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.
1,069
datasets available to search
ShareScore release 0.9.0
Dataset results
1,069 results for “Data managment”
Data Management Plan for PsyCoMed
<p>Ongoing Data management plan for PsyCoMed 14-06-2024</p>
eELib: Open-Source Model Library for Prosumer Power Systems and Energy Management Strategies (data)
<p>Dataset and results used for the simulations in following publication:</p> <p>Carsten Wegkamp, Henrik Wagner, Eike Niehs, Julien Essers, Marcel Lüdecke, Mattias Hadlak, Bernd Engel:<br>"<strong>eELib: Open-Source Model Library for Prosumer Power Systems and Energy Management Strategies</strong>",<br>Open Source Modelling and Simulation of Energy Systems (OSMSES) 2024, Vienna, Austria, 2024</p> <p> </p> <p>This contains the input (scenario) files for the building & grid scenario and the results of the two simulations.<br>It uses the elenia Energy Library (eELib) with release version 1.0.0: https://gitlab.com/elenia1/elenia-energy-library</p>
FAIRmat Tutorial 13: NOMAD for Experimental Data Management in Synthesis
<p>FAIRmat Tutorial 13, presented by <a href="https://www.fairmat-nfdi.eu/fairmat/areas-fairmat/area-a-fairmat" target="_blank" rel="noopener">FAIRmat Area A Synthesis</a>, introduces <a href="https://nomad-lab.eu/nomad-lab/" target="_blank" rel="noopener">NOMAD</a> and <a href="https://nomad-lab.eu/nomad-lab/nomad-oasis.html">NOMAD Oasis</a> as essential tools for research data management (RDM). This tutorial will specifically demonstrate how to utilize these tools for managing experimental materials science data, with a particular focus on synthesis data.</p> <p>Participants will learn about NOMAD's versatile data model, which ensures data interoperability, and its various types and levels of schemas, including custom yaml schemas, community standards, plugins, and BaseSections. The tutorial will also cover the integration of NOMAD with Electronic Lab Notebooks (ELNs) to enhance data documentation and management. A practical session will guide users through a typical synthesis data example, demonstrating how to start from NOMAD's built-in ELNs, develop a data schema, convert it into a NOMAD plugin for automated data processing, and deploy the schema on a local NOMAD Oasis. This hands-on approach will provide invaluable insights into customizing NOMAD to fit specific experimental workflows.</p> <p>The session is designed to serve various user groups, including standard users, data stewards & data scientists, and system administrators, ensuring that each participant gains a comprehensive understanding of the tool's capabilities and applications in their respective roles. Join us to explore how NOMAD can transform your approach to data management in experimental synthesis, leading to more efficient and coherent research outputs - FAIR principles in practice.</p> <p> </p> <p>Disclaimer: NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
FAIRmat Tutorial 11: Research data management, from fundamentals to implementation
<p>Scientific data are the key outcome of research activities at universities, research institutes, and industrial R&D centers. With the recent surge in research data production driven by high-throughput techniques, and the digitalization of scientific methods, data management has become more critical than ever. Moreover, multidisciplinary collaborative research requires the seamless exchange of research data between team members and different laboratories.</p> <p>To ensure that these vast amounts of diverse research data are handled effectively and result in valuable human knowledge and discoveries, it is imperative that proper data management practices are implemented. This ensures that data are produced in a high-quality, reproducible, and well-documented manner so that they can be machine-actionable and reusable in the future. </p> <p>Research Data Management (RDM) refers to the practices used in handling scientific data throughout their lifecycle, from collection to reuse.</p> <p>In this interactive tutorial, we will explain the different stages of the research data lifecycle and identify the best practices for each stage. We will explore the FAIR data principles —Findable, Accessible, Interoperable, and Reusable— and provide insights into their implementation during the research process. In addition, we will introduce the concept of data management plans, which define the data management process during research projects, along with practical tips on the various components and how to comply with funder requirements. </p> <p>The tutorial is divided into two sessions:</p> <ol> <li>Basic concepts and definitions of FAIR data and RDM</li> <li>Practical tips and examples for applying best practices in RDM</li> </ol> <p>After completing this tutorial, you will be able to: </p> <ul> <li>Describe the FAIR principles and understand the different aspects and implementations of FAIR data.</li> <li>Describe and justify measures for good RDM at different stages of the data lifecycle.</li> <li>Understand and explain the concepts of persistent identifiers (PID), metadata, data storage systems, etc. </li> <li>List and understand the contents of the components of a DMP according to the requirements of different funders in Europe. </li> <li>Identify the tools available to create a DMP.</li> </ul>
FAIRmat Tutorial 12: Getting started with NOMAD and NOMAD Oasis for research data management (RDM)
<p>In this online tutorial we will cover the first steps with NOMAD and NOMAD Oasis. We will briefly cover the core NOMAD functionality on exploring, uploading, sharing and publishing data with NOMAD. We will then explore options for creating your own schemas and plugins to support new file formats and create custom electronic lab notebooks (ELNs), we show ways to customize an NOMAD Oasis, and how to contribute to the development of NOMAD and its ecosystem.</p> <p>The tutorial includes an introduction talk about NOMAD and FAIRmat, including the latest changes and features in NOMAD. This is followed by a practical follow along session, where we go through a Jupyter notebook that demonstrates how to use NOMAD for managing custom data and file types. Based on a simple given dataset, we show how to model the data in a schema, do parsing and normalization, process data, access existing data with NOMAD's API for analysis, and how to add visualization to your data.</p> <p> </p> <p><strong>Disclaimer: </strong>NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
Data from a study on the role of the American toad (Anaxyrus americanus) in vegetable pest management in Pennsylvania
<p>Datasets associated with a manuscript on the role of the American toad (<em>Anaryxus americanus</em>) in pest management in vegetable production in Pennsylvania, USA. The datasets derive from three distinct studies that shed light on the ecological role of the toad: (1) gut contents of field-collected individuals from a small diversified vegetable farm, (2) outcomes from a laboratory experiment on toad foraging behavior toward a range of prey, and (3) outcomes from a field mesocosm experiment in which different densities of toads were enclosed with kale plants. </p> <p>Please see the file <code>toad_column_key.xlsx</code> for the definitions of fields in all data files. </p>
Effect size data for the meta-analysis article ""Effects of vegetation management intensity on biodiversity and ecosystem services in vineyards: a meta-analysis"
<p>This Exel file includes the effect size dataset used for the statistical analysis for the paper "Effect of vegetation management intensity on biodiversity and ecosystem services in vineyards: a meta-analysis", which will be published in the Journal of Applied Ecology in 2018.</p> <p>This meta-analysis was conducted in the course of the project VineDivers (<a href="http://www.vinedivers.eu/">www.vinedivers.eu</a>) funded through the 2013-2014 BiodivERsA/FACCE-JPI joint call for research proposals, with the national funders: Austrian Science Fund (FWF), Spanish Ministry for Economy and Competitiveness (MINECO), French National Research Agency (ANR), Romanian Executive Agency for Higher Education, Research, Development and Innovation Funding (UEFISCDI) and Federal Ministry of Education and Research (BMBF/Germany). P. Batáry was supported by the German Research Foundation (DFG BA4438/2-1) and by the Economic Development and Innovation Operational Programme of Hungary (GINOP–2.3.2–15–2016–00019).</p> <p> </p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: LPJmL managed grass
<p>This is model output from LPJmL for managed grass as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
Research Data Management - Persistent identifier (Video)
<p>Video of our latest presentation for the training seminars in Research Data Management of the FoDaKo-project: <a href="https://fodako.de/">https://fodako.de</a></p>
Research Data Management – Basics (Video)
<p>Video of our latest presentation for the training seminars in Research Data Management of the FoDaKo-project: <a href="https://fodako.de/">https://fodako.de</a></p>
Code and data for "Optimising habitat management for amphibians: from simple models to complex decisions"
<p>The zip archive contains code and data to reproduce the analysis contained in the following manuscript:</p> <p>Scroggie, M.P., Preece, K., Nicholson, E., McCarthy, M.A., Parris, K.M. and Heard, G.W. Optimising habitat management for amphibians: from simple models to complex decisions.</p> <p>Included in the archive is the source code of two R packages (METAPOP, and METAPOPPLAN), which must first be installed, along with their various dependencies which include Rcpp, RcppArmadillo, sp, spdep and rgeos. As package METAPOPPLAN contains C++ code, installation requires the presence of the appropriate C++ compilers and other software development tools. These should be available or easily installable on Linux or other Unix based systems, but Microsoft Windows users must first install the appropriate version of Rtools, which can be downloaded from https://cran.r-project.org/bin/windows/Rtools/.</p> <p>With all appropriate packages installed, the analysis can be replicated by running the included Makefile.</p> <p>Total execution time will be quite long, due to the large number of simulations that must be run. On my Windows system, with 12 cores and 4GB of RAM, execution took approximately 10 days. The code will run much faster if the various management scenarios included in the analysis are executed in parallel. This can be done by executing make with a -j argument specifying the number of cores to utilise. For example, if your system has 12 cores, invoke *make* as follows:</p> <p>make -j 12</p> <p>Overall execution time will scale roughly with the number of available cores, up to a maximum of 24 (the total number of management scenarios).<br> </p>
Illustrative Darwin core archive to output data from a citizen science platform to a collection management system
<p>Illustrative DwC archive to send data back to a collection management system from a citizen sciences platform. This illustrative archive displays the specimens used for the trans-institutional and trans-platform pilot project held in the frame of ICEDIG.</p> <p>Further description of its content in the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
Research Data Management – Reuse of Research Data (Video)
<p>Video of our latest presentation for the training seminars in Research Data Management of the FoDaKo-project: <a href="https://fodako.de/">https://fodako.de</a></p> <p> </p> <p>Slides can be found here: <a href="https://doi.org/10.5281/zenodo.3269237">10.5281/zenodo.3269237</a></p>
Prioriteringsscore op basis van weging aantal stemmen - bijlage bij: Landelijk Coördinatiepunt Research Data Management (LCRDM) Positioning paper voor 2019 en verder
<p>Bijlage bij Positinioningpaper (juni 2019) https://doi.org/10.5281/zenodo.3266833</p>
Demo-Dataset for publication "FAIR workflows in Earth system modelling: a use case with semantic data management"
<p>This demodataset is intended to be used to test the workflow described in the publication by Lennartz & Schlemmer "FAIR workflows in Earth System modelling: a use case with semantic data management". It contains example model output for an arbitrary biogeochemical model tracer (here: dissolved organic carbon, DOC) from an ocean model as a 4-dimensional dataset (latitude, longitude, depth, time), the corresponding grid point locations as well as a textfile specifying parameter inputs for the model. The file structure is adapted for seamless integration into the workflow described in Lennartz & Schlemmer, which builds on the open source semantic research data management system LinkAhead. The dataset contains the following structure: The folder DataAnalysis stores data required for data analysis, such as the grid point locations in the file TMM_grid_v2018a.mat. The folder SimulationData stores model output in the folder 2022_TMM, containing the parameter input file nl_in.txt and the model output TR_monthly.mat. Related instructions can be accessed here: https://gitlab.com/salexan/fairworkflows-demodataset .</p>
D1.2 Data Management Plan (DMP)
<p>This deliverable constitutes the Data Management Plan (DMP) of the NANOFACTS project. It is the first version of DMP, describing data management life cycle for all research and publication data. This deliverable is introducing ways to produce, collect, process, save, exchange, share, and publish research data in line with the EC “Guidelines on FAIR Data Management in Horizon 2020”.</p>
Data analysis & code: Quantifying the impact of climate change and forest management on Swedish forest ecosystems using the dynamic vegetation model LPJ-GUESS
<p><span>This file contains code to optimize the allometric parameters, to plot the figures, and details of the underlying data analysis in "Quantifying the impact of climate change and forest management on Swedish forest ecosystems using the dynamic vegetation model LPJ-GUESS" (Bergkvist et al.). <br></span></p>
Species functional data and species distribution model projections for future land-use and fire management scenarios in the Transboundary Biosphere Reserve Gerês-Xurés
<p>The data includes nine functional traits and species distribution model projections for 102 species of vertebrates (amphibians, birds, and reptiles) in the Transboundary Biosphere Reserve Gerês-Xurés. The model projections are available for 2050 under six different land-use and fire management scenarios, namely two land-use scenarios of “business-as-usual” (BAU; ongoing trends of land abandonment) and “High Nature Value farmlands” (HNV), each under three fire management scenarios (low suppression - LS, current fire suppression - CS, and high fire suppression - HS). The species distribution projections for each scenario are presented as matrices of species presences/absences, obtained after reclassifying consensus predictions of species distribution models.</p>
Data from: Direct and indirect effects of landscape and field management intensity on carabids through trophic resources and weeds
<p>Carabids are important biological control agents of weeds and other pests in agricultural fields. The carabid community is built upon direct and indirect ecological effects of landscape complexity, field management intensity and biotic components that in interaction make any prediction of community size and composition challenging.</p> <p>We analyse a large-scale sample of 60 European cereal fields using Structural Equation Modelling to quantify the direct effects of field management intensity and the surrounding landscape, and their indirect effect via biotic components, on carabid diversity.</p> <p>Our results highlight that direct and indirect effects of increasing landscape complexity, mediated by trophic resources, mainly affect carabids positively. Field management intensity only ever affects carabids through indirect effects that are generally negative, by suppressing standing weeds and weed seeds.</p> <p>Indirect effects on granivore carabid species depended on weed seed availability whereas omnivores depended on the availability of both weed seeds and animal prey.</p> <p><i>Synthesis and applications</i><span>: A consideration of both the direct and indirect effects of landscape and field management is necessary for predicting carabid communities and with interactions. These effects, mediated via trophic resources, supports the diversity and abundance of carabid communities and their provision of ecosystem services. Our results show that promoting crop diversity and connectivity to semi-natural habitats will directly enhance carabid communities in farmland by manipulating their migration from source habitats. A reduction in field management intensity will preserve local standing weeds and weed seeds, and indirectly support carabid communities. These local and landscape modifications could contribute to improve the natural regulation of pests and weeds by carabids.</span></p>
Data from: Simulating effects of fitness and dispersal on the use of Trojan sex chromosomes for invasive species management
<ol> <li>The use of Trojan Y Chromosomes (TYC) for controlling invasive species involves manipulating the sex chromosomes of captive-raised individuals. Once released, the offspring of these individuals consist of only one sex, thereby skewing the sex-ratio of the invasive population and potentially leading to eradication. Simulation models are needed that can inform managers on how to maximize the likelihood of species eradication, since implementation of this novel management approach in the field is still rare.</li> <li>Here, we present the first spatially explicit, mechanistic simulation model of a real-world TYC program for invasive species eradication. Using a brook trout (Salvelinus fontinalis) system model, we investigated the effects of competitive and reproductive fitness of the captive-raised YY males, dispersal behavior upon their release, and landscape heterogeneity on eradication success.</li> <li>Likelihood of eradication was dependent on both the competitive and reproductive fitness of the Trojan individuals. Competitive fitness (i.e., survival) had a higher threshold for eradication, below which populations failed to be eradicated.</li> <li>Movement ecology of both the wild and YY male populations was important for eradication. Under a restricted dispersal scenario for YY males following their release, the wild population was not extirpated but maintained a stable, yet reduced, population size. In terms of landscape configuration, time to eradication of local patches increased with greater connectivity within the stream network.</li> <li>In addition to sex ratio distortion, density-dependent mortality resulting from outplantings made an important contribution to eradication and therefore may also affect native competitors.</li> <li>While our results indicate that eradication is possible, maximizing its likelihood requires an understanding of the fitness and movement ecology of both the wild and YY male populations of the invasive species. Both our model and the principles derived from this study related to fitness and behavioral landscape ecology can be broadly applied to other invaded species and systems.</li> </ol>
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.