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Dataset results
88 results for “digital research”
Scientometric Assessment of Digital Library Research Publications during 2008-2017 using SCOPUS Database
Open the record for dataset details and reuse information.
Research on a Machine Interpretable and Traceable Digital Representation Method for SI
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Figure 3 from: Bernstein A, Moore RS, Rhee LQ, Aronson DL, Katz DL (2021) A digital dietary assessment tool may help identify malnutrition and nutritional deficiencies in hospitalized patients. Research Ideas and Outcomes 7: e70642. https://doi.org/10.3897/rio.7.e70642
Figure 3 Results illustrating a select micronutrient profile which may be within or out of range for a sample patient with the following characteristics: 70 years old, male, height 5 foot 6 inches, weight 125 pounds, minimal physical activity, losing weight, following restrictive diet.
Figure 2 from: Bernstein A, Moore RS, Rhee LQ, Aronson DL, Katz DL (2021) A digital dietary assessment tool may help identify malnutrition and nutritional deficiencies in hospitalized patients. Research Ideas and Outcomes 7: e70642. https://doi.org/10.3897/rio.7.e70642
Figure 2 Results illustrating a select macronutrient profile which may be within or out of range for a sample patient with the following characteristics: 70 years old, male, height 5 foot 6 inches, weight 125 pounds, minimal physical activity, losing weight, following restrictive diet.
Researching the Effectiveness of a Alivis, a Digital Health Application for Borderline Personality Disorder
ClinicalTrials.gov study NCT06601907. IPD Sharing: NO. Countries: 1. Publications: 0.
Digital research data from: Biochar stability in a highly weathered sandy soil under four years of continuous corn production
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Digital Elevation Models for the Global Change Research Wetland, Maryland, USA, 2016
This dataset contains four alternative digital elevation models (DEMs) at 1 m resolution and model performance statistical metrics for the Global Change Research Wetland (GCReW) site on the Rhode River, a tributary of the Chesapeake Bay in Maryland, USA, for the year 2016. Three DEMs were created by using different strategies for correcting positive biases in Light Detection and Ranging (LiDAR)-based DEMs that are common in tidal wetlands. These included (1) applying a single average offset based on a literature review, (2) using the LiDAR Elevation Correction with NDVI (LEAN)-method, and (3) applying plant community-specific offsets using a local vegetation cover map. Existing LiDAR data at 1 m resolution collected in 2011 was the basis for these DEMs. The fourth DEM was created by using Empirical Bayesian Kriging to extrapolate between measured ground points. The elevation is provided in meters relative to the North American Vertical Datum of 1988 (NAVD 88). To calibrate the four approaches, the elevation of the entire marsh complex was surveyed at 20 m x 20 m resolution to document the distribution of elevation relative to tidal datums from a single year. Two Trimble R8 real-time kinematic (RTK) GPS receivers were used to survey 525 points over the complex from July 26, 2016, to August 15, 2016. Relative plant cover was also documented. Tidal datums were calculated from the nearby Annapolis, MD tidal gauge located 13 km from GCReW.
Figure 4 from: Borsch T, Stevens A-D, Häffner E, Güntsch A, Berendsohn WG, Appelhans MS, Barilaro C, Beszteri B, Blattner FR, Bossdorf O, Dalitz H, Dressler S, Duque-Thüs R, Esser H-J, Franzke A, Goetze D, Grein M, Grünert U, Hellwig F, Hentschel J, Hörandl E, Janßen T, Jürgens N, Kadereit G, Karisch T, Koch MA, Müller F, Müller J, Ober D, Porembski S, Poschlod P, Printzen C, Röser M, Sack P, Schlüter P, Schmidt M, Schnittler M, Scholler M, Schultz M, Seeber E, Simmel J, Stiller M, Thiv M, Thüs H, Tkach N, Triebel D, Warnke U, Weibulat T, Wesche K, Yurkov A, Zizka G (2020) A complete digitization of German herbaria is possible, sensible and should be started now. Research Ideas and Outcomes 6: e50675. https://doi.org/10.3897/rio.6.e50675
Figure 4 Collection data portal based on existing open source software components.
Figure 9 from: Hardisty A, Saarenmaa H, Casino A, Dillen M, Gödderz K, Groom Q, Hardy H, Koureas D, Nieva de la Hidalga A, Paul DL, Runnel V, Vermeersch X, van Walsum M, Willemse L (2020) Conceptual design blueprint for the DiSSCo digitization infrastructure - DELIVERABLE D8.1. Research Ideas and Outcomes 6: e54280. https://doi.org/10.3897/rio.6.e54280
Figure 9 Funding sources as they correspond to the different development phases of the DiSSCo RI.
Figure 8 from: Hardisty A, Saarenmaa H, Casino A, Dillen M, Gödderz K, Groom Q, Hardy H, Koureas D, Nieva de la Hidalga A, Paul DL, Runnel V, Vermeersch X, van Walsum M, Willemse L (2020) Conceptual design blueprint for the DiSSCo digitization infrastructure - DELIVERABLE D8.1. Research Ideas and Outcomes 6: e54280. https://doi.org/10.3897/rio.6.e54280
Figure 8 DiSSCo Programme of linked projects.
Figure 6 from: Hardisty A, Saarenmaa H, Casino A, Dillen M, Gödderz K, Groom Q, Hardy H, Koureas D, Nieva de la Hidalga A, Paul DL, Runnel V, Vermeersch X, van Walsum M, Willemse L (2020) Conceptual design blueprint for the DiSSCo digitization infrastructure - DELIVERABLE D8.1. Research Ideas and Outcomes 6: e54280. https://doi.org/10.3897/rio.6.e54280
Figure 6 Some uses of natural science collections in formal and informal education.
Figure 7 from: Hardisty A, Saarenmaa H, Casino A, Dillen M, Gödderz K, Groom Q, Hardy H, Koureas D, Nieva de la Hidalga A, Paul DL, Runnel V, Vermeersch X, van Walsum M, Willemse L (2020) Conceptual design blueprint for the DiSSCo digitization infrastructure - DELIVERABLE D8.1. Research Ideas and Outcomes 6: e54280. https://doi.org/10.3897/rio.6.e54280
Figure 7 Governance and management models during the different programme phases.
Figure 5 from: Hardisty A, Saarenmaa H, Casino A, Dillen M, Gödderz K, Groom Q, Hardy H, Koureas D, Nieva de la Hidalga A, Paul DL, Runnel V, Vermeersch X, van Walsum M, Willemse L (2020) Conceptual design blueprint for the DiSSCo digitization infrastructure - DELIVERABLE D8.1. Research Ideas and Outcomes 6: e54280. https://doi.org/10.3897/rio.6.e54280
Figure 5 An example matrix (QR) code encoding information about a specimen.
Figure 3 from: Hardisty A, Saarenmaa H, Casino A, Dillen M, Gödderz K, Groom Q, Hardy H, Koureas D, Nieva de la Hidalga A, Paul DL, Runnel V, Vermeersch X, van Walsum M, Willemse L (2020) Conceptual design blueprint for the DiSSCo digitization infrastructure - DELIVERABLE D8.1. Research Ideas and Outcomes 6: e54280. https://doi.org/10.3897/rio.6.e54280
Figure 3 From Hub platform to APIs, data lifecycle and system design.
Figure 4 from: Hardisty A, Saarenmaa H, Casino A, Dillen M, Gödderz K, Groom Q, Hardy H, Koureas D, Nieva de la Hidalga A, Paul DL, Runnel V, Vermeersch X, van Walsum M, Willemse L (2020) Conceptual design blueprint for the DiSSCo digitization infrastructure - DELIVERABLE D8.1. Research Ideas and Outcomes 6: e54280. https://doi.org/10.3897/rio.6.e54280
Figure 4 Illustrative system design based on multiple CORDRA object repository instances.
Figure 12 from: Hardisty A, Saarenmaa H, Casino A, Dillen M, Gödderz K, Groom Q, Hardy H, Koureas D, Nieva de la Hidalga A, Paul DL, Runnel V, Vermeersch X, van Walsum M, Willemse L (2020) Conceptual design blueprint for the DiSSCo digitization infrastructure - DELIVERABLE D8.1. Research Ideas and Outcomes 6: e54280. https://doi.org/10.3897/rio.6.e54280
Figure 12 Introducing a funding thematic coordination layer between national facilities and RIs.
Figure 10 from: Hardisty A, Saarenmaa H, Casino A, Dillen M, Gödderz K, Groom Q, Hardy H, Koureas D, Nieva de la Hidalga A, Paul DL, Runnel V, Vermeersch X, van Walsum M, Willemse L (2020) Conceptual design blueprint for the DiSSCo digitization infrastructure - DELIVERABLE D8.1. Research Ideas and Outcomes 6: e54280. https://doi.org/10.3897/rio.6.e54280
Figure 10 Illustrating the direct funding model for RIs. Arrows depict funding flows.
Figure 11 from: Hardisty A, Saarenmaa H, Casino A, Dillen M, Gödderz K, Groom Q, Hardy H, Koureas D, Nieva de la Hidalga A, Paul DL, Runnel V, Vermeersch X, van Walsum M, Willemse L (2020) Conceptual design blueprint for the DiSSCo digitization infrastructure - DELIVERABLE D8.1. Research Ideas and Outcomes 6: e54280. https://doi.org/10.3897/rio.6.e54280
Figure 11 SWOT analysis of the direct funding model for RIs.
Figure 2 from: Hardisty A, Saarenmaa H, Casino A, Dillen M, Gödderz K, Groom Q, Hardy H, Koureas D, Nieva de la Hidalga A, Paul DL, Runnel V, Vermeersch X, van Walsum M, Willemse L (2020) Conceptual design blueprint for the DiSSCo digitization infrastructure - DELIVERABLE D8.1. Research Ideas and Outcomes 6: e54280. https://doi.org/10.3897/rio.6.e54280
Figure 2 The DiSSCo Hub data platform.
Figure 1 from: Hardisty A, Saarenmaa H, Casino A, Dillen M, Gödderz K, Groom Q, Hardy H, Koureas D, Nieva de la Hidalga A, Paul DL, Runnel V, Vermeersch X, van Walsum M, Willemse L (2020) Conceptual design blueprint for the DiSSCo digitization infrastructure - DELIVERABLE D8.1. Research Ideas and Outcomes 6: e54280. https://doi.org/10.3897/rio.6.e54280
Figure 1 Multiple factors balance to achieve goals.
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.