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.
557
datasets available to search
ShareScore release 0.9.0
Dataset results
557 results for “data reporting”
META-DATA for IEA Wind Task 46 report: Atmospheric drivers of wind turbine blade leading edge erosion: Hydrometeors
<p>The objectives of the work summarized in the report that accompanies this dataset are to:</p> <ul> <li>Describe crucial meteorological parameters for wind turbine blade leading edge erosion</li> <li>Describe technologies appropriate to measurement of hydroclimates and specifically hydrometeor size distributions and phase</li> <li>Identify available data sets that are available to describe hydrometeor size distributions and phase and generate meta-data for data sets available for use in mapping wind turbine blade leading edge erosion potential. This dataset summarizes those meta-data. </li> <li>Identify priority geographic areas for geospatial mapping of wind turbine blade leading edge erosion potential <p> </p> </li> </ul>
Data and R code - ISRR11/Rooting2021 Meeting Report
<p>This repository contains the raw data and R code used to analyse the results of the online root phenotyping survey that was created and disseminated by ISRR Ambassadors during the ISRR11/Rooting2021 meeting.</p>
Excel mapping tools for 2021 zoonoses data reporting
<p>The main objective of the mapping tools is to provide a simple and useable platform for Member states and other reporting countries to map their country-specific standard terminology to that used by EFSA and to enable the production of an XML file for the submission of sample or aggregated-based zoonoses monitoring data via the Data Collection Framework (DCF).</p> <p>The catalogues and the specific hierarchy of each data model (PRV, FBO, AP, DS and SSD2 for <em>Echinococcus multilocularis</em>) are already inserted into each of the specific mapping tool. Specific Excel mapping tools corresponding to each of the five data models are available.</p> <p>Dynamic or manual version of the tool can be chosen for the first four data models.</p> <p>The Emulti_SSD2_tool can be used to report sample-based results of <em>Echinococcus multilocularis</em> under the Commission Delegated Regulation (EU) 2018/772.</p>
Catalogues for 2021 zoonoses data reporting
<p>The EFSA catalogues applied for the 2021 zoonoses data reporting are made available in Excel format for easier use and reference.</p>
Accompanying data for the report data.europa.eu and Citizen-generated Data: Opportunities and challenges associated to the inclusion of citizen-generated data in data.europa.eu
<p>Accompanying data for the report data.europa.eu and Citizen-generated Data: Opportunities and challenges associated to the inclusion of citizen-generated data in data.europa.eu. This includes the catalogue of papers that have been revised in the course of generating this report, as well as a file with the analysis of datasets from different open data portals in Europe, with respect to their inclusion of Citizen-Generated Data.</p>
Raw EEG data for the experiment reported in "Understanding the effects of constraint and predictability in ERP"
<p>This repository contains the raw EEG data for the above-named paper. Preprocessing scripts are stored at: <a href="http://osf.io/fndk5/">https://osf.io/fndk5/</a></p> <p>The raw EEG data are the files *.eeg, *.vmrk and *.vhdr (BrainVision format EEG data). The numeric prefix indicates the participant ID. All three files must be stored in the same directory to work with the preprocessing script. Individual participant log files from the experimental presentation paradigm are stored in the zipped subdirectory opensesame_logs.zip. To work with the preprocessing script, these must be unzipped into a folder called opensesame_logs, stored in the folder containing the raw EEG files.</p> <p>A repository of intermediate preprocessing files based on the raw data is at <a href="http://zenodo.org/record/7002697">https://zenodo.org/record/7002697</a>.</p> <p>Note that the following raw files are included in the dataset for transparency but were not preprocessed for the final analysis: subject 15 due to a recording software crash mid-experiment, and subjects 40:43 as their EEG data were corrupted.</p>
[SUPERCEDED] Preprocessed EEG data for the experiment reported in "Understanding the effects of constraint and predictability in ERP"
<p>This repository contains <strong><em>an outdated version of</em></strong> intermediate preprocessing files for the above-named paper. Please see the current version here: <a href="https://zenodo.org/record/7334782">https://zenodo.org/record/7334782</a></p>
Data reported in development and cross-validation of a veterans mental health risk factor screen
<p>Background. VA primary care patients are routinely screened for current symptoms of PTSD, depression, and alcohol disorders, but many who screen positive do not engage in care. In addition to stigma about mental disorders and a high value on autonomy, some veterans may not seek care because of uncertainty about whether they need treatment to recover. A screen for mental health risk could provide an alternative motivation for patients to engage in care.</p> <p>Results. Twelve items assessing dissociation, emotional lability, life stress, and moral injury correctly classified 86% of those who later had elevated PTSD and/or depression symptoms (sensitivity) and 75% of those whose later symptoms were not elevated (specificity). Performance was also very good for 110 veterans who identified as members of ethnic/racial minorities.</p> <p>Conclusions. Mental health status was prospectively predicted in VA primary care patients with high accuracy using a screen that is brief, easy to administer, score, and interpret, and fits well into VA's integrated primary care. When care is readily accessible, appealing to veterans, and not perceived as stigmatizing, information about mental health risk may result in higher rates of engagement than information about current mental disorder status.</p>
Text Mining of Archaeological Reports for Urban farming (data and code)
<p>This release is created to create a DOI in Zenodo for the data related to Fischer, AD, van Londen, H, Blonk-van den Bercken, AL, Visser, RM and Renes, J. 2021. Urban farming and ruralisation in the Netherlands (1250 up tot the nineteenth century), unravelling farming practice and the use of (open) space by synthesising archaeological reports using text mining. Nederlandse Archeologische Rapporten 68. Amersfoort: Rijksdienst voor het Cultureel Erfgoed. <a href="https://www.cultureelerfgoed.nl/publicaties/publicaties/2021/01/01/urban-farming-and-ruralisation-in-the-netherlands">https://www.cultureelerfgoed.nl/publicaties/publicaties/2021/01/01/urban-farming-and-ruralisation-in-the-netherlands</a></p>
Supplemental data for the report "Optimisation of lattice simulations energy efficiency"
<p>Supplemental data for the report <a href="http://doi.org/10.5281/zenodo.7057319">"Optimisation of lattice simulations energy efficiency"</a>. Also available as a <a href="https://git.dev.dirac.ed.ac.uk/portelli/tursa-energy-efficiency">git repository</a>.</p> <p>It contains:</p> <ul> <li>Full copy of benchmark run directories</li> <li>Power monitoring scripts</li> <li>Power monitoring raw measurements</li> <li>Power monitoring data analysis and results used in the report</li> </ul> <p>For a more complete description, please see the README.md file.</p>
Occupational and environmental diseases by operating results report of 43 files from Health Data Center (HDC)
<p>Data used in this study were from the Health Data Center (HDC); permission to use these data can be requested from the Health Data Center (HDC), Ministry of Public Health Thailand. <br> This study got approval from the Health Data Center (HDC)(reference no.0212-78) to use the data on pesticide pollutions (insecticide and herbicide) in provinces years 2018-2020 excluding Bangkok because data from Bangkok were not reported on HDC. Included pesticide poisoning from DIAGNOSIS_OPD and DIAGNOSIS_IPD files which used to DIAGCODE were 'T600','T601','T602','T603','T604','T608','T609' without the X68 code. </p> <p><strong>File descriptions</strong>: The pesticide pollutions (pesticide-induced, insecticide, and herbicide) in provinces years 2018-2020:<br> cwt : post code <br> cwt_n : province names<br> 63_pop : population in 2020<br> N63_pesticide : pesticide-induced patients in 2020<br> P63_pesticide : prevalence of pesticide-induced patients in years 2020<br> N63_Insecticide : insecticide patients in 2020<br> P63_Insecticide : prevalence of insecticide patients in years 2020<br> N63_Herbicide : herbicide patients in 2020<br> P63_Herbicide : prevalence of herbicide patients in years 2020<br> N63_Other : other pesticide patients in 2020<br> P63_Other : prevalence of other pesticide patients in years 2020<br> 62_pop : population in 2019<br> N62_pesticide : pesticide-induced patients in 2019<br> P62_pesticide : prevalence of pesticide-induced patients in years 2019<br> N62_Insecticide : insecticide patients in 2019<br> P62_Insecticide : prevalence of insecticide patients in years 2019<br> N62_Herbicide : herbicide patients in 2019<br> P62_Herbicide : prevalence of herbicide patients in years 2019<br> N62_Other : other pesticide patients in 2019<br> P62_Other : prevalence of other pesticide patients in years 2019<br> 61_pop : population in 2018<br> N61_pesticide : pesticide-induced patients in 2018<br> P61_pesticide : prevalence of pesticide-induced patients in years 2018<br> N61_Insecticide : insecticide patients in 2018<br> P61_Insecticide : prevalence of insecticide patients in years 2018<br> N61_Herbicide : herbicide patients in 2018<br> P61_Herbicide : prevalence of herbicide patients in years 2018<br> N61_Other : other pesticide patients in 2018<br> P61_Other : prevalence of other pesticide patients in years 2018</p> <p>P3y_pesticide : average prevalence of pesticide-induced patients from 2018 to 2020<br> P3y_Insecticide : average prevalence of insecticide patients from 2018 to 2020<br> P3y_Herbicide : average prevalence of herbicide patients from 2018 to 2020<br> P3y_Other : average prevalence of other pesticide patients from 2018 to 2020</p>
Patient-reported outcomes via electronic health record portal vs. telephone: process and retention data in a pilot trial of anxiety or depression symptoms in epilepsy
<p>Objective: To close gaps between research and clinical practice, tools are needed for efficient pragmatic trial recruitment and patient-reported outcome(PROM) collection. The objective was to assess feasibility and process measures for patient-reported outcome collection in a randomized trial comparing electronic health record(EHR) patient portal questionnaires to telephone interview among adults with epilepsy and anxiety or depression symptoms.</p> <p>Results: Participants were 60% women, 77% White/non-Hispanic, with mean age 42.5 years. Among 15 individuals randomized to EHR portal, 10(67%, CI 41.7-84.8%) met the 6-month retention endpoint, versus 100%(CI 79.6-100%) in the telephone group(p=0.04). EHR outcome collection at 6 months required 11.8 minutes less research staff time per participant than telephone (5.9, CI 3.3-7.7 vs. 17.7, CI 14.1-20.2). Subsequent telephone contact after unsuccessful EHR attempts enabled near complete data collection and still saved staff time.</p> <p>Discussion: Data from this randomized pilot study of pragmatic outcome collection methods for patients with anxiety or depression symptoms in epilepsy includes baseline participant characteristics, recruitment flow resulting from a novel EHR-based, care-embedded recruitment process, and data on retention along with various process measures at 6-months.</p>
Laboratory data complementing the annual report on the epidemiological analyses of African swine fever (ASF) in the European Union - North Macedonia
<p>This dataset contains ASF laboratory analytical results in domestic pigs and wild boar.</p> <p><strong>Reporting authorities contributing to the data collection:</strong></p> <ul> <li>ASF2023_MK - Food and Veterinary Agency (FVA)</li> <li>ASF2022_MK - Food and Veterinary Agency (FVA)*</li> <li>ASF2022_MK - Food and Veterinary Agency (FVA)</li> </ul> <p> </p> <p> </p> <p>*This version of the ASF laboratory data has been republished with the subunit identification code (sampUnitIds.subUnitId) column empty due to data protection reasons</p>
Data and analysis for "Association of self-reported musculoskeletal pain with school furniture suitability and daily activities among primary school and university students"
<p>Datasets and R analysis code and report for the article "Association of self-reported musculoskeletal pain with school furniture suitability and daily activities among primary school and university students".</p>
FIGURE 10 in An analysis of fossil identification guides to improve data reporting in citizen science programs
FIGURE 10. An example of a †Cosmopolitodus hastalis photo enhanced by illustration.
Figure 2 in New data on Ovalisia (Palmar) festiva (Linnaeus) (Coleoptera: Buprestidae) and its natural enemies reported from Bulgaria
Figure 2. Ovalisia festiva, adults (a); exit hole (b).
Excel mapping tools for 2017 zoonoses data reporting
<p>The main objective of the mapping tool is to provide a simple and useable platform for MSs to map their country-specific standard terminology to that used by EFSA and to enable the production of an XML file for the submission of sample or aggregated-based zoonoses monitoring data via the DCF.</p> <p>The catalogues and the specific hierarchy of each data model (AMR, ESBL, PRV, FBO, POP and DST) are already inserted into each of the specific mapping tool. Specific Excel mapping tools correspond to each of the six data models are available.</p> <p>You can choose between the dynamic or the manual version of the tool.</p>
Supporting publication for 'Guidelines for reporting 2017 prevalence sample-based data in accordance with SSD2 data model'
<p>These two Excel documents help you to map terms from the matrix catalogue ZOO_CAT_MATRIX used in the aggregated prevalence data model to FoodEx2 codes and offer you examples on how prevalence data can be reported using SSD2.</p>
Coral growth data from Dongsha Atoll - published in DeCarlo et al. (2017) in Scientific Reports
<p>Coral growth data (annual extension, density, and calcification) for massive <em>Porites</em> corals from the eastern reef flat and lagoon of Dongsha Atoll, South China Sea. The data are derived from computed tomography (CT) scans of coral skeletons. Please cite the following paper when using these data:</p> <p>DeCarlo, T. M. <em>et al</em>. Mass coral mortality under local amplification of 2 °C ocean warming. <em>Sci. Rep</em> <strong>7</strong>, 44586 (2017).</p>
MarTREC Data Set for Report: Economic Impact of the GIWW on the States It Serves
<p>Data set used for the report, "Economic Impact of the Gulf Intracoastal Waterway on the States It Serves". The purpose of the report was to examine the economic impact of the GIWW on the five states it serves: Texas, Louisiana, Mississippi, Alabama, and Florida. This project involved several research tasks. First, researchers estimated the economic impact of the GIWW by looking specifically at the impact of the commodities using the GIWW in the relevant coastal counties in each of these five states. Once this task was completed, researchers then estimated the impact of the GIWW on other modes, modeling the possible adverse impacts that would result if the GIWW were to become permanently unavailable and shippers would instead have to use the next economically feasible transportation mode. Researchers examined the cost of shifting additional traffic to other modes of transportation.</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.