Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
632
datasets available to search
ShareScore release 0.9.0
Dataset results
632 results for “Policies”
Data from: A critical analysis of the potential for EU Common Agricultural Policy measures to support wild pollinators on farmland
<p>1. Agricultural intensification and associated loss of high-quality habitats are key drivers of insect pollinator declines. With the aim of decreasing the environmental impact of agriculture, the 2014 EU Common Agricultural Policy (CAP) defined a set of habitat and landscape features (Ecological Focus Areas: EFAs) farmers could select from as a requirement to receive basic farm payments. To inform the post-2020 CAP, we performed a European-scale evaluation to determine how different EFA options vary in their potential to support insect pollinators under standard and pollinator-friendly management, as well as the extent of farmer uptake.</p> <p> 2. A structured Delphi elicitation process engaged 22 experts from 18 European countries to evaluate EFAs options. By considering life cycle requirements of key pollinating <i>taxa</i> (i.e. bumble bees, solitary bees and hoverflies), each option was evaluated for its potential to provide forage, bee nesting sites and hoverfly larval resources.</p> <p>3. EFA options varied substantially in the resources they were perceived to provide and their effectiveness varied geographically and temporally. For example, field margins provide relatively good forage throughout the season in Southern and Eastern Europe but lacked early-season forage in Northern and Western Europe. Under standard management, no single EFA option achieved high scores across resource categories and a scarcity of late season forage was perceived.</p> <p>4. Experts identified substantial opportunities to improve habitat quality by adopting pollinator-friendly management. Improving management alone was, however, unlikely to ensure that all pollinator resource requirements were met. Our analyses suggest that a combination of poor management, differences in the inherent pollinator habitat quality and uptake bias towards catch crops and nitrogen-fixing crops severely limit the potential of EFAs to support pollinators in European agricultural landscapes.</p> <p>5. <i>Policy Implications.</i> To conserve pollinators and help protect pollination services, our study highlights the need to create a variety of interconnected, well-managed habitats that complement each other in the resources they offer<i>. </i>To achieve this the CAP post-2020 should take a holistic view to implementation that integrates the different delivery vehicles aimed at protecting biodiversity (e.g. enhanced conditionality, eco-schemes and Agri-Environment and Climate Measures). To improve habitat quality we recommend an effective monitoring framework with target-orientated indicators and to facilitate the spatial targeting of options collaboration between land managers should be incentivised.</p>
Payoff of the Grain for Green policy
<p><span>1. To protect the global ecological environment and prevent threats to human safety and property, nations around the world have invested heavily in ecological restoration programmes. However,<b> </b>we don't know whether these investments have been repaid by the resultant benefits. </span></p> <p><span>2. To answer this question, we developed an improved method of quantifying costs and benefits that accounts for more of the costs associated with ecological restoration, thereby letting us calculate the <i><span>net</span></i> benefit. </span></p> <p><span>3. To demonstrate this method, we analysed the Grain for Green Program, which is the world's largest ecological restoration program. We found that the increase in net benefits amounted to a total of 530.1×10<sup><span>9</span></sup> RMB·yr<sup><span>-1</span></sup> in 2017. However, 11 of China's 25 provinces have incurred net losses due to high cost increases and low increases in benefits under the GGP. </span></p> <p><span>4. <i><span>Synthesis and applications. </span></i>Because compensation payments to farmers and herders whose livelihoods were disrupted by implementation of the Grain for Green Program only accounted for 0.8% of the programme's cost, improving compensation payments will be essential to its success. Assessing GGP the largest ecological restoration program in the world will provide guidance for ecological protection and restoration policies around the world.</span></p> <p><span>1. To protect the global ecological environment and prevent threats to human safety and property, nations around the world have invested heavily in ecological restoration programmes. However,<b> </b>we don't know whether these investments have been repaid by the resultant benefits. </span></p> <p><span>2. To answer this question, we developed an improved method of quantifying costs and benefits that accounts for more of the costs associated with ecological restoration, thereby letting us calculate the <i><span>net</span></i> benefit. </span></p> <p><span>3. To demonstrate this method, we analysed the Grain for Green Program, which is the world's largest ecological restoration program. We found that the increase in net benefits amounted to a total of 530.1×10<sup><span>9</span></sup> RMB·yr<sup><span>-1</span></sup> in 2017. However, 11 of China's 25 provinces have incurred net losses due to high cost increases and low increases in benefits under the GGP. </span></p> <p><span>4. <i><span>Synthesis and applications. </span></i>Because compensation payments to farmers and herders whose livelihoods were disrupted by implementation of the Grain for Green Program only accounted for 0.8% of the programme's cost, improving compensation payments will be essential to its success. Assessing GGP the largest ecological restoration program in the world will provide guidance for ecological protection and restoration policies around the world.</span></p>
Data from: Finding politically feasible conservation policies: the case of wildlife trafficking
Conservation management is of increasing importance in ecology as most ecosystems nowadays are essentially managed ones. Conservation managers work within a political-ecological system when they develop and attempt to implement a conservation plan that is designed to meet particular conservation goals. In this article, we develop a decision support tool that can identify a conservation policy for a managed wildlife population that is both sustainable and politically feasible. Part of our tool consists of a simulation model composed of interacting influence diagrams. We build, fit, and use our tool on the case of rhino horn trafficking between South Africa and Asia. Using these diagrams, we show how a rhino poacher's belief system can be modified by such a policy and locate it in a perceived risks-benefits space before and after policy implementation. We statistically fit our model to observations on group actions and rhino abundance. We then use this fitted model to compute a politically feasible conservation policy.
Data from: Assessing the sustainability of African lion trophy hunting, with recommendations for policy
While trophy hunting provides revenue for conservation, it must be carefully managed to avoid negative population impacts, particularly for long-lived species with low natural mortality rates. Trophy hunting has had negative effects on lion populations throughout Africa, and the species serves as an important case study to consider the balance of costs and benefits, and to consider the effectiveness of alternative strategies to conserve exploited species. Age-restricted harvesting is widely recommended to mitigate negative effects of lion hunting, but this recommendation was based on a population model parameterized with data from a well-protected and growing lion population. Here, we used demographic data from lions subject to more typical conditions, including source–sink dynamics between a protected National Park and adjacent hunting areas in Zambia's Luangwa Valley, to develop a stochastic population projection model and evaluate alternative harvest scenarios. Hunting resulted in population declines over a 25-yr period for all continuous harvest strategies, with large declines for quotas >1 lion/concession (~0.5 lion/1,000 km2) and hunting of males younger than seven years. A strategy that combined periods of recovery, an age limit of ≥7 yr, and a maximum quota of ~0.5 lions shot/1,000 km2 yielded a risk of extirpation <10%. Our analysis incorporated the effects of human encroachment, poaching, and prey depletion on survival, but assumed that these problems will not increase, which is unlikely. These results suggest conservative management of lion trophy hunting with a combination of regulations. To implement sustainable trophy hunting while maintaining revenue for conservation of hunting areas, our results suggest that hunting fees must increase as a consequence of diminished supply. These findings are broadly applicable to hunted lion populations throughout Africa and to inform global efforts to conserve exploited carnivore populations.
Economic and financial barriers to delivering mental health services in Georgia: Informing policy and acting on evidence
<p>Dataset includes qualitative data for the manuscript entitled "Economic and financial barriers to delivering mental health services in Georgia: Informing policy and acting on evidence".</p>
D6.1 Review, analysis and crosswalk of the data requirements of key policies
Open the record for dataset details and reuse information.
Data and Code 1/3 "Public Policies and Global Forest Conservation: Empirical Evidence from National Borders"
<p>Data and Code "Public Policies and Global Forest Conservation: Empirical Evidence from National Borders", published in Global Environmental Change, authored by David Wuepper (1,2)*, Thomas Crowther (3), Thomas Lauber (3), Devin Routh (3,4), Solen Le Clec'h (5), Rachael D. Garrett (6,7), Jan Börner (8,9). (1 Land Economics Group, University of Bonn, Germany, 2 Agricultural Economics and Policy Group, ETH Zurich, Switzerland, 3 Global Ecosystem Ecology Group, ETH Zurich, Switzerland, 4 SIT Group, Uiversity of Zurich, Switzerland, 5 Environmental Systems Analysis Group, Wageningen University, Netherlands, 6 Conservation and Development, Cambridge University, UK, 7 Environmental Policy Lab, ETH Zürich, Switzerland, 8 Institute for Food and Resource Economics, University of Bonn, Germany, 9 Center for Development Research, University of Bonn, Germany. </p>
Data and Code 2/3 "Public Policies and Global Forest Conservation: Empirical Evidence from National Borders"
<p>Data and Code "Public Policies and Global Forest Conservation: Empirical Evidence from National Borders", published in Global Environmental Change, authored by David Wuepper (1,2)*, Thomas Crowther (3), Thomas Lauber (3), Devin Routh (3,4), Solen Le Clec'h (5), Rachael D. Garrett (6,7), Jan Börner (8,9). (1 Land Economics Group, University of Bonn, Germany, 2 Agricultural Economics and Policy Group, ETH Zurich, Switzerland, 3 Global Ecosystem Ecology Group, ETH Zurich, Switzerland, 4 SIT Group, Uiversity of Zurich, Switzerland, 5 Environmental Systems Analysis Group, Wageningen University, Netherlands, 6 Conservation and Development, Cambridge University, UK, 7 Environmental Policy Lab, ETH Zürich, Switzerland, 8 Institute for Food and Resource Economics, University of Bonn, Germany, 9 Center for Development Research, University of Bonn, Germany. </p>
Data and Code 3/3 "Public Policies and Global Forest Conservation: Empirical Evidence from National Borders"
<p>Data and Code "Public Policies and Global Forest Conservation: Empirical Evidence from National Borders", published in Global Environmental Change, authored by David Wuepper (1,2)*, Thomas Crowther (3), Thomas Lauber (3), Devin Routh (3,4), Solen Le Clec'h (5), Rachael D. Garrett (6,7), Jan Börner (8,9). (1 Land Economics Group, University of Bonn, Germany, 2 Agricultural Economics and Policy Group, ETH Zurich, Switzerland, 3 Global Ecosystem Ecology Group, ETH Zurich, Switzerland, 4 SIT Group, Uiversity of Zurich, Switzerland, 5 Environmental Systems Analysis Group, Wageningen University, Netherlands, 6 Conservation and Development, Cambridge University, UK, 7 Environmental Policy Lab, ETH Zürich, Switzerland, 8 Institute for Food and Resource Economics, University of Bonn, Germany, 9 Center for Development Research, University of Bonn, Germany. </p>
A systems analysis of sustainability impacts of agricultural policies in India - Supporting Information
<p>This Supporting Information provides additional details about the HTE framework applied to study sustainability challenges and interventions in the rice-wheat cropping system of Punjab, India. It includes: </p><p>Supp. Info Word document with: </p><ul><li>Text S1-S2 (Table S1-S3) on detailed quantitative model set-up and model validation and sensitivity analysis results</li><li>Text S3-S4 (Table S4-S5) on methods used to evaluate the impacts of interventions on interactions, specifying direct (structural) and indirect (quantitative) changes as well as sustainability benefits using the inclusive wealth approach.</li><li>Text S5 on expert interviews conducted to inform choice of policy options analyzed in this work </li></ul><p> </p><p>Supp. Info Excel spreadsheet Data Set S1 with the following tables:</p><p>Data Table SD1: List of system components and their attributes</p><p>Data Table SD2: Detailed interaction matrix between system components</p><p>Data Table SD3: Attributes of crops and residues: crop production, protein content and residue generation</p><p>Data Table SD4: Attributes of crops: use of agricultural inputs for crop production </p><p>Data Table SD5: Attributes of technical components: Emission factors and GWP</p><p>Data Table SD6: Values of system components' attributes at t=1 (year=2019)</p><p>Data Tables SD7-SD14: Detailed quantitative impacts of interventions on sustainability metrics</p>
Overview of Research Data Sharing Policies Across Various Scientific Publishers
<p><strong>Context </strong></p><p>With the aim of advancing trust in published research, (scientific) publishers are developing Research Data Sharing Policies for their portfolio. Some publishers have the same policy across all journals. For example for the publishers Frontiers and Springer-Nature, a Data Availability statement is mandatory for all journals in their portfolio. While other publishers have different policy levels depending on the journal (e.g. Elsevier, American Chemical Society). In this list, one will find a short overview of Research Data Sharing Policies across publishers (mostly relevant to the Faculty of Science at Utrecht University).</p><p><strong>Limitations</strong></p><p>The list of publishers does not encompass all scientific publishers. Some Research Data Sharing Policies might be subjected to change.</p>
Replication package for: "P-Hacking, Data Type and Data-Sharing Policy".
<p>See Readme file. Journal article abstract: "This paper examines the relationship between p-hacking, publication bias, and data-sharing policies. We collect 38,876 test statistics from 1,106 articles published in leading economic journals between 2002--2020. We find that while data-sharing policies increase the provision of data, they do not decrease the extent of p-hacking and publication bias. Similarly, articles that use hard-to-access administrative data or third-party surveys, as compared to those that use easier-to-access (e.g., author-collected) data are not different in their p-hacking and publication extent. Voluntary provision of data by authors on their homepages offers no evidence of reduced p-hacking."</p>
Data Policy
<p> Magnifying glass over the Data Policy Writing</p>
JTCT - Annex 1 - List of 11 analysed policy documents
Open the record for dataset details and reuse information.
Survey on key policies and perceptions_Survey data Belgium
Open the record for dataset details and reuse information.
JTCT - Annex 3 - Results of the Interdisciplinary Policy Analysis
Open the record for dataset details and reuse information.
Strategic Game Theory Analysis of Digitalization in Raw Materials Manufacturing: A Case Study of China and Implications for Public Policy
Open the record for dataset details and reuse information.
MIGNEX policy database
<p>Dataset of migration-relevant policies. See documentation for details.</p>
Open Data Set for the article Dynamic demand response to electricity prices: Evidence from the Spanish retail market. Utilities Policy, 88 (2024), 101763
<p><span>The datasets available for open access from the article ‘Furió, D. and Moreno-del-Castillo, J.<em> Dynamic demand response to electricity prices: Evidence from the Spanish retail market. Utilities Policy, 88 (2024), 101763</em>’ are provided here.</span></p> <p><span>The datasets comprise time series data on wholesale market global prices and quantity demanded by reference suppliers and competing retailers, along with day-ahead market prices for each of the 24 hours of the day spanning from January 1, 2007, to March 31, 2022. These series have been downloaded from the CNMC website. Additionally, primary temperature data were obtained from the Spanish Meteorological Agency’s website. This dataset provided hourly weather information from a nationwide network of weather stations. Given the scope of the demand series data, which reflects the entire Spanish market, the temperature series were constructed to ensure national representativeness. These data were then aggregated to develop comprehensive national temperature series, aligning them with the demand series. <span>Finally, a dummy variable for each day t of the studied period was constructed to capture the business/non-business day effect on electricity demand. It takes the value of 1 if </span></span><span>𝑡</span><span> corresponds to a working day (non-holiday, Monday to Friday) and 0 otherwise. The national holidays considered are as follows: January 1<sup>st</sup>, January 6<sup>th</sup>, May 1<sup>st</sup>, August 15<sup>th</sup>, October 12<sup>th</sup>, November 1<sup>st</sup>, December 6<sup>th</sup>, December 8<sup>th</sup>, and December 25th. Additionally, the corresponding Good Friday for each year included in the study period was also considered.</span></p>
Research software funding policies and programs: Results from an international survey (Dataset)
<p>Research software is increasingly recognized as critical infrastructure in contemporary science. Research software spans a broad spectrum, including source code files, algorithms, scripts, computational workflows, and executables, all created for or during research. Research funders have developed programs, initiatives and policies to bolster research software’s role. However, there has been no empirical study of how research funders prioritize support for research software. This information is needed to clarify where current funder support is concentrated and where strategic gaps may exist. Here, we present data from a survey of research software funders (n=36) from around the world. The survey explored these funders’ priorities, finding a strong emphasis on developing skills, software sustainability, embedding open science, building community and collaboration, advancing research software funding, increasing software visibility and use, innovation and security. </p> <h1>Methods</h1> <p>This research was carried out using a survey combining qualitative and quantitative items. The survey was designed to investigate how research software funders support research software’s sustainability and impact.</p> <p>The study was reviewed and given an exempt determination by the University of Illinois Urbana-Champaign Institutional Review Board (no. 24374).</p> <h2>Survey design</h2> <p>The survey designed for this study began by collecting profile information, including institutional affiliation and job title. The survey gathered information about respondents’ organization’s initiatives, policies, or programs to support research software. The range of questions yielded too much data for one article. In this article, we focus exclusively on the results generated via an open-ended question asking about the top priorities for the respondents’ organizations’ support for research software: “What are your organization's top priorities related to research software?”. Four open-response text boxes were provided for respondents to indicate and list these priorities.</p> <h2>Sampling</h2> <p>This survey was aimed at international research funders, including governmental and non-governmental (e.g., philanthropic) funders. A list of contacts to invite to participate in this survey was created based on participation in the Research Software Association (ReSA) and responsibility for research software funding known to the authors. This initial list of people was refined, with removals based on individuals having moved to unrelated professional roles or being unavailable long-term, for example, due to personal issues.</p> <p>The final, refined contact list comprised 71 people. After removing individuals when a member of their organization already provided a complete answer or when the person turned out to no longer be working on a relevant topic or to be otherwise unavailable (total of n=30), 41 people remained. Five of these individuals did not complete the survey, while 36 people (representing 30 research funding organizations) did, yielding a response rate of 87.8%. Fully completed survey responses were not required for individuals to be retained in the sample, resulting in varied sample bases across survey questions.</p> <p>The sample includes research funders in North and South America, Europe, Oceania and Asia, but over-represents North America and European funder representatives. Some participating funders cover a broad spectrum of disciplines, while others focus on a particular domain such as social science, health, environment, physical sciences or humanities.</p> <table> <tbody> <tr> <td> <p><strong>Continent</strong></p> </td> <td> <p><strong>Count</strong></p> </td> </tr> <tr> <td> <p>North America</p> </td> <td> <p>15</p> </td> </tr> <tr> <td> <p>South America</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>Europe</p> </td> <td> <p>12</p> </td> </tr> <tr> <td> <p>Oceania</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>Asia</p> </td> <td> <p>1</p> </td> </tr> </tbody> </table> <p>The respondents represented research funders supported by governmental (n=26), philanthropic (n=6) and corporate (n=1) resources.</p> <p>Respondents’ job titles span the following categories: <em>Senior Leadership and Executive</em>, such as a Vice President of Strategy; <em>Program and Project Management</em>, such as Senior Program Manager; <em>Planning and Business Development</em>; <em>Scientific, Technical and IT</em>, such as Scientific Information Lead.</p> <p>Most respondents 72.7% (n=24) answered ‘Yes’ to the question, “Has your organization established any policies, initiatives or programs aimed at supporting research software?”, while 18.2% (n=6) said ‘No’ and 9.1% (n=3) ‘Unsure’.</p> <h2>Data collection, management and analysis</h2> <p>Data collection took place from December 2023 to May 2024. The mean completion time for the detailed survey was 28 minutes and 13 seconds.</p> <p>The data were cleaned and prepared for analysis by removing any identifiable respondent details. The data analysis process followed a standard thematic qualitative analysis approach (e.g., Jensen & Laurie, 2016). This involved first identifying themes and organizing the data accordingly. Dimensions of each theme were identified where relevant. Then data extracts were selected from the survey responses associated with each theme and theme dimension. </p> <h1>Additional data: Evolving funding strategies for research software: Insights from an international survey of research funders</h1> <p>Data were uploaded in December 2024 to support another paper drawing on the same overall survey data. This one is entitled: 'Evolving funding strategies for research software: Insights from an international survey of research funders'. The survey data for this upload were generated using the following survey items.</p> <table> <tbody><tr> <td> <p><strong><span>Variable</span></strong></p> </td> <td> <p><strong><span>Survey Item</span></strong></p> </td> <td> <p><strong><span>Response Options</span></strong></p> </td> </tr> </tbody><tbody> <tr> <td> <p><span>Policies, initiatives, or programs aimed at supporting research software</span></p> </td> <td> <p><span>“Has your organization established any policies, initiatives or programs aimed at supporting research software?”<br>(This could include grants, fellowships, funding policies, conference funding, or other kinds of support aimed at bolstering the sustainability or impact of research software)</span></p> </td> <td> <p><span>Yes, No, Unsure</span></p> <p><span>(If ‘Yes’, then the next question was asked)</span></p> </td> </tr> <tr> <td> <p><span>Number of policies or programs to be reported</span></p> </td> <td> <p><span>“How many of your organization’s policies, initiatives or programs to support research software are you familiar with?”</span></p> </td> <td> <p><span>1, 2, 3, 4, 5+</span></p> </td> </tr> <tr> <td> <p><em><span>The following questions were asked for each policy, initiative, or program</span></em></p> </td> </tr> <tr> <td> <p><span>Name of policy or program</span></p> </td> <td> <p><span>“Please name the policy, initiative or program (starting with the one you are most familiar with):”</span></p> </td> <td> <p><span>[Text line]</span></p> </td> </tr> <tr> <td> <p><span>Status of policy or program</span></p> </td> <td> <p><span>“What is the status of this policy, initiative or program?”</span></p> </td> <td> <p><span>Completed/closed, In progress/open, Other (please specify)</span></p> </td> </tr> <tr> <td> <p><span>Link(s)/description</span></p> </td> <td> <p><span>“Please provide link(s) to the policy, initiative or program, upload or email to [the researcher’s contact details].”<br>“Link(s)/Description:”<br>(If there is no documentation available, please describe it here:)</span></p> </td> <td> <p><span>[Textarea], [File upload]</span></p> </td> </tr> <tr> <td> <p><span>Type of policy or program</span></p> </td> <td> <p><span>“Which of the following best describes the policy, initiative or program you named above?”</span></p> </td> <td> <p><a name="_Hlk180534652"></a><span>Funding program, Policy that affects funding decision-making or outcomes (funder side), Policy that affects funding applicants or recipients (applicant/awardee side), Other (please specify)</span></p> </td> </tr> <tr> <td> <p><em><span>If ‘Funding program’ was selected in the previous question, then the next question was asked</span></em></p> </td> </tr> <tr> <td> <p><span>Type of funding</span></p> </td> <td> <p><span>“Which of the following best describes the available funding?”</span></p> </td> <td> <p><span>Funding that <strong>includes</strong> research software, Dedicated funding <strong>only</strong> for research software, Other (please specify)</span></p> </td> </tr> <tr> <td> <p><em><span>For all categories of policy, initiative or program, the following questions were asked.</span></em></p> </td> </tr> <tr> <td> <p><span>Problem(s) addressed</span></p> </td> <td> <p><span>“Please summarize the problem(s) this policy, initiative or program is aiming to address from your organization’s perspective:”</span></p> </td> <td> <p><span>[Text Area]</span></p> </td> </tr> <tr> <td> <p><span>Perceived level of program success</span></p> </td> <td> <p><span>“What factors have contributed to its success or lack of success?”</span></p> </td> <td> <p><span>Very successful, Successful, Neutral, Unsuccessful, Very unsuccessful, Not applicable / No opinion</span></p> </td> </tr> </tbody> </table>
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.