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.
3
datasets available to search
ShareScore release 0.9.0
Dataset results
3 results for “Legacy sources”
First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS
<p>This dataset is relative to the paper entitled: "First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS" publishing in journal Diversity (MPDI).</p> <p>Abstract:</p> <p>Zooplankton is a fundamental group in all aquatic ecosystems located the base of the food chain. It forms a link between the lower trophic levels with secondary consumers and shows marked fluctuations of populations with environmental change, especially reacting to heating and water acidification. At sea copepod crustaceans account for app. 70% in abundance of zooplankton and are a target of monitoring activities in key areas such as the Southern Ocean. In this study we have used FAIR-inspired legacy data (dating back to the ‘80s) collected in the Ross Sea by the Italian National Antarctic Program in GBIF.org. Together with other open-access GIS data sources and tools it allows generating, for the first time, three-dimensional predictive distribution maps for twenty-six copepod species. These predictive maps were obtained by applying machine learning techniques to grey literature data, which were visualized in open-source GIS platforms. In a Species Distribution Modeling (SDM) framework we used machine learning with three types of algorithms (TreeNet, RandomForest and Ensemble) to analyze the presence and absence of copepods at different areas and depth classes in function of environmental descriptors obtained from the Polar Macroscope Layers present in Quantartica. The models allow for the first time to map-predict the food chain in quantitative terms showing the relative index of occurrence (RIO) and identified the presence for each copepod species analyzed in the Ross Sea. Our results show marked geographical preferences that vary with species and trophic strategy. This study demonstrates that machine learning is a successful method in accurately predicting Antarctic copepod presence, also providing useful data to orient future sampling and management of wildlife and conservation.</p>
Managing competition between legacy television services and video streaming platforms in Hungary in the early 2020s – A case study [Secondary documentary sources]
<p>Secondary documentary sources used in the paper entitled "Managing competition between legacy television services and video streaming platforms in Hungary in the early 2020s – A case study"</p>
Database-for-cross-country-regional-active-and-legacy-nutrient-source-attribution
<p>This dataset contains nutrient concentration (TN/NH3-N, TP) and water discharge data from Australia, China, Sweden, and the USA.<br>Each country's data is organized into subfolders based on the respective country.</p><p>Data Sources:<br>The data were collected from water quality monitoring agencies, research institutions, and public data sources in each respective country. The data sources for each country are as follows:<br>Australia: Retrieved from <a href="https://data.water.vic.gov.au/">https://data.water.vic.gov.au/</a><br>China: Retrieved from the Ministry of Ecology and Environment of the People's Republic of China (nutrient concentration) and the Hydrological Year Book (water discharge).<br>USA: Retrieved from <a href="https://doi.org/10.5066/P948Z0VZ">https://doi.org/10.5066/P948Z0VZ</a><br>Sweden: Retrieved from <a href="https://doi.org/10.5281/zenodo.7433379">https://doi.org/10.5281/zenodo.7433379</a></p><p>Data Formats and contents:<br>The data in each subfolder are stored in TXT or Excel formats.<br>TXT Files: These files are named by monitor station ID and include monitor time, nutrient data, water discharge data, and corresponding units.<br>Excel Files: These files contain information about the location of the monitoring stations and maps of the catchment areas.</p><p>To align with the source data, please be aware that the units for nutrient concentration and water discharge data may vary for each country.</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.