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.
545
datasets available to search
ShareScore release 0.7.1
Dataset results
545 results for “decision support”
A list of newly (re)appearing alien species in Belgium in support of decision making
<h2><strong>Context</strong></h2> <p>Invasive alien species are an important driver of biodiversity loss. Policy responses are developed to address this threat and need to be based on the best available data, including information from alien species registries and occurrence data. The Tracking Invasive Alien Species (<a href="http://trias-project.be" target="_blank" rel="noopener">TrIAS</a>) project implemented a workflow based on FAIR principles to identify new species in Belgium. These are species that have been newly observed on the territory or that were newly added to a species registry or checklist. The workflow is built on the Global Biodiversity Information Facility (GBIF) and uses the Belgian Global Register of Introduced and Invasive Species (<a href="https://doi.org/10.15468/xoidmd" target="_blank" rel="noopener">GRIIS Belgium</a>) as a baseline for comparison. </p> <h2><strong>Description</strong></h2> <p>This dataset contains the outputs of the <a href="https://trias-project.github.io/indicators/06_occurrence_indicators_appearing_taxa.html" target="_blank" rel="noopener">pipeline</a> that generates a list of new alien species occurring in Belgium. This pipeline retrieves alien taxa from openly published species checklists or occurrence datasets on GBIF and compares this list with the <a href="https://doi.org/10.15468/xoidmd" target="_blank" rel="noopener">Global Register of Introduced and Invasive Species - Belgium</a> (GRIIS Belgium) which is published by the IUCN Invasive Species Specialist Group (ISSG). This register is based on the <a href="https://github.com/trias-project/unified-checklist" target="_blank" rel="noopener">unified checklist of alien species in Belgium</a> which was created by TrIAS in support of research and policy using an open and reproducible workflow. Appearing/reappearing species are defined as follows:</p> <ul> <li>Appearing: an alien species which newly occurs on the Belgian territory in the three years before the year of the GBIF download used for creating the <a href="../records/10527772" target="_blank" rel="noopener">occurrence cube for non-native taxa in Belgium</a>. We will refer to this 3 years period as <em>evaluation period</em>.</li> <li>Re-appearing: an alien species reappearing on the Belgian territory after a latency of 4 years or more. For example, we consider a taxon reappearing in 2022 if observations occur in 2022 and 2018 or before.</li> </ul> <h2><strong>Files</strong></h2> <ul> <li><code>appearing_taxa.tsv</code></li> <li><code>reappearing_taxa.tsv</code></li> </ul> <h2><strong>Field values</strong></h2> <p>Field values of <code>appearing_taxa.csv</code>: </p> <ul> <li><code>taxonKey</code>: GBIF taxonKey</li> <li><code>canonicalName</code>: scientific species name</li> <li><code>year</code>: year of appearance</li> <li><code>ncells_prot_areas</code>: number of 1x1km grid cells in protected areas</li> <li><code>ncells_BE</code>: number of 1x1km grid cells in Belgium</li> <li><code>in_prot_areas</code>: species occurs for the first time in protected areas of NATURA2000 in Belgium during the evaluation period (<code>TRUE</code>/<code>FALSE</code>)</li> <li><code>in_BE</code>: species occurs for the first time in Belgium during the evaluation period(<code>TRUE</code>/<code>FALSE</code>)</li> <li><code>class</code></li> <li><code>kingdom</code></li> <li><code>classKey</code></li> <li><code>kingdomKey</code></li> </ul> <p>Field values of <code>reappearing_taxa.csv</code>: </p> <ul> <li><code>taxonKey</code>: GBIF taxonKey</li> <li><code>canonicalName</code>: scientific species name</li> <li><code>year</code>: year of reappearance</li> <li><code>ncells_prot_areas</code>: number of 1x1km grid cells in protected areas</li> <li><code>ncells_BE</code>: number of 1x1km grid cells in Belgium</li> <li><code>in_prot_areas</code>: species reappears in protected areas of NATURA2000 in Belgium (<code>TRUE</code>/<code>FALSE</code>)</li> <li><code>in_BE</code>: species reappears in Belgium during the evaluation period (<code>TRUE</code>/<code>FALSE</code>)</li> <li><code>n_latent_years</code>: latency, in year, i.e. the number of years since last occurrence in Belgium</li> <li><code>class</code></li> <li><code>kingdom</code></li> <li><code>classKey</code></li> <li><code>kingdomKey</code></li> </ul> <h2><strong>Potential uses of the dataset</strong></h2> <p>The list of newly (re)appearing alien species in Belgium can be used for various purposes:</p> <ul> <li>to update the Belgian GRIIS checklist</li> <li>to flag the occurrence of new, regulated species on the territory (early warning)</li> <li>to develop a rapid response </li> <li>to select species for quick impact assessment</li> <li>to select species for risk assessment</li> <li>to draft alert lists</li> <li>for horizon scanning alien species</li> <li>to select species for risk assessment</li> <li>to identify new introduction patways</li> <li>...</li> </ul>
Presentation of e-DSS: the e-SAFE Decision Support System
<p>e-DSS (e-SAFE Decision Support System) is a simple and intuitive software tool conceived to support professionals during the preliminary co-design process of an e-SAFE building renovation. The users can assess the energy performance of the building in its current state. Then, they are guided in the creation of a renovation project with the e-SAFE solutions (e-PANEL, e-EXOS, e-CLT, e-THERM): the tool facilitates the choice of the most suitable solution in terms of energy and seismic improvement, while also calculating energy savings, environmental benefit, and payback time. The tool is intended for all types of non-historical buildings and is available at <a href="https://esafe.eng.it/">https://esafe.eng.it/</a>.</p> <p>e-DSS has been created in the framework of Work Package 4 in the H2020 "e-SAFE" project. A final updated and refined version will be released at the end of the project. This video wants to show the tool with its different functions, including input data and results. </p> <p>For further information please contact: marilena.lazzaro@eng.it.</p>
Software-based decision support tools used in the sanitation sector
<p>This dataset includes data used in a scoping review on how decision support tools used in the sanitation sector address resource recovery considerations. The dataset is an accompaniment to the publication "A review of how decision support tools address resource recovery in sanitation systems", which was submitted to the Journal of Cleaner Production.</p>
Raw (main) dataset for the paper "Decision Support Systems Adoption in Pesticide Management"
<p>Raw dataset for farmer responses to a survey on the decision support systems adoption for intergrated pest management in the framework of the EU funded project IPM Decisions.</p>
Model results: Model-based decision support for the choice of active spring frost protection measures in apple production
<p><strong>Background: </strong></p> <p>Apple producers are dealing with weather related risks affecting their production. One important risk, is the damage of buds or young fruits by late spring frosts. Fruit growers can protect their apple orchards against this risk in various ways. With a probabilistic model (available on Git Hub: <a href="https://github.com/ChristineSchmitz/Supporting_Information_DA_Frost_Protection">https://github.com/ChristineSchmitz/Supporting_Information_DA_Frost_Protection</a>, <a href="https://doi.org/10.5281/zenodo.11473204">https://doi.org/10.5281/zenodo.11473204</a>), we want to support the decision between several active frost protection measures. The measures considered in the model are: overhead irrigation, below-canopy irrigation, stationary wind machines, mobile wind machines, tractor-mounted gas heaters, portable gas heaters, candles and pellet heaters.</p> <p>As case studies, we parameterized the model for two German apple production regions (Rhineland and Lake Constance region).</p> <p><strong>Repository content:</strong></p> <p>This repository contains the simulation results of 100,000 Monte Carlo runs with the model.</p> <p>The results are provided as .RDS and .csv files. The .RDS files are suitable to be uses with the Code on Git Hub to follow the Post-Hoc analysis and figure plotting.</p>
Zambezi dataset to "WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the Water-Energy-Food-Climate Nexus"
<p>This is the dataset used in the HESS publication "<a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-167/">WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the Water-Energy-Food-Climate Nexus</a>"</p> <p>The dataset describes the water-energy-food nexus of the Zambezi River Basin used as input to the <a href="https://github.com/RaphaelPB/WHAT-IF">WHAT-IF model</a>.</p> <p>The file Data_Organization.pdf, summarizes the available data. For more info look at the <a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-167/">publication</a> and/or <a href="https://github.com/RaphaelPB/WHAT-IF">Github</a>.</p>
The Application of a Snowpack Runoff Decision Support System for Rain-on-Snow Events Dataset
<p>This work was funded by the State of Nevada - Department of Transportation award No. P296-22-803 and UCAR COMET Outreach Program SUBAWD004566. </p>
Samples of Rectified Transfemoral Sockets with Fuzzy-Logic-Based Decision Support System
<p>This dataset contains sample rectified transfemoral sockets as an output of the fuzzy-logic DSS.</p> <p>This work was supported by the EU Horizon2020 research and innovation project SocketSense, No 825429.</p> <p>Relevant paper DOI: <a href="https://doi.org/10.3390/s21113743">https://doi.org/10.3390/s21113743</a></p>
Curated dataset for analysis for the paper "Decision Support Systems Adoption in Pesticide Management"
<p>Dataset created from farmer responses to a survey on the decision support systems adoption for intergrated pest management in the framework of the EU funded project IPM Decisions.</p>
UF & UAB's Phase 2 Demonstration Study: Developing a Model to Support Transportation System Decisions considering the Experiences of Drivers of all Age Groups with Autonomous Vehicle Technology (Project A3)
<p>Enclosed you will find the data collected during our STRIDE Phase II research project (A3) and a data dictionary.</p>
Factorial Survey: Decision Making for Extracorporeal life support (ECLS)
<p>Extracorporeal life support (ECLS) provides support to patients with cardiopulmonary failure refractory to conventional therapy. While ECLS is potentially life-saving, it is associated with severe complications; decision making to initiate ECLS must, therefore, carefully consider which patients ECLS potentially benefits despite its consequences.</p> <p>Data of a factorial survey among 420 physicians from 111 hospitals in Switzerland and Germany.</p>
Appendices for the paper "Decision Support Systems Adoption in Pesticide Management"
Open the record for dataset details and reuse information.
Figure 4 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 4: Crop sequence in rotation 3: standard sugar beets variety "Mixer"; Cereals; WOSRWWOSR; Cereals; Oil radish; "Mixer." The SBN initial population (Pi eggs g−1 soil) = 2.
Figure 8 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 8: SBN population dynamic under the semi-tolerant variety "Rosalinda" followed by non-host crops at The SBN initial population Pi (eggs g−1 soil) = 4, Tolerance (T) = 0.273, proportion of the population survived (s) = 0.35 and reproduction factor (Rf) = 3.8. The suggested number of waiting years by SBN-Watch to the next "Rosalinda" crop is four years (Pf <T).
Figure 2 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 2: Crop sequence in rotation 1: standard sugar beets variety "Mixer"; Cereals; Cereals; "Mixer." The SBN initial population (Pi eggs g−1 soil) = 2.
Figure 3 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 3: Crop sequence in rotation 2: standard sugar beets variety "Mixer"; Cereals; WOSR; Cereals; "Mixer." The SBN initial population (Pi eggs g−1 soil) = 2.
Figure 1 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 1: A screenshot of the user interface showing a selected crop rotation and the estimated final SBN population (Pf) values, sugar yield (tonnes/ha), income (SEK/ha) and the reproduction factor (Rf) values.
Figure 9 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 9: (A) relationship between initial SBN population (Pi eggs g−1 soil) and reproduction factors (Rf) of three sugar beets varieties estimated by SBNWatch; (B) relationship between initial SBN population Pi (eggs g−1 soil) and reproduction factors (Rf) of four sugar beets varieties sown in microplots in 2013–2014 (n = 4). The bars represent means of Rf ± Sd.
Figure 7 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 7: Crop sequence in rotation 6: tolerant sugar beets variety "Julietta"; Cereals; WOSR; Cereals; Oil radish; "Julietta." The SBN initial population (Pi eggs g−1 soil) = 2.
Figure 5 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 5: Crop sequence in rotation 4: tolerant sugar beets variety "Julietta"; Cereals; Cereals; "Julietta." The SBN initial population (Pi eggs g−1 soil) = 2.
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.