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Dataset results
28 results for “data reuse”
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>
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>
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>
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: WP1: Water and water-related vulnerabilities in Egypt, Morocco and Tunisia: Task1.2: Analysis and mapping of water stress, water vulnerability and potential for water reuse in Egypt, Morocco and Tunisia: Subtask1.2.b: Data collection on water stress and vulnerability: Souss-Massa Region Subset
<p>This folder contains the dataset that I used to write my conference paper "Groundwater Resources Scarcity in Souss-Massa Region and Alternative Solutions for Sustainable Agricultural Development"</p>
Building Public Confidence in Constructed Wetlands for Wastewater Treatment and Reuse: Survey Data and Focus Group Transcripts
<p>Constructed wetlands have been proposed as a cost-effective wastewater treatment, storage, and reuse solution for communities that are considering alternative water supply options to meet essential demands. In 2016, we began exploring the idea of wastewater reuse and the construction of an experimental wetland in Sewanee, located in the southern U.S. state of Tennessee. As a major barrier to water reuse is often public resistance, we conducted a survey and focus groups to determine strategies to develop and initiate a community engagement campaign, aiming to empower residents to form reasoned opinions about local water supply options.</p> <p>This data set includes the survey that was distributed to Sewanee community members between November 2015 and February 2016, as well as protocols for three focus groups that were conducted with K12 teachers and community leaders on February 11 and 12, 2016. The survey results are summarized in a Microsoft Excel file. The three focus groups were transcribed, these transcripts are included here as PDF documents.</p>
Reuse of Model Transformations for Propagating Variability Annotations in Annotative Software Product Lines - Evaluation Data
<p>This package contains all data that was produced for and used in the doctoral thesis for evaluating commutativity of propagating annotations in model-driven product lines.<br> This includes the implementation that conducts the evaluation, the measured results, and the input subjects.</p>
Raw data files for the manuscript entitled "Hybridization of Synthetic Humins with a Metal–Organic Framework for Precious Metal Recovery and Reuse"
<p>Datasets for the data presented in the manuscript entitled "Hybridization of Synthetic Humins with a Metal–Organic Framework for Precious Metal Recovery and Reuse" published in ACS Applied Materials and Interfaces.</p>
Results of the poll in the study "Information Scientists' Motivations for Research Data Sharing and Reuse"
<p>This is a dataset with results of the poll conducted in the study “Information Scientists’ Motivations for Research Data Sharing and Reuse”.</p> <p>In terms of the Uses and Gratifications Theory (Questions 1 and 2), the most popular uses relate to the categories of research support and information. Researchers share, or would share, their research data in general for any reusability purposes and especially for combination of different datasets to produce new evidence. Also, the vast majority of study participants associate research data sharing with possibilities to accelerate scientific progress and to increase research efficiency. In case of research data reuse, all the researchers indicated that they use, or would use, others’ data first of all for inspiration. Interestingly, study participants put relatively high the category of recognition in case of sharing, but at the same time they do not associate increased recognition among colleagues and other researchers with research data reuse. The remaining categories belonging to the categories of self-esteem and social interaction, i.e. increased citation level and visibility of the research as well as enhanced scientific reputation, possible cooperations and co-authorship, were selected only by few respondents. Also remarkably, data reuse is more frequently linked to entertainment then data sharing. </p> <p>In terms of the Self-Determination Theory (Questions 3 and 4), all but one of the interviewees indicated that they have shared or would share their research data because it can accelerate scientific progress which they consider important and would like to contribute to it (i.e., identified regulation). The second most popular motivation turned out to be the obligation by employer, project funder and/or journals (i.e., external regulation). The third most popular option was social influence, i.e. because many other researchers participate in data sharing and they feel obligated to do the same (i.e., external regulation).This way, the participants demonstrate a mixture of identified motivation and external regulation, both material and social. In the case of data reuse, the participants demonstrate more homogeneous results with identification and intrinsic motivation having most of the votes. The role of external regulation seems to be much less important as in the case with data sharing. So, researchers reuse, or would reuse, research data because it can accelerate scientific progress which is important for them. Additionally, researchers enjoy exploring and using third party research data. Thus, interviewees participate or would participate in data sharing because they consider it important, but also feel or are obliged to do so. At the same time, study participants do not feel pressure from outside when deciding whether to reuse data or not.</p> <p>For more information about the study and its results, please read the article “Information Scientists’ Motivations for Research Data Sharing and Reuse” by Shutsko and Stock (2023).</p>
Data from: The biogeochemical boomerang: Site fidelity creates nutritional hotspots that may promote recurrent calving site reuse
Open the record for dataset details and reuse information.
Supporting information for IUCr survey on raw data archival and reuse in chemical crystallography
<p>Supporting information i.e. questions, responses and raw data, relating to a study into raw data management and availability in small molecule crystallography conducted under the auspices of the International Union of Crystallography Committee on Data.</p> <p>It is now common to deposit structure factors when publishing, which means that the small molecule crystallography community caters very well for routine structures. However this is generally only the case if everything in a raw image is fully and/or properly accounted for and the model is correct or appropriate. So for example, in some cases raw data may no longer be required, while in others it may be necessary to validate or ‘do better’ in the future. Moreover there are increasing pressures from bodies e.g. funders to make the data relating to research outputs Findable, Accessible, Interoperable and Reusable (FAIR). In acknowledgement of this situation and in order to begin addressing it, IUCr Journals now facilitate access to and citation of large raw diffraction datasets in its articles. Therefore it is important for our community understand and define how we manage our raw data in this respect.</p> <p>As Members of the IUCr Committee on Data we see the need to conduct this survey about exploring raw data archival<br> practice and gathering opinions as to if/how raw data could/should be used if it were to be made more widely<br> available.</p>
Simulation data DC Waste Heat Reuse - Multiple DCs connected to river water / ATES system
<p>CATALYST D7.5 report - SBP-DC-TC-7 - Scenario 7</p> <p>Compilation of simulation result data on DC waste heat reuse for a DC chain, connected to river water / ATES free cooling system.</p> <p>Scenario 7: Workload federated DCs - DC Waste Heat Reuse - EleENGctrical Flexibility - IT Load Migration</p>
Research Data Management – Reuse of Research Data (Video)
<p>Video of our latest presentation for the training seminars in Research Data Management of the FoDaKo-project: <a href="https://fodako.de/">https://fodako.de</a></p> <p> </p> <p>Slides can be found here: <a href="https://doi.org/10.5281/zenodo.3269237">10.5281/zenodo.3269237</a></p>
Data, code snippets, and resources of BMF CP51: Political ideology, climate change belief, and potable water reuse willingness in the USA
<p>The dataset, data description, code snippets, and figures related to the Bayesian analysis of BMF CP 51 on SM3D Portal were uploaded to Zenodo to enhance transparency and assist in later replication and validation (https://mindsponge.info/posts/246). The original dataset can be found at: https://www.sciencedirect.com/science/article/pii/S2352340920301839</p>
Dataset for "Initial insight of three modes of data sharing: Prevalence of primary reuse, data integration and dataset release in research articles"
<p>The dataset for "Initial insight of three modes of data sharing: Prevalence of primary reuse, data integration and dataset release in research articles" is coded as follows:</p> <p>01 DOI: DOI<br> 02 article number: the accession number in Web of Science<br> 03 article title: title of the articles<br> 04 exclude: if the article was excluded from the sample, assign 1.<br> 05 research_field: the categories of research fields are described in the Appendix (Table S1)<br> 06 target_of_study: the categories of the target of studies are described in the Appendix (Table S1)<br> 29 release_location_nameofpublicarchive: the names of the deposited public archives (comma separated)</p> <p>The following items, if they occur, are assigned a value of 1:<br> 07 No_datause: The article did not use data<br> 08 primary_reuse: primary reuse<br> 09 primary_data_specificresarchdata: primary reuse of specific research data<br> 10 primary_data_resource: primary reuse of resource<br> 11 primary_source_self: primary reuse from self-constructed data<br> 12 primary_source_citation: primary reuse from citation<br> 13 primary_source_archive: primary reuse from an archive<br> 14 primary_source_others: primary reuse from the other source<br> 15 primary_souce_na: primary reuse source is not available<br> 16 data_integration: data integration<br> 17 integration_type_empirical: data integration as empirical type<br> 18 integration_type_Introductionmaterialresearchmethod: data integration as introduction/material/research methods type<br> 19 integration_type_combinedanalysis: data integration as introduction/material/research methods type<br> 20 integration_source_self: data integration from self-constructed data<br> 21 integration_source_citation: data integration from citation<br> 22 integration_source_archive: data integration from an archive<br> 23 integration_source_others: data integration from the other source<br> 24 integration_source_na: data integration source is not available<br> 25 dataset_release: dataset release<br> 26 release_location_publicarchive: dataset deposit to a public archive <br> 27 release_location_supporting: dataset release in Supporting Information<br> 28 release_location_onrequest: dataset release through personal contacts </p> <p> </p> <p>The appendix includes following tables:<br> Table S1. Coding schema for analysis<br> Table S2. Primary reuse by research field and reused data<br> Table S3. Primary reuse by target of study and reused data<br> Table S4. Data integration by research field and reuse type<br> Table S5. Data integration by target of study and reuse type<br> Table S6. Dataset release by research field<br> Table S7. Dataset release by target of study and methods<br> Table S8. List of names of public data archives for dataset release</p>
Data from: Data sharing, management, use, and reuse: practices and perceptions of scientists worldwide
Open the record for dataset details and reuse information.
Results Data for the Circular City and Adaptive Reuse of Cultural Heritage Opportunity Index
<p>The data file provides the results data for the article, "Circular City and Adaptive Reuse of Cultural Heritage Index: Measuring the Investment Opportunity in Europe" written by Dr. Gillian Foster and Dr. Ruba Saleh.</p>
Kaggle Data Reuse Community Survey Feb 11 2021
<p>Kaggle Data Reuse Community Survey Dataset</p> <p>Date: Feb 2021</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.