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11,394 results for “Surveys”
Inter-Chemical Correlation results for the study: NHANES20112012 (NHANES Survey 2011-2012)
Title: NHANES Survey 2011-2012 <br>Species: Homo sapiens <br>Number of samples: 10352 <br>Number of named analytes: 201 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Inter-Chemical Correlation results for the study: NHANES20012002 (NHANES Survey 2001-2002)
Title: NHANES Survey 2001-2002 <br>Species: Homo sapiens <br>Number of samples: 9851 <br>Number of named analytes: 230 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Inter-Chemical Correlation results for the study: NHANES20052006 (NHANES Survey 2005-2006)
Title: NHANES Survey 2005-2006 <br>Species: Homo sapiens <br>Number of samples: 9582 <br>Number of named analytes: 319 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Inter-Chemical Correlation results for the study: NHANES20092010 (NHANES Survey 2009-2010)
Title: NHANES Survey 2009-2010 <br>Species: Homo sapiens <br>Number of samples: 9717 <br>Number of named analytes: 186 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Nephrops (Nephrops norvegicus) Burrow object detection simple training dataset from Irish Underwater TV surveys
<div> <div> <div> <div> <h1>Training dataset</h1> <p>Norway prawns (<em>Nephrops norvegicus</em>), also known as the Dublin Bay prawn, are common around the Irish coast. They are found in distinct sandy/muddy areas where the sediment is suitable for them to construct their burrows. <em>Nephrops </em>spend a great deal of time in their burrows and their emergence from these is related to time of year, light intensity and tidal strength. The Irish <em>Nephrops </em>fishery is extremely valuable with landings recently worth around €55m at first sale, supporting an important Irish fishing industry. </p> <p><em>Nephrops</em> are managed in Functional Units (FUs). The Marine Institute has conducted under water television surveys since 2002 to independently estimate abundance, distribution and stock sizes of <em>Nephrops</em> <em>norvegicus </em>for:</p> <ul> <li>Irish Sea <em>Nephrops</em> Grounds (FU 14 and 15) in collaboration with <a title="Link to 'Fisheries and Aquatic Ecosystems' work in AFBI Northern Ireland" href="https://www.afbini.gov.uk/area-of-expertise/fisheries-and-aquatic-ecosystems">AFBI</a> an <a title="Link to Cefas (the Centre for Environment, Fisheries, and Aquaculture Science) in the UK" href="https://www.cefas.co.uk/">CEFAS</a>.</li> <li>Porcupine Bank <em>Nephrops</em> Grounds (FU16)</li> <li>Aran, Galway Bay and Slyne Head <em>Nephrops</em> Grounds (FU17)</li> <li>South and South west Ireland <em>Nephrops</em> Grounds (FU19)</li> <li>Labadie, Jones and Cockburn <em>Nephrops</em> Grounds (FU20 and 21)</li> <li>“Smalls” <em>Nephrops</em> Grounds (FU22)</li> </ul> <p>Each year during the summer months, on average 300 stations are surveyed each year, in three survey legs, covering all the FUs in depths from 20 to 650 metres.</p> <p>A high definition camera system is towed over the sea bed for 10 minutes travelling approx. 200m at 0.8 knots on a purpose built sledge. The UWTV survey follows survey protocols available <a title="Link to survey protocols" href="https://doi.org/10.17895/ices.pub.8014">here</a> agreed by International Council for the Exploration of the Sea (ICES) Working Group on <em>Nephrops </em>surveys (WGNEPS). </p> <p>As part of the iMagine project a selection of images from the Underwater TV survey Functional Units were annotated with bounding boxes and labels in YOLOv8 format to train an YOLOv8 Object Detection Models. The training dataset is saved in YOLOv8 format. It is intended to train a YOLOv8 Nephrrops burrow object detection model to assess the utility of an Object Detection model is assisting Prawn Survey work in the semi automated annotation of prawn burrow imagery.</p> </div> </div> </div> </div>
Local Governance in Ukraine during the full-scale Russian invasion. – Merged data from online surveys of local self-government authorities by the Congress of Local and Regional Authorities of the Council of Europe in 2022 and Kyiv School of Economics in 2024.
The dataset includes responses from two waves of online surveys targeting local self-government representatives in Ukraine, with a focus on crisis governance during the ongoing Russian war. The first wave was conducted from August 30 to September 20, 2022, by the Congress of Local and Regional Authorities of the Council of Europe, yielding 241 responses (16% of all Ukrainian local communities). The second wave was conducted by Kyiv School of Economics from January 1 to March 12, 2024, with 181 responses (14% of government-controlled municipalities). Data formats include CSV and SAV files, along with an XSL codebook for both waves. The merged dataset comprises 442 responses from small, medium, and large municipalities under varied security conditions, with a total file size of approximately 4 MB.
RRING Global Survey Research Dataset (WP3)
<p>The RRING Work Package 3 (WP3) objective was to clarify how Research Funding Organisations (RFOs) and Research Performing Organisations (RPOs) operated within region-specific research and innovation environments. It explored how they navigated the governance and regulatory frameworks for Responsible Research and Innovation (RRI), as well as offering their perspectives on the entities responsible for RRI-related policy and action in their locales.</p> <p>This data set covers the global survey research part, which was designed to contextualise how RPOs and RFOs interacted within the research environment and with non-academic stakeholders. Countries were grouped according to the UNESCO regions of the world and key results per region are listed below. For a detailed analysis and further findings of the work completed under WP3 of the RRING project, please refer to the full deliverable document "State of the Art of RRI in the Five UNESCO World Regions" [link to be inserted].</p> <p> </p> <p><strong>European and North American States</strong></p> <ul> <li>‘Diverse and inclusive': Respondents were most attitudinally supportive of the importance of ensuring ethical principles were applied in R&I (92%), followed by diverse perspectives (88%), and gender equality (79%). Including ethnic minorities was the area which garnered the least attitudinal support (71%). Respondents took the most practical steps towards engaging with diverse perspectives (63%), and the least towards inclusion of ethnic minorities (24%).</li> <li>‘Anticipative and reflective’: Respondents widely agreed (82%) with the importance of ensuring R&I work does not cause concerns for society, but only 37% confirmed they had taken practical steps to ensure this.</li> <li>‘Open and transparent’: Vast majorities of respondents agreed on the importance of keeping R&I methods open and transparent (94%), with 65% also confirming they take practical steps to do this. An equally high number agreed on the importance of making the results of R&I work accessible to as wide a public as possible (94%), and 68% confirmed this through their reported actions. This indicated the smallest value-action gap of all RRI measures for respondents from European and North American countries. Attitudinal agreement on the importance of making data freely available to the public was lower (83%), as was the practical action aspect for this measure (45%).</li> <li>‘Responsive and adaptive to change’: Most respondents agreed (89%) that it was important to ensure their work addresses societal needs, and 62% confirmed that they take practical steps towards this aim.</li> </ul> <p> </p> <p><strong>Latin American and Caribbean States</strong></p> <ul> <li>‘Diverse and inclusive': Respondents were most attitudinally supportive of the importance of gender equality in R&I (86%), followed by ensuring ethical principles are applied (85%), and diverse perspectives incorporated (83%). Including ethnic minorities was the area which garnered the least attitudinal support (77%). Respondents took the most practical steps towards ensuring ethical principles guide their work (50%), and the least towards including ethnic minorities (25%), but the smallest value action gap was found for gender equality.</li> <li>‘Anticipative and reflective’: Respondents agreed (79%) that it is important to ensure R&I work does not cause concerns for society, but only 29% confirmed they had taken practical steps to ensure this.</li> <li>‘Open and transparent’: The majority of respondents agreed on the importance of keeping R&I methods open and transparent (89%), with 45% indicating they had taken practical action. A majority also agreed on the importance of making the results of R&I work accessible to as wide a public as possible (88%), and 44% backed this up with practical action. Attitudinal agreement on the importance of making data freely available to the public was slightly lower (81%), as was the practical action aspect for this measure (35%).</li> <li>‘Responsive and adaptive to change’: Most respondents agreed (84%) that it was important to ensure their work addresses societal needs, and 49% confirmed that they take practical steps towards this aim.</li> </ul> <p> </p> <p><strong>Asian and Pacific States</strong></p> <ul> <li>‘Diverse and inclusive': Respondents were most attitudinally supportive of the importance of ensuring ethical principles were applied in R&I (90%), followed by diverse perspectives (89%), and gender equality (86%). Including ethnic minorities was the area which garnered the least attitudinal support (76%). Respondents took the most practical steps towards engaging with diverse perspectives (65%), and the least towards including ethnic minorities (30%).</li> <li>‘Anticipative and reflective’: Respondents widely agreed (78%) with the importance of ensuring R&I work does not cause concerns for society, and 42% confirmed they had taken practical steps to ensure this.</li> <li>‘Open and transparent’: The majority of respondents agreed on the importance of keeping R&I methods open and transparent (91%), with 58% indicating they take practical steps to do this. A majority also agreed on the importance of making the results of R&I work accessible to as wide a public as possible (89%), and 64% backed this up with practical action. Attitudinal agreement on the importance of making data freely available to the public was lower (79%), as was the practical action aspect for this measure (40%).</li> <li>‘Responsive and adaptive to change’: Most respondents agreed (92%) that it was important to ensure their work addresses societal needs, and 69% confirmed that they take practical steps towards this aim. This was the RRI measure with the smallest valueaction gap for respondents from the Asian and Pacific region.</li> </ul> <p> </p> <p><strong>Arab States</strong></p> <ul> <li>‘Diverse and inclusive': Respondents were most attitudinally supportive of the importance of ensuring ethical principles were applied in R&I (93%), followed by diverse perspectives (81%), and gender equality (85%). Including ethnic minorities was the area which garnered the least attitudinal support (74%). Respondents took the most practical steps towards engaging with diverse perspectives (66%), which equated to one of two equally small value-action gaps for respondents from Arab states, and the least practical steps towards inclusion of ethnic minorities (22%).</li> <li>‘Anticipative and reflective’: A high proportion of respondents (85%) agreed that it is important to ensure R&I work does not cause concerns for society. However, only 38% confirmed they had taken practical steps to ensure this.</li> <li>‘Open and transparent’: The majority of respondents agreed on the importance of keeping R&I methods open and transparent (89%), with 59% also confirming they take practical steps to do this. A majority also agreed on the importance of making the results of R&I work accessible to as wide a public as possible (90%), and 66% backed this up with practical action. Ensuring public accessibility of research results was the second of two measures with equally small value-action gaps. Attitudinal agreement on the importance of making data freely available to the public was much lower (78%), which also reflected the practical action aspect for this measure (49%).</li> <li>‘Responsive and adaptive to change’: Most respondents agreed (96%) that it was important to ensure their work addresses societal needs, and 68% confirmed that they take practical steps to achieve this.</li> </ul> <p><strong>African States</strong></p> <ul> <li>‘Diverse and inclusive': Respondents were most attitudinally supportive of the importance of ensuring engagement with diverse perspectives and expertise in R&I (91%), followed by ensuring ethical principles are applied (90%), and gender equality (89%). Including ethnic minorities was the area which garnered the least attitudinal support (74%). Respondents took the most practical steps towards ensuring ethical principles guide their work (57%), and the least towards including ethnic minorities (32%).</li> <li>‘Anticipative and reflective’: The majority of respondents (85%) agreed that it is important to ensure R&I work does not cause concerns for society, with 59% confirming that they take practical steps to ensure this.</li> <li>‘Open and transparent’: A high proportion of respondents agreed on the importance of keeping R&I methods open and transparent (90%), with 54% also confirming they take practical steps to do this. A majority also agreed on the importance of making the results of R&I work accessible to as wide a public as possible (86%), and 56% backed this up with practical action. Attitudinal agreement on the importance of making data freely available to the public was significantly lower (73%), as was the practical action aspect for this measure (38%).</li> <li>‘Responsive and adaptive to change’: Respondents mostly agreed (92%) that it was important to ensure their work addresses societal needs, and 64% confirmed that they take practical steps towards this aim. This was the RRI measure with the smallest valueaction gap for respondents from African states.</li> </ul> <p> </p> <p><em>Note: Please refer to the "RRING WP3 - Survey Data Documentation" document for detailed instructions on how to use this dataset.</em></p>
Resident survey data for Living Lab regions
<p>The datasets present collected data through resident surveys on the perceptions on tourism development and the state of cultural heritage, as part of the Horizon 2020 funded project SmartCulTour (<a href="http://www.smartcultour.eu">www.smartcultour.eu</a>).</p> <p>The data is collected <strong>on individual respondent level</strong> for Local Administrative Units (LAUs) for the following municipalities/cities:</p> <ul> <li>Spain: Huesca, Graus, Benasque, Barbastro, Ainsa, Jaca, Sariñena</li> <li>the Netherlands: Rotterdam, Dordrecht, Molenlanden, Ridderkerk, Zwijndrecht, Barendrecht, Delft</li> <li>Belgium: Dendermonde, Puurs-Sint-Amands, Bornem, Berlare, Aalst, Denderleeuw, Willebroek</li> <li>Croatia: Split, Trogir, Kaštela, Solin, Sinj, Dugopolje, Klis</li> <li>Finland: Utsjoki</li> <li>Italy: Vicenza, Caldogno, Grumolo, Pojana Maggiore, Lonigo, Montagnana</li> </ul> <p>The data is presented as cross-sectional data and available for the following <strong>year</strong>: 2020.</p> <p>Please consult the <strong>metadata</strong> on each dataset for an overview of collected indicators and units of measurement.</p>
BRAVE stakeholder survey
<p>Data set from the BRAVE stakeholder survey. A codebook ist documenting and complementing the data set.</p> <p>The BRAVE stakeholder survey was conducted within the context of the multidisciplinary research project “BRidging gaps for the adoption of Automated VEhicles” (BRAVE) funded by the EU as part of the Horizon 2020 research program. The survey took place in the seven countries of the participating project partners, i.e. in the EU countries France, Germany, Slovenia, Spain, and Sweden, as well as in Australia and the USA and took place in February and March 2020. The survey was conducted via computer assisted web interviews (CAWI).</p> <p>The aim of the study was to explore stakeholders’ expectations, concerns and needs of a large-scale introduction of automated vehicles at SAE Level 3. Applying a mixed method approach, expert interviews were conducted to design the questionnaire of the online stakeholder survey.</p> <p>The codebook of the BRAVE stakeholder survey documents the applied questionnaire and codes given in data preparation. The documentation accompanies the data set of the BRAVE stakeholder survey stored in the repository zenodo.org available under doi: 10.5281/zenodo.5495219.</p> <p>Further details on the survey and results are reported in the BRAVE Deliverable D2.2 “Report on the findings of the expert online survey”, available at https://www.brave-project.eu/wp-content/uploads/2021/03/20210225-Deliverable-D2.2-Stakeholder-survey-Final.pdf.</p>
Financing conditions of renewable energy projects – results from an EU wide survey
<p>The dataset contains data related to financing conditions and costs of capital for onshore wind, solar PV and offshore wind within the EU. It provides data on minimum, maximum and average country and technology-specific values on costs of debt, debt service coverage ratios, loan tenors, debt size, costs of equity and WACC values. The data was collected between September 2019 and April 2020.</p> <p>The data contains values for onshore wind in Austria, Belgium, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Italy, Latvia, Lithuania, Netherlands, Poland, Portugal, Romania, Spain and Sweden. Furthermore, it contains values for solar PV in Czech Republic, Estonia, France, Greece, Hungary, Latvia, Portugal, Romania, Slovakia and Spain. Finally, it also contains values for offshore wind in Belgium, France, Germany and UK. </p> <p>The PDF files are survey questionnaires that were used for the data collection. This includes 1) a survey questionnaire used in an exploratory research phase, in which we identified the most relevant research aspects related to the impacts of auctions on costs of capital and financing 2) a survey questionnaire used for the focus-group countries (Germany, Denmark, Spain, Portugal and Greece), which includes a list of more extensive qualitative questions and 3) a survey questionnaire used for the focus-group countries (all other EU member states) and which focused only on collecting the quantitative data. </p>
Supplement for Drone-based magnetic and multispectral surveys to develop a 3D model for mineral exploration at Qullissat, Disko Island, Greenland
<p>Supplement to Jackisch et al., 2021: Drone-based magnetic and multispectral surveys to develop a 3D model for mineral exploration at Qullissat, Disko Island, Greenland.</p> <p><a href="https://se.copernicus.org/articles/13/793/2022/se-13-793-2022.html">https://se.copernicus.org/articles/13/793/2022/se-13-793-2022.html</a></p> <p>Data set contains 3D model in dxf file, additional images, selected handheld spectra.</p> <p>Publication summary:</p> <p>We integrate UAS-based magnetic and remote sensing mineral exploration data with legacy exploration data of a Ni-Cu-PGE prospect on Disko Island, West Greenland. The basalt unit has a complex magnetization, and we use a 3D magnetic vector inversion on the UAS magnetics to estimate magnetic properties and spatial dimensions of the mineralized unit. Our 3D modelling reveals a horizontal sheet and a strong remanent magnetization component. We highlight the advantage of UAS in rugged terrain.</p> <p> </p>
Base rates of food safety practices in European households: Summary data from the SafeConsume Household Survey
<p>This data set contains estimates of the base rates of 550 food safety-relevant food handling practices in European households. The data are representative for the population of private households in the ten European countries in which the SafeConsume Household Survey was conducted (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, UK).</p> <p><em>Sampling design</em></p> <p>In each of the ten EU and EEA countries where the survey was conducted (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, UK), the population under study was defined as the private households in the country. Sampling was based on a stratified random design, with the NUTS2 statistical regions of Europe and the education level of the target respondent as stratum variables. The target sample size was 1000 households per country, with selection probability within each country proportional to stratum size.</p> <p><em>Fieldwork</em></p> <p>The fieldwork was conducted between December 2018 and April 2019 in ten EU and EEA countries (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, United Kingdom). The target respondent in each household was the person with main or shared responsibility for food shopping in the household. The fieldwork was sub-contracted to a professional research provider (Dynata, formerly Research Now SSI). Complete responses were obtained from altogether 9996 households.</p> <p><em>Weights</em></p> <p>In addition to the SafeConsume Household Survey data, population data from Eurostat (2019) were used to calculate weights. These were calculated with NUTS2 region as the stratification variable and assigned an influence to each observation in each stratum that was proportional to how many households in the population stratum a household in the sample stratum represented. The weights were used in the estimation of all base rates included in the data set.</p> <p><em>Transformations</em></p> <p>All survey variables were normalised to the [0,1] range before the analysis. Responses to food frequency questions were transformed into the proportion of all meals consumed during a year where the meal contained the respective food item. Responses to questions with 11-point Juster probability scales as the response format were transformed into numerical probabilities. Responses to questions with time (hours, days, weeks) or temperature (C) as response formats were discretised using supervised binning. The thresholds best separating between the bins were chosen on the basis of five-fold cross-validated decision trees. The binned versions of these variables, and all other input variables with multiple categorical response options (either with a check-all-that-apply or forced-choice response format) were transformed into sets of binary features, with a value 1 assigned if the respective response option had been checked, 0 otherwise.</p> <p><em>Treatment of missing values</em></p> <p>In many cases, a missing value on a feature logically implies that the respective data point should have a value of zero. If, for example, a participant in the SafeConsume Household Survey had indicated that a particular food was not consumed in their household, the participant was not presented with any other questions related to that food, which automatically results in missing values on all features representing the responses to the skipped questions. However, zero consumption would also imply a zero probability that the respective food is consumed undercooked. In such cases, missing values were replaced with a value of 0.</p>
Weekly CoMix contact matrices for UKHSA COVID-19 dashboard and ONS COVID-19 infection survey age-groups
<p>Weekly contact matrices calculated from data collected as part of the UK arm of the CoMix survey. All contact matrices were calculated over two survey rounds (SR) of data to account for alternating panels (the indicated SR and the previous SR). Full details of composition can be found in Munday et. al. [1]. Contact matrices are provided for age-groups consistent with publicly available case data from the UKHSA COVID-19 dashboard <strong> (0-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70+) </strong>and publicly available aggregates of infection and antibody prevalence from the ONS COVID-19 infection survey <strong>(2-10, 11-15, 16-24, 25-34, 35-49, 49-69 and 70+)</strong>. The data is provided in qs files as 1000 bootstrapped samples of each contact matrix for weekly 'survey rounds' between 19 and 94 (see directory "survey_round_dates.csv"). The files that begin with UKHSA contain the contact matrices for the age stratification of the UKHSA COVID-19 dashboard case data. The files that begin with ONS contain the contact matrices for the age stratification of the ONS COVID-19 infection survey. </p> <p>Ethics: The study and method of informed consent were approved by the ethics committee of the London School of Hygiene & Tropical Medicine (LSHTM; reference number 21795).</p> <p>1. Munday, J.D., Jarvis, C.I., Gimma, A. <em>et al.</em> Estimating the impact of reopening schools on the reproduction number of SARS-CoV-2 in England, using weekly contact survey data. <em>BMC Med</em> <strong>19</strong>, 233 (2021). https://doi.org/10.1186/s12916-021-02107-0</p>
Small mammal survey in Narati (China) and Sary-Mogul (Kyrgyzstan)
<p>Field surveys were conducted in September 2006 at Narati (China) and September 2014 at Sary Mogul (Kyrgyzstan) using trapping and transect methods. See <a href="https://dataosu.obs-besancon.fr/FR-18008901306731-2015-08-06-20">dat@osu</a> for general metadata and Marston et al. (2022), cited below (submitted) for sampling details.</p> <p><a href="https://zenodo.org/record/7413284/files/Narati_transect.csv?download=1">Narati_transect.csv</a> : transects of over 41 km were surveyed in grassland areas between 1509-3335 m altitude. Each row corresponds to an approximatively 130 m segment, divided into 20 intervals of ten paces. Activity indicators identifiable to species or genus level (including foraging corridors, ground holes, earth tumuli and small mammal faeces) were recorded. The abundance index is the number of intervals where presence indicators were observed divided by the number of intervals walked (n = 20). The geographical coordinates are the centroïd of each segment.</p> <p><a href="https://zenodo.org/record/7413284/files/Narati_traplines.txt?download=1">Narati_traplines_rd.txt</a>: small mammal relative densities obtained using standard trapping as described in Marston et al. (2022).</p> <p><a href="https://zenodo.org/record/7413284/files/Sary_Mogul_transect.csv?download=1">Sary_Mogul_transect.csv</a>: For each transect, 20 intervals of 10 paces were surveyed with activity indicators identifiable to species or genus level (including foraging corridors, ground holes, earth tumuli and small mammal faeces) recorded. Relative density scores of small mammal presence (the number of intervals where presence indicators were observed) were produced for each species for each transect. In Sary Mogul, field surveys comprised 37 transects as described in <a href="https://doi.org/10.3390/rs11010039">Marston and Giraudoux (2019</a>)</p> <p><a href="https://zenodo.org/record/7413284/files/Sary_Mogul_traplines_rd.txt?download=1">Sary_Mogul_traplines_rd.txt</a>: small mammal relative densities obtained using standard trapping as described in Marston et al. (2022).</p>
Data of Survey on National Contributions to EOSC 2021
<p>This is the data set from the EOSC Steering Board pilot survey on National Contributions to EOSC 2021</p> <p>The annual survey on National Contributions to EOSC monitors policies, practices, and impacts related to EOSC and Open Science at national and institutional levels in Europe</p> <p>The pilot survey for 2021 was published and the data was collected and is now available in the online dashboard of the EOSC Observatory: [<a href="https://eoscobservatory.eosc-portal.eu">https://eoscobservatory.eosc-portal.eu</a>]</p> <p>This data set has removed confidential information on financial estimations in questions 4-14 and also descriptions of use cases in question 19 which will be presented in an EOSC Catalogue of Best Practices</p>
Dataset - AHRC survey of digital/software requirements survey 2021
<p>This is the data collected from the SSI survey of digital/software requirements run for the AHRC in 2021.</p> <p>Personally Identifiable Information (names, email addresses) have been removed, as have other information to minimise the chance of deductive disclosure (job role, institution).</p>
Weed surveys in Oilseed rape fields in the LTSER Zone Atelier Plaine & Val de Sèvre
<p>Dataset used in Berquer, A.; Martin, O.; Gaba, S. Landscape Is the Main Driver of Weed Assemblages in Field Margins but Is Outperformed by Crop Competition in Field Cores. Plants 2021, 10, 2131. https://doi.org/10.3390/plants10102131</p> <p>Because there personnal and sensitive data, agricultural practices data are not given. Please contact Sabrina Gaba or Vincent Bretagnolle if you are interested in a collaborative project.</p>
ELWar Expert Survey Trend File
<p>This repository contains the combined data from the 2018, 2020, and 2022 ELWar expert surveys on parties’ political ideology, policy positions, and behavior in six countries in South-East Europe: Bosnia and Herzegovina, Croatia, Kosovo, North Macedonia, Montenegro, and Serbia. The survey is part of the project “Electoral Legacies of War: Political Competition in Postwar Southeast Europe" (ELWar).</p>
UKRI Digital Research Infrastructure Mapping Survey Dataset (for Net Zero Scoping Project)
<p>This dataset was generated as an output for the DRI Mapping exercise carried out during the UKRI Net Zero Digital Research Infrastructure (DRI) Scoping Project undertaken from 2021-2023. The "README.md" provides more information about the dataset and how to use it.</p> <p>The report associated with this dataset is available at:</p> <p>https://doi.org/10.5281/zenodo.7805987</p>
A dataset from a survey investigating disciplinary differences in data citation
<p><strong>GENERAL INFORMATION</strong></p> <p><em>Title of Dataset: </em> A dataset from a survey investigating disciplinary differences in data citation</p> <p><em>Date of data collection:</em><strong> </strong>January to March 2022</p> <p><em>Collection instrument: </em>SurveyMonkey</p> <p><em>Funding:</em> Alfred P. Sloan Foundation</p> <p><br> <strong>SHARING/ACCESS INFORMATION</strong></p> <p><em>Licenses/restrictions placed on the data: </em>These data are available under a <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0 license</a> </p> <p><em>Links to publications that cite or use the data: </em></p> <p>Gregory, K., Ninkov, A., Ripp, C., Peters, I., & Haustein, S. (2022). Surveying practices of data citation and reuse across disciplines. Proceedings of the 26th International Conference on Science and Technology Indicators. <em>International Conference on Science and Technology Indicators</em>, Granada, Spain. https://doi.org/10.5281/ZENODO.6951437</p> <p>Gregory, K., Ninkov, A., Ripp, C., Roblin, E., Peters, I., & Haustein, S. (2023). <em>Tracing data:<br> A survey investigating disciplinary differences in data citation.</em> Zenodo. https://doi.org/10.5281/zenodo.7555266</p> <p><br> <strong>DATA & FILE OVERVIEW</strong></p> <p><em>File List</em></p> <ul> <li>Filename: MDCDatacitationReuse2021Codebookv2.pdf<br> <em>Codebook</em></li> <li>Filename: MDCDataCitationReuse2021surveydatav2.csv<br> <em>Dataset format in csv</em></li> <li>Filename: MDCDataCitationReuse2021surveydatav2.sav<br> <em>Dataset format in SPSS</em></li> <li>Filename: MDCDataCitationReuseSurvey2021QNR.pdf<br> <em>Questionnaire</em></li> </ul> <p><em>Additional related data collected that was not included in the current data package: </em>Open ended questions asked to respondents</p> <p><br> <strong>METHODOLOGICAL INFORMATION</strong></p> <p><em>Description of methods used for collection/generation of data: </em></p> <p>The development of the questionnaire (Gregory et al., 2022) was centered around the creation of two main branches of questions for the primary groups of interest in our study: researchers that reuse data (33 questions in total) and researchers that do not reuse data (16 questions in total). The population of interest for this survey consists of researchers from all disciplines and countries, sampled from the corresponding authors of papers indexed in the Web of Science (WoS) between 2016 and 2020. </p> <p>Received 3,632 responses, 2,509 of which were completed, representing a completion rate of 68.6%. Incomplete responses were excluded from the dataset. The final total contains 2,492 complete responses and an uncorrected response rate of 1.57%. Controlling for invalid emails, bounced emails and opt-outs (n=5,201) produced a response rate of 1.62%, similar to surveys using comparable recruitment methods (Gregory et al., 2020).</p> <p><em>Methods for processing the data: </em></p> <p>Results were downloaded from SurveyMonkey in CSV format and were prepared for analysis using Excel and SPSS by recoding ordinal and multiple choice questions and by removing missing values.</p> <p><em>Instrument- or software-specific information needed to interpret the data: </em></p> <p>The dataset is provided in SPSS format, which requires IBM SPSS Statistics. The dataset is also available in a coded format in CSV. The Codebook is required to interpret to values.</p> <p><br> <strong>DATA-SPECIFIC INFORMATION FOR: MDCDataCitationReuse2021surveydata</strong></p> <p><em>Number of variables:</em> 95</p> <p><em>Number of cases/rows:</em> 2,492</p> <p><em>Missing data codes:</em> 999 Not asked</p> <p>Refer to MDCDatacitationReuse2021Codebook.pdf for detailed variable information.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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