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62 results for “open research data”
Monitoring open access publishing of NWO funded research (data)
<p>This is the dataset underlying the report "Monitoring open access publishing of NWO funded research" (https://doi.org/10.5281/zenodo.5055609). </p> <p>The report presents statistics on the extent to which publications from the period 2015–2020 funded by NWO are available in Open Access . The analyses presented in this report also cover publications funded by the Netherlands Organisation for Health Research and Development ZonMw.</p> <p>This report builds on an <a href="https://doi.org/10.5281/zenodo.4446042">earlier report</a> published in 2020 covering publications from the period 2015–2018.</p> <p> </p>
Data and Code for "Value dissonance in research(er) assessment: Individual and institutional priorities in review, promotion and tenure criteria related to research quality, quantity, openness and responsibility"
<p>This snapshot contains code and data for the preprint "Value dissonance in research(er) assessment: Individual and institutional priorities in review, promotion and tenure criteria related to research quality, quantity, openness and responsibility".</p> <p>Instructions on re-using the data and running the code can be found in the README.md.</p> <p>Changes:</p> <ul> <li>Added survey instrument and informed consent.</li> </ul>
The value framework of open research data_Appendix-v3
<p>The supplementary data and the final version of the questionnaire "<strong>Survey on the 'Value of Open Research Data'".</strong></p>
SOils DAta Harmonization database (SoDaH): an open-source synthesis of soil data from research networks
This SOils DAta Harmonization (SoDaH) database is designed to bring together soil carbon data from diverse research networks into a harmonized dataset that can be used for synthesis activities and model development. The research network sources for SoDaH span different biomes and climates, encompass multiple ecosystem types, and have collected data across a range of spatial, temporal, and depth gradients. The rich data sets assembled in SoDaH consist of observations from monitoring efforts and long-term ecological experiments. The SoDaH database also incorporates related environmental covariate data pertaining to climate, vegetation, soil chemistry, and soil physical properties. The data are harmonized and aggregated using open-source code that enables a scripted, repeatable approach for soil data synthesis.
Data from: Exploratory and confirmatory research in the open science era
<p>1. Applied ecological research is increasingly inspired by the Open Science movement. However, new challenges about how we define our science when biodiversity data is being shared and re-used are not solved. Among these challenges is the risk associated with blurring the distinction between research that mainly seeks to explore patterns with no a-priori articulated hypotheses (exploratory research), and research that explicitly tests a-priori formulated hypotheses (confirmatory research).</p> <p>2. A rapid screening of a random selection of peer-reviewed articles suggests that neither experimental protocols nor hypothesis-testing sensu stricto are common in applied ecological research. In addition, most experiments are carried out on small spatial scales, which contrast with current global policy needs and research trends towards addressing large spatial and temporal scales. This latter trend make it unfeasible for policy to rely mainly on insights gained from experimental research.</p> <p>3. To solve fundamental local, regional and global societal challenges, we need both exploratory and confirmatory research, and the fundamental (but different) role that hypothesis-testing and prediction play in applied ecological research should be revaluated.</p> <p>4. A clearer distinction between exploratory and confirmatory research could be facilitated by allocating journal sections to different types of research; embracing new tools offered by the open science era, such as pre-registration of hypothesis; establishing new systems where post-hoc hypotheses emerging through exploration can also be registered for later testing; and more broad adoption of causal inference methods that foster more structured testing of hypotheses about causal mechanisms from observational biodiversity data.</p> <p>5. Synthesis and applications. To gain the full benefits from the open science era, researchers, funding bodies and journal editors should explicitly consider incentives that encourage openness about methods and approaches, as well as value the full plurality of scientific approaches needed to address questions in conservation science.</p>
Open data for spatial public health research
<p><strong>Background</strong></p> <p>Preventive and health-promoting policies can guide (place and space-specific) factors influencing human health, such as the physical and social environment. Required is data that can lead to a more nuanced decision-making process and identify both, existing and future challenges. Along with the rise of new technologies, and thus the multiple opportunities to use and process data, new options have emerged to measure and monitor factors that affect health. Thus, in recent years, several gateways for open data (including governmental and geospatial data) became available. At present, an increasing number of research institutions as well as (state and private) companies and citizens' initiatives provide data. However, there is a lack of overviews covering the range of such offerings regarding health. In particular, for geographically differentiated analyses, there are challenges related to data availability at different spatial levels and the growing number of data providers.</p> <p><strong>Objectives</strong></p> <p>To provide an overview of open data resources available in the context of space and health to date. It also describes the technical and legal conditions for using open data</p> <p><strong>Results</strong></p> <p>An up-to-date summary of results including information on relevant data access and terms of use is provided along with a web visualization. All data is available for further use under an open license.</p>
Funding Covid-19 research: Insights from an exploratory analysis using open data infrastructures - Supplementary material
<p>This dataset contains supplementary material for the paper 'Funding Covid-19 research: Insights from an exploratory analysis using open data infrastructures' by Alexis-Michel Mugabushaka, Nees Jan van Eck, and Ludo Waltman.</p> <ul> <li>supplementary_material_1_dataset.ods: Dataset of Covid-19 publications.</li> <li>supplementary_material_2_sample.ods: Samples of publications used to assess the accuracy of funding data in the different databases.</li> <li>supplementary_material_3_tables_and_figures.ods: Statistics underlying the tables and figures presented in the paper.</li> </ul>
Data for manuscript sumitted to Open Research Europe, entitled "Low-resistivity, high-resolution W-C electrical contacts fabricated by direct-write focused electron beam induced deposition"
<p>These are the data obtained experimentally and used to draw the figures in the article submitted to Open Research Europe</p>
Data underlying research paper: Exploring Governance Modes in Open Data Initiatives: Insights from France and Ireland
<p><span><span>The folder </span><span>contains</span> <span>the interview questions</span> <span>of</span><span> the research paper “</span></span><span><span>Exploring Governance Modes in Open Data Initiatives: I</span></span><span><span>nsights</span><span> from France and Ireland</span></span><span><span>” and consists of the interview questions. </span></span><span> </span></p> <div> <p><strong><span><span>Note about the interview </span><span>and the interview </span><span>questions:</span></span><span> </span></strong></p> </div> <div> <p><span><span>The </span><span>semi-structured </span><span>interviews were conducted with </span><span>(three) </span><span>experts selected through purposive</span> <span>sampling combined with snowballing approach to gain knowledge from</span> <span>key people who actively worked in the coordination bodies in France and Ireland. In</span> <span>the case of France, the first interviewee was reached following the reading of a blogpost</span> <span>on the governance of open data during the Covid-19 crisis. The first interviewee</span> <span>allowed the identification of a second expert who could complement his knowledge on</span> <span>the governance of the open data in France. In the case of Ireland, the expert was</span> <span>identified through the reading of an academic article that explained in great details the</span> <span>unfolding of the events that led to the Covid-19 open data strategy in Ireland. All</span> <span>the interviewees were approached through email exchange. Interviews took place in</span> <span>August 2022 online through the Microsoft Teams and Webinaire de l’État platforms,</span> <span>each lasting approximately forty-five minutes.</span></span><span> </span></p> <p><strong><span>Acknowledgment:</span></strong></p> </div> <p><span>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 955569. The opinions expressed in this document reflect only the author’s view and in no way reflect the European Commission’s opinions. The European Commission is not responsible for any use that may be made of the information it contains.</span></p>
Gates Open Research - publication data
<p>Gates Open Research (https://gatesopenresearch.org/) publication metadata and events data, as of 1 March 2018.</p>
Is there a social life in Open Data? Open datasets exploring practices in Educational Technology Research
<p>In the landscape of Open Science, Open Data [OD] plays a crucial role as data are one of the most basic components of research despite their diverse formats across scientific disciplines. Opening up data is a recent concern for policy makers and researchers as the basis for good open science practices. The common factor underlying these new practices – the relevance of promoting open data circulation and reuse – is mostly a social form of knowledge sharing and construction. However, while data sharing is being strongly promoted by policy making and is becoming a frequent practice in some disciplinary fields, open data sharing is much less developed in social sciences and in educational research.</p> <p>The Open Data hereby introduced is composed of two integrated datasets which collect data relating practices of Open Data publication and sharing in the field of Educational Thecnologies. This data was collected to suport the aim of investigating open data sharing in a selection of open data repositories as well as in the academic social network site ResearchGate.</p> <p>The research questions addressing this study are:</p> <ol> <li>Do researchers in the field of Educational Technology publish Open Datasets [ODs]?</li> <li>To which extent are ODs compliant with the FAIR data principles?</li> <li>What is the social life relating to the ODs in terms of the metrics provided by the OD portals? As a subsidiary question: 3.a- To what extent do open data portals allow researchers to cultivate social practices around OD?</li> </ol> <p> </p> <p>In order to investigate OD presence in ResearchGate, the following research aim guided the second part of the study: Analysis of the presence of the same selected OD in ResearchGate and of the type of social activity OD exhibited by OD according to ResearchGate mètrics.</p> <p>The Open Data here presented is composed by two datasets.</p> <ol> <li>23 datasets extracted out from 82 randomly selected datasets, from an inital total of 633. The data set presents a Codebook explaining the several categories of analysis or variables and the values assigned to the same. Since the analysis was performed in two phases (first selection and screening, second selection and classification) the codebook is divided in two tables. Moreover, the data set contains 6 Worksheets, ordered taking the workflow as reference: Codebook, Dataset_Extraction, Analysis_Workflow, Selected_Analysis. This dataset has been produced and curated by J.E.Raffaghelli</li> <li>The analysis of correspondance for the 23 datasets in ResearchGate. The data set presents a Codebook, and is divided into two worksheets: Codebook, ODT&RG. The last worksheet introduces the comparison between the social mètrics within RG of the found datasets and the relating papers. This dataset has been produced and curated by S. Manca and J.E. Raffaghelli.</li> </ol>
Open dataset for the research of "Assessing accuracy improvement of integrating digital footprints into gridded population mapping: spatiotemporal variations and data bias"
<p>Result datasets for "Assessing accuracy improvement of integrating digital footprints into gridded populationmapping:spatiotemporal variations and data bias":</p> <ol> <li> S1 is the results for gridded population mapping using different methods.</li> <li> S2 is the aggregate results of population mapping at county level.</li> <li> S3 is the results for intraday variation of population disaggregation accuracy,</li> <li> S4 is the data bias of different digital footprints.</li> </ol>
Data Matrix Theme-Specific Analysis of the Recommendation on Science and Scientific Researchers (RSSR): Open Access, Open Data, and Open Science
<p>This Table sets out findings from the mapping exercise conducted as part of the objectives of subtask 6.1 of the RRING project.</p> <p>Aim: Alignment of RRI to advance the UN SDGs.</p> <p>Objectives:</p> <ul> <li>Mapping the RSSR to the SDGs </li> </ul> <p>Mapping the RSSR to the SDGs is aimed at providing new perspectives, ideas and approaches that can help to improve the operationalization and implementation of each SDG, <em>by facilitating the integration of RRI (or RRI-like) practices in the SDGs, to make them more achievable.</em> The impact of the new perspectives, ideas and approaches in SDG operationalization and implementation will be aimed at the level of <em>national and international policy (making); future research and innovation projects (in industry and academia); as well as education and training of researchers, policy makers and other stakeholders.</em></p> <p>Two documents were used for this task:</p> <ul> <li>2017 Recommendation on Science and Scientific Researchers ([RSSR], UNESCO), and</li> <li>the United Nations 2030 Agenda for Sustainable Development with the 17 Sustainable Development Goals (SDGs).</li> </ul>
Dataset relating the Social activity of Open Research Data on ResearchGate
<p>The potential of Open Research Data (ORD) within the context of open science and digital scholarship can be frustrated if data remains unused. Although current research has investigated the way ORD is being published, researchers’ behaviour of ORD publishing and sharing on academic social networks (ASN) remains insufficiently explored. The research to which this dataset is connected aims to illustrate some parameters of social activity around self-archived ORDs on ResearchGate. The study analyses whether the ORDs publication leads to social activity (reads and citations) around the ORDs and their linked published articles, including eventual associations between the social activity and the researchers’ profile (scientific domain, gender, region, professional position, reputation) as well as the quality of the ORD published.</p> <p>The current dataset is composed by:</p> <p>A- The .csv file, extracted as a random sample of 752 ORD items from ResearchGate. The dataset has been polished and anonymized. The variables relating the researchers' profiles and citations to the ORD lined research were got from the researchers' profiles and published research. However, for the purpose of anonymisation, these variables have been coded and the original information removed.</p> <p>B- The codebook explaining the variables and metrics contained in the file (A)</p> <p>C- The R script. This script contains a number of explorations not reported in the final paper. The quantitative techniques applied include descriptive statistics, logistic regression and K-means cluster analysis.</p> <p>D- Five tables, three figures and two annexes (Logistic Regression I and II) created over the basis of the dataset (A)</p> <p>The results have been interpreted in terms of three main aspects.</p> <ul> <li>Firstly, there is still an underdeveloped social activity around self-archived ORD in ResearchGate (operationalized as reads and citations) overall and in spite of the published ORDs quality.</li> <li>Secondly, it was found an uncovering of the relevance of the moderating effects over ORD, which spots traditional dynamics within the “innovative” practice of engaging with data practices.</li> <li>Thirdly, a rather similar situation of ResearchGate as ASN with regard to other data platforms and repositories in terms of social activity around ORD was detected.</li> </ul> <p>The potential of Open Research Data (ORD) within the context of open science and digital scholarship can be frustrated if data remains unused. </p>
Research Workflows and Open Science - Data Set
<p>Data set accompanying the report "Research Workflows and Open Science", a systematic study of open science research workflows.</p> <p>The data set summarises the open science characteristics exhibited by the analysed workflows. The first two columns ‘<strong>workflow ID</strong>’ and ‘<strong>URL</strong>’ are dedicated to the ID we used to identify each workflow and to the publications related to the workflows respectively.</p> <p>The remaining columns are dedicated to the characteristics exhibited by the analysed workflows and are named The remaining columns are dedicated to the characteristics exhibited by the analysed workflows and are named following the different categories identified:</p> <ul> <li> <p>'<strong>used/open science infrastructure/virtual</strong>'</p> <ul> <li> <p>If a workflow relies on a virtual open infrastructure (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open science infrastructure/physical</strong>'</p> <ul> <li> <p>If a workflow relies on a physical open infrastructure (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open source software</strong>'</p> <ul> <li> <p>If a workflow relies on open source software (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open hardware</strong>'</p> <ul> <li> <p>If a workflow relies on open hardware (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open research data</strong>'</p> <ul> <li> <p>If a workflow (re)uses open research data (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open educational resources</strong>'</p> <ul> <li> <p>If a workflow (re)uses open educational resources (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/(open access) scientific publication</strong>'</p> <ul> <li> <p>If a workflow envisages the release of a scientific publication (e.g. papers, reports, data management plans, preprints, study designs) under an open access licence (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/open source software</strong>'</p> <ul> <li> <p>If a workflow envisages the release of software (e.g. code, analysis scripts) under an open access licence (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/open research data</strong>'</p> <ul> <li> <p>If a workflow envisages the release of open research data (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/open educational resources</strong>'</p> <ul> <li> <p>If a workflow envisages the release of open educational resources (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>transparency/transparency type</strong>'</p> <ul> <li> <p>degree of transparency of a workflow, defined in terms of which research products are openly shared and when in order to document the research processes (‘built-in’ if transparent, ‘enabled’ if capable of being transparent, ‘opaque’ otherwise)</p> </li> </ul> </li> <li> <p>'<strong>transparency/sharing type</strong>'</p> <ul> <li> <p>workflow categories based on when the research products are shared (‘end’ for sharing at the end of the workflow, mixed for sharing part of the research products during the workflow and the rest at the end of it, ‘iterative’ for sharing iteratively during or at the end of the related workflow phase, and ‘user-dependent’, where it is ultimately up to the researcher to decide when to share the research products since the workflow offers different paths to follow while imposing no sharing constraint.)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/collaboration implementation</strong>'</p> <ul> <li> <p>If a workflow implements collaborative practices (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/crowdfunding</strong>'</p> <ul> <li> <p>If a workflow envisages crowdfunding (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/crowdsourcing</strong>'</p> <ul> <li> <p>If a workflow envisages crowdsourcing (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/scientific volunteering</strong>'</p> <ul> <li> <p>If a workflow envisages scientific volunteering (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/citizen and participatory science</strong>'</p> <ul> <li> <p>If a workflow envisages citizen and participatory science (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open dialogue with other knowledge systems/indigenous peoples</strong>'</p> <ul> <li> <p>If a workflow envisages the establishment of a dialogue with indigenous peoples (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open dialogue with other knowledge systems/marginalised scholars</strong>'</p> <ul> <li> <p>If a workflow envisages the establishment of a dialogue with marginalised scholars (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open dialogue with other knowledge systems/local communities</strong>'</p> <ul> <li> <p>If a workflow envisages the establishment of a dialogue with local communities (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>assessment</strong>'</p> <ul> <li> <p>If a workflow implements assessment processes for the evaluation of the research products created (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>automation</strong>'</p> <ul> <li> <p>If a workflow includes automated processes (yes/no)</p> </li> </ul> </li> </ul>
Open data and research tools
<p>From article: Government Innovation Laboratories in South America: Congruences and Peculiarities</p> <p>Journal: Brazilian Administration Review (BAR)</p>
Open data for spatial public health research
Open the record for dataset details and reuse information.
Data from: Digitizing extant bat diversity: an open-access repository of 3D μCT-scanned skulls for research and education
Open the record for dataset details and reuse information.
Data from: Exploratory and confirmatory research in the open science era
Open the record for dataset details and reuse information.
Data underlying research paper "Developing an open data intermediation business model: insights from the case of Esri"
<p><strong>Data underlying research paper “Developing an open data intermediation business model: insights from the case of Esri” </strong></p> <p>by Ashraf Shaharudin, Bastiaan van Loenen, and Marijn Janssen from Delft University of Technology (TU Delft), the Netherlands.</p> <p>This folder contains data underlying the research paper “Developing an open data intermediation business model: insights from the case of Esri”. It consists of:</p> <p>1. De-identified interview transcripts</p> <p>2. Informed consent form template</p> <p><strong>Note about the de-identified interview transcripts:</strong></p> <p>The de-identified interview transcripts should be read in the context of the research on open data ecosystem and the role of Esri as open data intermediaries.</p> <p>The 27 interviews, involving 29 interviewees, were conducted between April 2023 and April 2024 based on the semi-structured approach. We shared the tentative interview questions with the interviewees in advance (for the majority, at least three working days prior). Since they are semi-structured interviews, the ultimate interview questions may differ from the tentative questions.</p> <p>We removed personally identifiable information from the transcripts. Some interviewees may risk being identifiable if their organization is known. Hence, we removed the organization and country information from all transcripts. </p> <p>With verbal communication, some sentences may be less incomprehensible in writing. Thus, we did minimal edits when transcribing to improve the comprehensibility where necessary, but the main objective was to keep the transcripts as close to verbatim as possible. </p> <p><strong>Note about the informed consent form template:</strong></p> <p>We sent the informed consent form to every interviewee in advance and requested that they return it to us before or during the interview. </p> <p>All interviewees whose interview transcripts are recorded in this document give permission for the anonymized transcript of their interview, with personally identifiable information redacted, to be shared in 4TU.ResearchData repository so it can be used for future research and learning.</p> <p><strong>Acknowledgement:</strong></p> <p>This research is part of the 'Towards a Sustainable Open Data ECOsystem' (ODECO) project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 955569. The opinions expressed in this document reflect only the author’s view and in no way reflect the European Commission’s opinions. The European Commission is not responsible for any use that may be made of the information it contains.</p>
ScienceDex guides
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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.