.wikidata.org/prop/direct/P244</a>.</li> <li>Instead of referring to the page, we refer to the entity. We use <a href="http://www.wikidata.org/entity/" rel="nofollow">http://www.wikidata.org/entity/</a>* instead of <a href="https://www.wikidata.org/wiki/" rel="nofollow">https://www.wikidata.org/wiki/</a>*</li> </ul> <p>The redirection test was conducted on 30th April, 2024, between 6PM and 8PM. The files can be found in the folder of ./data/Homosaurus/redirect/.</p> <div> <h2>Integrating the data</h2> </div> <p>In the folder ./integrated_data/, you can find all the scripts related to the integrated data. Unfortunately, due to the CC-BY-NC-ND license of GSSO and Homosaurus, the integrated data will not be made available. But you can generate it with the instructions above and by using the following scripts.</p> <p>The script ./integrated_data/integrate.py takes advantage of the data generated. It first integrates a list of files of links. Then we go through the links between Wikidata and LCSH. Only those that are in the scope of the study are included.</p> <ul> <li>If your steps are correct and using the same version as we did, you should be able to get four files:</li> <li>a) the integrated file as integrated.nt</li> <li>b) the links that are relevant for this study: wikidata-lcsh-links-selected.nt.</li> <li>c) a plot of the distribution of the size of WCCs</li> <li>d) a mapping of entities and their corresponding ID of WCCs.</li> </ul> <div> <h2>Weakly Connected Components</h2> </div> <p>The weakly connected components (WCCs) were computed for the following three purposes:</p> <p>a) Discovering missing links. See the section below for details.</p> <p>b) The WCCs can be used for manual examination. These are entities that form clusters about related concepts. The intuition is that the larger they are, the more likely there is concept drift/change, ambiguity, and mistakes.</p> <p>c) Multilingual information reuse. Smaller WCCs with exactly one entity from each dataset (e.g. Homosaurus and Wikidata) can then be used to suggest labels for the one with fewer labels for some given languages. See below for more details.</p> <p>As mentioned above, the distribution has been plotted. You can find this plot here: ./integrated_data/frequency.png</p> <p>In the folder ./integrated_data/weakly_connected_components/, you can find all the WCCs and their links.</p> <p>Two examples were given in the folder. The largest WCC about sex, gender, fucking, etc. The other is about BDSM and fetish.</p> <div> <h2>Discovering missing and outdated links</h2> </div> <p>Taking advantage of WCCs, we can further find missing and outdated links. The scripts are in the folder ./discover_missing_links.</p> <p>Three examples were given. The first two is about discovering missing links. The last one is about finding outdated links.</p> <ul> <li> <p>The script ./discover_missing_links/discover_H3_LCSH.py and ./discover_missing_links/discover_QLIT_LCSH.py are scripts that outputs links that could be missing in Homosaurus and QLIT respectively. This was computed by looking at the WCCs. If two entities are both involved in the same WCC, there could be a link between them. The csv files in the same folder are the corresponding links found.</p> </li> <li> <p>The script ./discover_missing_links/find_qlit_outdated_links/ is used to discover the outdated links between QLIT and Homosaurus v3. There was only one link found.</p> </li> <li> <p>The 105 potentially missing links were taken for further review by Swedish-speaking experts from the QLIT team, which showed that 78 (72.38%) suggested links should be included: 38 (36.19%) can be included using skos:exactMatch and another 38 (36.19%) using skos:closeMatch. 28 (26.67%) suggested links are incorrect. The manual annotation are included in the file ./discover_missing_links/Annotated_found_new_links_qlit-lcsh.xlsx.</p> </li> </ul> <div> <h2>Multilingual Information Reuse</h2> </div> <p>You can find two attempts in the folders about the use of GSSO and Wikidata for Homosaurus respectively.</p> <ul> <li>./WCC-based-gsso-multilingual_info_reuse/</li> <li>./WCC-based-wikidata-multilingual_info_reuse/</li> </ul> <p>Additionally, we provide also some code for the reuse of Wikidata multilingual info for QLIT. It's in the folder</p> <ul> <li>./WCC-based-QLIT-info-reuse-from-Wikidata/</li> </ul> <p>They follow very similar steps:</p> <ol> <li> <p>Compute the one-to-one mapping using the WCCs. The script is named compute-one-to-one-mapping.py</p> </li> <li> <p>Extract the multilingual labels from sources. The corresponding file is extract_multilingual_labels_from_one_to_one_mappings.py</p> </li> <li> <p>Provide the extracted multilingual as suggestions for targeting entities. The name of the corresponding files are like "*suggesting-labels.py", where the * is replaced by the actual source/target.</p> </li> </ol> <p>For GSSO, we use the following relations:</p> <ul> <li><a href="http://www.w3.org/2000/01/rdf-schema#label" rel="nofollow">http://www.w3.org/2000/01/rdf-schema#label</a></li> <li><a href="http://www.geneontology.org/formats/oboInOwl#hasRelatedSynonym" rel="nofollow">http://www.geneontology.org/formats/oboInOwl#hasRelatedSynonym</a></li> <li><a href="http://www.geneontology.org/formats/oboInOwl#hasSynonym" rel="nofollow">http://www.geneontology.org/formats/oboInOwl#hasSynonym</a></li> <li><a href="http://www.geneontology.org/formats/oboInOwl#hasExactSynonym" rel="nofollow">http://www.geneontology.org/formats/oboInOwl#hasExactSynonym</a></li> <li><a href="http://purl.org/dc/terms/replaces" rel="nofollow">http://purl.org/dc/terms/replaces</a></li> <li><a href="https://www.wikidata.org/wiki/Property:P5191" rel="nofollow">https://www.wikidata.org/wiki/Property:P5191</a></li> <li><a href="https://www.wikidata.org/wiki/Property:P1813" rel="nofollow">https://www.wikidata.org/wiki/Property:P1813</a></li> <li><a href="https://schema.org/alternateName" rel="nofollow">https://schema.org/alternateName</a></li> <li><a href="http://www.w3.org/2002/07/owl#annotatedTarget" rel="nofollow">http://www.w3.org/2002/07/owl#annotatedTarget</a></li> </ul> <p>Additioinally, we found the relation to be studied in the future: <a href="http://www.geneontology.org/formats/oboInOwl#hasNarrowSynonym" rel="nofollow">http://www.geneontology.org/formats/oboInOwl#hasNarrowSynonym</a></p> <p>For Wikidata, there are only two:</p> <ul> <li><a href="http://www.w3.org/2000/01/rdf-schema#label" rel="nofollow">http://www.w3.org/2000/01/rdf-schema#label</a></li> <li><a href="http://www.w3.org/2004/02/skos/core#altLabel" rel="nofollow">http://www.w3.org/2004/02/skos/core#altLabel</a></li> </ul> <div> <h2>Additional analysis</h2> </div> <p>Additionally, we perform an analysis using only redirection and replacement for GSSO and Homosaurus. The scripts are in the folder ./additional_test_gsso_multilingual_info_reuse. We consider also Homosaurus v2. This additional analysis shows the following:</p> <ul> <li> <p>For the Turkish language, in total there are 103 triples about labels about 23 entities. The average suggested labels per entity is 3.0.</p> </li> <li> <p>For the Spanish language, in total there are 205 triples about labels about 43 entities. The average suggested labels per entity is 2.12.</p> </li> <li> <p>For the French language, in total there are 277 triples about labels about 47 entities. The average suggested labels per entity is 2.19.</p> </li> <li> <p>For the Danish language, in total there are 115 triples about labels about 47 entities. The average suggested labels per entity is 2.70.</p> </li> </ul> <p>Some analysis about the replacement relations of Homosaurus is in the folder ./data/Homosaurus/replace_relations_homosaurus/.</p> <p>Finally, some additional analysis is included in the folder ./analysis_integrated_graph. Currently, there is only one that is about outdated entities in Homosaurus v3. Some more analysis will be added in the future.</p> <div> <h2>Acknowledgement</h2> </div> <p>The authors appreciate the help of the following researchers:</p> <ul> <li>Siska Humlesjö, QLIT, Göteborgs Universitet (<a href="mailto:siska.humlesjo@lir.gu.se">siska.humlesjo@lir.gu.se</a>)</li> <li>Olov Kriström, former member of QLIT</li> <li>Jack van der Wel, IHLIA (<a href="mailto:jack@ihlia.nl">jack@ihlia.nl</a>)</li> <li>Clair Kronk, GSSO (<a href="mailto:clair.kronk@mountsinai.org">clair.kronk@mountsinai.org</a>)</li> </ul> <div> <p>If you would like to extend this work, you may want to contact them before releasing your data/code about legal and ethical issues.</p> <h2>Contact</h2> </div> <ul> <li>Shuai Wang, Vrije Universiteit Amsterdam (<a href="mailto:shuai.wang@vu.nl">shuai.wang@vu.nl</a>)</li> <li>Maria Adamidou, Vrije Universiteit Amsterdam (<a href="mailto:m.adamidou@student.vu.nl">m.adamidou@student.vu.nl</a>)</li> </ul> <p> </p> <p>Thank you very much for your interest in our project!</p>
[Dataset] Software Process Line as an Approach to Support Software Process Reuse: a Systematic Literature Review
<p>Dataset of a Systematic Literature Review on Software Process Line as an Approach to Support Software Process Reuse</p> <p>Please, read README.txt file before go through dataset.</p>
Webis Wikipedia Text Reuse Corpus 2018 (Webis-Wikipedia-Text-Reuse-18)
<p>The Wikipedia Text Reuse Corpus 2018 (Webis-Wikipedia-Text-Reuse-18) containing text reuse cases extracted from within Wikipedia and in between Wikipedia and a sample of the Common Crawl.</p> <p>The corpus has following structure:</p> <ul> <li>wikipedia.jsonl.bz2: Each line, representing a Wikipedia article, contains a json array of article_id, article_title, and article_body</li> <li>within-wikipedia-tr-01.jsonl.bz2: Each line, representing a text reuse case, contains a json array of s_id (source article id), t_id (target article id), s_text (source text), t_text (target text)</li> <li>within-wikipedia-tr-02.jsonl.bz2: Each line, representing a text reuse case, contains a json array of s_id (source article id), t_id (target article id), s_text (source text), t_text (target text)</li> <li>preprocessed-web-sample.jsonl.xz: Each line, representing a web page, contains a json object of d_id, d_url, and content</li> <li>without-wikipedia-tr.jsonl.bz2: Each line, representing a text reuse case, contains a json array of s_id (Wikipedia article id), d_id (web page id), s_text (article text), d_content (web page content)</li> </ul> <p>The datasets were extracted in the work by Alshomary et al. 2018 that aimed to study the text reuse phenomena related to Wikipedia at scale. A pipeline for large scale text reuse extraction was developed and used on Wikipedia and the CommonCrawl.</p>
Challenges of cultural heritage adaptive reuse: a stakeholders-based comparative study in three European cities. Dataset
<p>Dataset analysed in Pintossi, N., Ikiz Kaya, D., van Wesemael, P. J. V., & Pereira Roders, A. R. (2023). Challenges of cultural heritage adaptive reuse: A stakeholders-based comparative study in three European cities. Habitat International, 136, [102807]. https://doi.org/10.1016/j.habitatint.2023.102807.</p> <ul> <li>Date of data collection: a) 31/05/2018, b) 27/11/2018, and c) 28/03/2019</li> <li>Geographic location of data collection: a) Amsterdam, The Netherlands. The venue of the data collection was Pakhuis de Zwijger, Piet Heinkade 179, 1019 HC, Amsterdam, The Netherlands; b) Salerno, Italy. The venue of the data collection was Salone dei marmi, Palazzo di Città, via Roma, 84121 Salerno, Italy; and c) Rijeka, Croatia. The venue of the data collection is RiHub, Ul. Ivana Grohovca 1/a, 51000, Rijeka, Croatia.</li> <li>Activity of data collection: a) Historic Urban Landscape workshop 1 - Amsterdam. Held in Amsterdam, the Nethelands, on 30-31/05/2018; b) Historic Urban Landscape workshop 2 - Salerno. Held in Salerno, Italy, on 26-27/11/2018; and c) Historic Urban Landscape workshop 3 - Rijeka. Held in Rijeka, Croatia, on 28/03/2019.</li> <li>Aim of data collection: Multi-scale, participatory identification of challenges entailed in the adaptive reuse of cultural heritage and solutions to overcome these challenges.</li> <li>Methods for collection/generation of data: See the methodology section in a) Pintossi, N., Ikiz Kaya, D., & Pereira Roders, A. (2021). Identifying Challenges and Solutions in Cultural Heritage Adaptive Reuse through the Historic Urban Landscape Approach in Amsterdam. Sustainability, 13(10), 5547. https://doi.org/10.3390/su13105547; b) Pintossi, N., Ikiz Kaya, D., Pereira Roders, A. (2023). Cultural heritage adaptive reuse in Salerno: Challenges and solutions. City, Culture and Society, 33, 100505. https://doi.org/10.1016/j.ccs.2023.100505; and c) Pintossi, N., Ikiz Kaya, D., & Pereira Roders, A. (2021). Assessing Cultural Heritage Adaptive Reuse Practices: Multi-Scale Challenges and Solutions in Rijeka. Sustainability, 13(7), 3603. https://doi.org/10.3390/su13073603.</li> <li>Researchers facilitating roundtable discussion and writing down paper version of data: a) Gamze Dane, Antonia Gravagnuolo, Paloma Guzman Molina, Ana Pereira Roders, Nadia Pintossi, and Julia Rey-Perez; b) Marco Acri, Gaia Daldanise, Gamze Dane, Cristina Garzillo, Antonia Gravagnuolo, Lu Lu, Nadia Pintossi, and Ruba Saleh; and c) Marco Acri, Martina Bosone, Deniz Ikiz Kaya, Silvia Iodice, Lu Lu, and Nadia Pintossi.</li> <li>Language of the data: English.</li> <li>References: a) Pintossi, Nadia. (2021). Assessing cultural heritage adaptive reuse practices: multi-scale challenges and solutions in Rijeka. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4518743; b) Pintossi, Nadia. (2020). Identifying challenges and solutions in cultural heritage adaptive reuse through the Historic Urban Landscape approach in Amsterdam. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4250495; and c) Pintossi, Nadia. (2023). Cultural heritage adaptive reuse in Salerno: challenges and solutions. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3925602</li> </ul> <p><br> </p>
Artifact for the ESEC/FSE 2020 Paper: An Empirical Analysis of the Costs of Clone- and Platform-Oriented Software Reuse
<p>This dataset comprises the supplementary material for the paper "An Empirical Analysis of the Costs of Clone- and Platform-Oriented Software Reuse" by Jacob Krüger and Thorsten Berger, accepted at ESEC/FSE 2020.</p> <p>The dataset comprises:</p> <ul> <li>bibFilesManualSearch: The bib files for all venues analyzed, as provided by DBLP (cf. Section 2.4)</li> <li>dataFromPapers: The pdf file documents all included studies and the data extracted from these (cf. Section 2.4, 3.2, and 3.3)</li> <li>interviewGuide: The guide/questions for our semi-structured intreviews in the cost assessment phase (cf. Section 2.3)</li> <li>anonymizedInterviewSummary: The anonymized and summarized data from the cost-assessment interviews (cf. Section 3.2 and 3.3)</li> <li>R: Our R script for creating our figures and the corresponding csv files</li> </ul> <p> </p>
The OpenITI Self-reuse Data
<p>This data set pertains to the OpenITI <a href="https://zenodo.org/records/10007820">2023.1.8 release</a> of the corpus and the corresponding <a href="https://zenodo.org/records/11501559">passim run</a>. The data will be analysed by Sarah Bowen Savant in a forthcoming monograph under contract with Edinburgh University Press.</p>
Replication Package of "Exploiting Vision-Language Models in GUI Reuse"
<p>This replication package is for the paper entitled "Exploiting Vision-Language Models in GUI Reuse". The authors remain anonymous for double-blind review purposes. The package contains six files. If the paper is accepted, then the authors will move the replication package to a public repository hosted by an institution.</p>
Dataset created in the context of the project "Stable methodologies to evaluate and measure quality, interoperability, blockchain and reuse of open data in the agricultural field"
<p>The project "Stable methodologies to evaluate and measure quality, interoperability, blockchain and reuse of open data in the agricultural field", whose website is https://datause.es/, is a project funded by the Ministry of Science and Innovation - State Research Agency, with reference PID2019-105708RB-C22.</p> <p>Within the framework of the project, a bibliographic search is carried out in all thematic categories of the Web of Science (WoS) related to agriculture and related areas. The search equation included the following categories:</p> <p><strong>WC </strong>= (FOOD SCIENCE TECHNOLOGY OR PLANT SCIENCES OR FORESTRY OR AGRICULTURAL ENGINEERING OR AGRONOMY OR HORTICULTURE OR AGRICULTURE DAIRY ANIMAL SCIENCE OR AGRICULTURE MULTIDISCIPLINARY OR AGRICULTURAL ECONOMICS POLICY) </p> <p>This data set shows the distribution of journals and the quartile they occupy in each of the thematic categories in 2019, with the aim of serving researchers in this area and for future data mining.</p>
Dataset for An Empirical Study of Hackathon Code Creation and Reuse
<p>This dataset is corresponds to our extended analysis done for “The Secret Life of Hackathon Code Where does it come from and where does it go?” (<a href="https://doi.org/10.1109/MSR52588.2021.00020">https://doi.org/10.1109/MSR52588.2021.00020</a>, pre-print at: <a href="https://arxiv.org/abs/2103.01145">https://arxiv.org/abs/2103.01145</a>) and “Tracking Hackathon Code Creation and Reuse” (<a href="https://doi.org/10.1109/MSR52588.2021.00085">https://doi.org/10.1109/MSR52588.2021.00085</a>, pre-print at: <a href="https://arxiv.org/pdf/2103.10167">https://arxiv.org/pdf/2103.10167</a>). The replication package including the scripts used for generating this dataset from the “World of Code” (https://worldofcode.org/) dataset is available in Github link <a href="https://github.com/woc-hack/track_hack">https://github.com/woc-hack/track_hack</a>.</p> <p>The dataset contains the blob hashes used in the scope of the analysis and the analysis outcome.</p> <p>The columns are as following</p> <ul> <li><strong>DevpostID</strong>: Devpost identification for the hackathon project and it can be used to get the URL for the devpost.com website. Example DevpostID -q9nd5 can be translated to https://devpost.com/software/-q9nd5</li> <li><strong>ProjectID</strong>: The Github project name</li> <li><strong>HackathonEndDate</strong>: Hackathon event end date</li> <li><strong>BlobHash</strong>: The blob hash used in the analysis</li> <li><strong>BeforeHackathon-DuringHackathon-AfterHackathon</strong>: This column represents if the blob was first introduced before/during/after the hackathon (1: before, 2: during, 3: after)</li> <li><strong>SameAuthor-Contributor-OtherAuthor</strong>: This column represents if the blob was first created by someone in the hackathon team, or someone who was a contributor to a project in which one of the members of the hackathon project contributed to as well (contributor), or someone else outside of the hackathon team (1: Author is a hackathon team member, 2: Author Contributed before with a hackathon team member, 3: Author is not related to the hackathon team).</li> <li><strong>UsedBySmallProject-UsedByMediumProject-UsedByLargeProject</strong>: This column represents if the hackathon blob is reused again after the hackathon event and what is the project size that reused the code (1: not reused, 3: reused in small project, 4: reused in medium project, 5: reused in large project, NA: not in scope of the reuse analysis)</li> <li><strong>IsTemplate</strong>: This column represents if the blob is identified as a template file or not ( YES: File is template, NO: File is not template). The template definition and method is explained in the research paper.</li> </ul>
Illustrations from the Environmental Data Science Book: Shared under CC-BY 4.0 for reuse
<p>Illustrations as part of the <em>Environmental Data Science</em> book.</p> <p>When using any of the images, please include the following attribution with the specific DOI as listed on the particular Zenodo page:</p> <blockquote> <p>This illustration is created by Scriberia with The Turing Way community. Used under a CC-BY 4.0 licence. DOI: <a href="https://doi.org/10.5281/zenodo.7030142">10.5281/zenodo.7030142</a></p> </blockquote> <p>When using any of the images, please include the following attribution with the specific DOI as listed on the particular Zenodo page:</p> <p>You can cite all versions by using the DOI <a href="https://doi.org/10.5281/zenodo.7030142">10.5281/zenodo.7030142</a>. This DOI represents all versions, and will always resolve to the latest one.</p> <p><em>This work was supported by Wave 1 of The UKRI Strategic Priorities Fund under the EPSRC Grant EP/W006022/1, particularly the Environment & Sustainability theme within that grant & The Alan Turing Institute.</em></p>
Data from: The biogeochemical boomerang: Site fidelity creates nutritional hotspots that may promote recurrent calving site reuse
<p>Animals interact with nutrient cycles by consuming and depositing nutrients, interactions that are studied in the separate fields of nutritional ecology and zoogeochemistry. Recent theoretical work has begun bridging these disciplines, highlighting that animal-driven nutrient recycling could be crucial in helping animals meet nutritional needs. When animals exhibit site fidelity, they consistently deposit nutrients, potentially improving vegetation quality. We investigated this potential feedback by analyzing changes in forage nitrogen stocks following simulated caribou calving. We found that forage nitrogen stocks increased after two weeks and remained elevated after one year, a change due to an increase in forage quality but not quantity. We thus highlight a positive zoogeochemical feedback whereby caribou deposit nutrients during calving that become bioavailable during lactation and provide evidence that site fidelity creates a biogeochemical boomerang in which animals deposit nutrients that can be reused at a later time.</p>
KITAB Text Reuse Data
<p><a href="http://kitab-project.org/">KITAB</a> is funded by the <a href="https://erc.europa.eu/">European Research Council</a> under the European Union’s Horizon 2020 research and innovation programme, awarded to the <a href="http://kitab-project.org/">KITAB</a> project (Grant Agreement No. 772989, PI Sarah Bowen Savant), hosted at Aga Khan University, London. In addition, it has received funding from the <a href="https://www.qnl.qa/en">Qatar National Library</a> to aid in the adaptation of the passim algorithm for Arabic.</p> <p><a href="http://kitab-project.org/">KITAB</a>’s text reuse data is generated by running <a href="https://github.com/dasmiq/passim">passim</a> on the OpenITI corpus (DOI: <a href="https://doi.org/10.5281/zenodo.3082463">10.5281/zenodo.3082463</a>). Each version is the output of a separate run and the version number corresponds to the corpus releases.</p> <p>To prepare the corpus for a passim run, we normalize texts and remove most of the non-Arabic characters and then chunk the texts into passages of 300 words (using the non-Arabic characters, including white space) in length. The chunks, called milestones, are identified by unique ids. This dataset represents the reuse cases that have been identified among milestones. </p> <p>The text reuse dataset consists of folders for each book. Each folder includes CSV files of the text reuse cases (alignments) between the corresponding book and all other books with which passim has found instances of reuses. The files have the below naming convention, using the book ids:</p> <p> <bookVersionID1>_<bookVersionID2>.csv<br> (e.g., ‘Shamela000001.mARkdown_Shamela000002.csv’).</p> <p><br>The CSV files are not the immediate output of passim, rather the result of the post-processing step. The folder structure is as below (for a total of four books, for example).</p> <p> bookVersionID1<br> |- bookVersionID1_bookVersionID4.csv<br> |- bookVersionID1_bookVersionID3.csv <br> bookVersionID4<br> |-bookVersionID4_bookVersionID3.csv</p> <p><br>Where we do not have any CSV files in any of the folders, it means that the passim algorithm has not been able to find any text reuse cases for that specific book. In the above example, we can not find any folder or CSV files for bookVresionID2, that means no reuse cases are detected between book2 and of the other three books.</p> <p>To save computational resources, we generate text reuse data uni-directionally, which means a pair of documents is compared only once (document1 to document2, not document2 to document1). </p> <p>The alignments the CSV files are a list of records. Each record shows a pair of matched passages between two books together with statistics, such as the algorithm score, and contextual information, such as the start and end positions of aligned passages so that one can find those passages in the books. A description of the alignment fields is given in the release notes.</p> <p>For each dataset, we also generate statistical data on the alignments between the book pairs. The data is published in an application that facilitates search, filtering, and visualizations. The link to the corresponding application is given in the release notes.</p> <p><strong>Note on Release Numbering</strong>: Version <strong>2020.1.1</strong>—where <strong>2020</strong> is the year of the release, the first dotted number—<strong>.1</strong>—is the ordinal release number in 2020, and the second dotted number—<strong>.1</strong>—is the overall release number. The first dotted number will reset every year, while the second one will continue on increasing.</p> <p><strong>Note:</strong> The very first release of the KITAB text reuse data (2019.1.1) is published <a href="http://dev.kitab-project.org/passim01022019/">here</a> as it was too big to publish on Zenodo. To receive more information on the complete datasets please contact us via kitab-project@outlook.com (or other team members). </p> <p>Future releases may include part of the generated data if the size of whole data is too big to publish on Zenodo. However, the data is open access for anyone to use. We provide the detailed information on the datasets in the corresponding release notes.</p>
MADFORWATER: WP2: Adaptation of wastewater treatment technologies for agricultural reuse: Task2.4: Industrial wastewater treatment: Treatment of different types of wastewater by means of innovative resins: Subset2
<p>This dataset contains the data underlying the following publication: Li Qimeng, Wang Cheng, Hua Ming, Shuang Chendong, Li Aimin, Gao Canzhu. (2017). High-efficient removal of phthalate esters from aqueous solution with an easily regenerative magnetic resin: Hydrolytic degradation and simultaneous adsorption. <em>Journal of Cleaner Production</em><em>. </em> <a href="https://doi.org/10.1016/j.jclepro.2017.11.121">https://doi.org/10.1016/j.jclepro.2017.11.121</a></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.