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.
9
datasets available to search
ShareScore release 0.9.0
Dataset results
9 results for “open content”
Carbon and nitrogen content and stable isotope compositions from particulate organic matter samples from lagoon, river, and open ocean sites along the Alaska Beaufort Sea coast, 2018-ongoing
Multiple water types (river, lagoon, ocean) from the North Slope of Alaska and nearshore Beaufort Sea are sampled seasonally by the Beaufort Lagoon Ecosystems LTER (BLE LTER) Core Program to investigate biogeochemical linkages between terrestrial, lagoon, and open ocean ecosystems. Water samples are collected during full ice cover (April), ice break-up (mid-June to early July), and open water (late July and August) periods, and analyzed for particulate organic carbon (POC) and particulate organic nitrogen (PON) content and stable isotopic composition.
Soil organic carbon content [g/kg] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: log organic carbon [g/kg] to back-transform use exp(x/10)-1;</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p> <p>To back-transform the log.oc maps use formula: exp(x/10)-1. These are examples of back-transformed values:</p> <p> log.oc = 15 → 0.3% SOC;<br> log.oc = 20 → 0.6% SOC;<br> log.oc = 25 → 1.1% SOC;<br> log.oc = 30 → 1.9% SOC;<br> log.oc = 35 → 3.2% SOC;<br> log.oc = 40 → 5.3% SOC;<br> log.oc = 50 → 14.8% SOC;</p>
Soil clay content [%] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: clay.tot = clay content [percent];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Soil sand content [%] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: sand.tot = sand content [percent];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Content Analysis of Canada's Science Based Departments and Agencies Open Science Action Plans
<p>Dataset and codebook for a content analysis of Science-based departments and agencies open science action plans in response to the Government of Canada's Roadmap for Open Science. </p>
SGS-LTER CO2 Elevation Study: Weekly volumetric soil water content, from TDR probes, for Open Top Chamber plots on the Central Plains Experimental Range, Nunn, Colorado, USA 1997-2001
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/82454. Volumetric soil water content was measured in the 0-15 cm soil depth layer using TDR probes, nearly weekly, in ambient and elevated CO2 open-top-chambers, and unchambered plots. A consistent improvement in SWC was seen in the elevated CO2 plots, while ambient-chambered plots often had lowest SWC. This research was conducted at the Central Plains Experimental Range, near Nunn, CO; lat.40degrees 40 minutes N; long. 104 degrees 45 minutes W in the shortgrass steppe region of NE Colorado, USA and as a collaboration between SGS-LTER and USDA-ARS researchers.
Exploring the reach of open access content to non-academic audiences
<p>Raw data from survey run on nature.com, springerlink and biomedcentral.com in association with VSNU. Relates to publication "Exploring the reach of open access content to non-academic audiences"</p> <p><a href="https://www.doi.org/10.5281/zenodo.4143313">https://www.doi.org/10.5281/zenodo.4143313</a></p> <p> </p>
Supporting dataset for : Promoting content discovery of open repositories : reviewing the impact of optimization techniques (2016-2019)
<p>This dataset supports the conference paper, 'Promoting content discovery of open repositories : reviewing the impact of optimization techniques (2016-2019)', <a href="https://doi.org/10.17868/67963">deposited and available in Strathprints</a> and presented at the 14th International Conference Open Repositories (OR2019). It also supports the journal article, 'Enhancing content discovery of open repositories: an analytics-based evaluation of repository optimizations', published in Publications. Full details of these publications are as follows: </p> <ul> <li>Macgregor, G. (2019). <em>Promoting content discovery of open repositories : reviewing the impact of optimization techniques (2016-2019)</em> . (pp. 1-11). Glasgow: University of Strathclyde [Strathprints repository]. Available: <a href="https://doi.org/10.17868/67963">https://doi.org/10.17868/67963</a> </li> <li>Macgregor, G. (2020). Enhancing content discovery of open repositories: an analytics-based evaluation of repository optimizations. <em>Publications</em>, <em>8</em>(1), [8]. <a href="https://doi.org/10.3390/publications8010008">https://doi.org/10.3390/publications8010008</a></li> </ul> <p>The dataset comprises a series of comma-separated values plain text documents (.csv). The .csv files contain data pertaining to COUNTER compliant usage statistics, search query traffic from Google Search Console, web traffic data from Google Analytics and figures on annual full-text repository deposits made to the Strathprints repository. A README.txt is included, describing in more detail the nature and the structure of the data.</p> <p>All data relate to the EPrints repository, <a href="https://strathprints.strath.ac.uk/">Strathprints</a>, based at the University of Strathclyde.</p>
A 24 Week, Multicenter, Prospective, Open-labeled, Single-arm, Exploratory Phase 4 Clinical Trial to Evaluate the Safety and Efficacy of Lobeglitazone in Decreasing Intrahepatic Fat Contents in Type 2
ClinicalTrials.gov study NCT02285205. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.