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FIG. 6 in The modern ontological natures of the Cairina moschata (Linnaeus, 1758) duck. Cases from Perú, the northern hemisphere, and digital communities
FIG. 6. — Seller of domestic birds in popular market,Trujillo (photo Jorge Gamboa).
Data and R code for the preprint "Downscaling digital soil maps using electromagnetic induction and aerial imagery"
<p>Data and R code used in the preprint "Downscaling digital soil maps using electromagnetic induction and aerial imagery" (Møller, 2020). This is the data and code for the preprint before submission for peer review. The data and code for the revised manuscript are available at <a href="https://doi.org/10.5281/zenodo.3959005">https://doi.org/10.5281/zenodo.3959005</a>.</p> <p>Code originally written for R version 3.6.2.</p> <p>References<br> Møller, A.B., Koganti, T., Beucher, A., Iversen, B.V. and Greve, M.H., 2020. Downscaling digital soil maps using electromagnetic induction and aerial imagery. EarthArXiv. <a href="http://dx.doi.org/10.31223/osf.io/a7xz6">http://dx.doi.org/10.31223/osf.io/a7xz6</a>. [preprint]</p> <p> </p>
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.
Outcomes of the LIBER Digital Humanities and Digital Cultural Heritage Working Group's survey (2019)
<p>This dataset represents the questions and summarised answers of the surveyed libraries for the LIBER Digital Humanities and Digital Cultural Heritage Working Group's survey conducted between 1 February 2019 and 15 March 2019. Much care has been taken to ensure answers remain anonymous and adhere to the GDPR. Please contact Lotte Wilms or the LIBER office should you wish to adapt anything published in this overview.</p> <p>The report published following this survey is the following:</p> <p>Wilms, Lotte, Derven, Caleb, O'Dwyer, Liam, Lingstadt, Kirsty, & Verbeke, Demmy. (2019, June 17). Europe's Digital Humanities Landscape: A Study From LIBER's Digital Humanities & Digital Cultural Heritage Working Group. Zenodo. http://doi.org/10.5281/zenodo.3247286 </p>
Frequency Maps of Digital Brain Model
<p> </p> <ul> <li><em>frequency_1.nii.gz</em> - Larmor frequency of digital brain model without microstructure</li> <li><em>frequency_2.nii.gz</em> - Larmor frequency of digital brain model with microstructure</li> </ul>
The Varying Openness of Digital Open Science Tools
<p>Dataset accompanying the paper submitted to F1000 entitled <strong>The Varying Openness of Digital Open Science Tools.</strong></p> <p> </p> <p>Abstract of paper</p> <p>Digital tools that support Open Science practices play a key role in the seamless accumulation, archiving and dissemination of scholarly data, outcomes and conclusions. Despite their integration into Open Science practices, the providence and design of these digital tools are rarely explicitly scrutinized. This means that influential factors, such as the funding models of the parent organizations, their geographic location, and the dependency on digital infrastructures are rarely considered. Suggestions from literature and anecdotal evidence already draw attention to the impact of these factors, and raise the question of whether the Open Science ecosystem can realise the aspiration to become a truly “unlimited digital commons” in its current structure. </p> <p> </p> <p>In an online research approach, we compiled and analysed the geolocation, terms and conditions as well as funding models of 242 digital tools increasingly being used by researchers in various disciplines. Our findings indicate that design decisions and restrictions are biased towards researchers in North American and European scholarly communities. In order to make the future Open Science ecosystem inclusive and operable for researchers in all world regions including Africa, Latin America, Asia and Oceania, those should be actively included in design decision processes. </p> <p>Digital Open Science Tools carry the promise of enabling collaboration across disciplines, world regions and language groups through responsive design. We therefore encourage long term funding mechanisms and ethnically as well as culturally inclusive approaches serving local prerequisites and conditions to tool design and construction allowing a globally connected digital research infrastructure to evolve in a regionally balanced manner.</p>
Recognition of Cutaneous Melanoma on Digitized Histopathological Slides via Artificial Intelligence Algorithm - deep net Matlab
<p>The file is the trained convolutional neural network (CNN) developed in "De Logu, Francesco, et al. "Recognition of Cutaneous Melanoma on Digitized Histopathological Slides via Artificial Intelligence Algorithm." <em>Frontiers in Oncology</em> 10 (2020)". The CNN is based on a pretrained Inception-ResNet-v2 to automatically recognizes cutaneous melanoma from histopathological digitalized slides. The file is in a Matlab format (.mat).</p>
Data from: Low second-to-fourth digit ratio predicts indiscriminate social suspicion, not improved trustworthiness detection
Testosterone administration appears to make individuals less trusting, and this effect has been interpreted as an adaptive adjustment of social suspicion, that improved the accuracy of trusting decisions. Here, we consider another possibility, namely that testosterone increases the subjective cost of being duped, decreasing the propensity to trust without improving the accuracy of trusting decisions. In line with this hypothesis, we show that second-to-fourth digit ratio (2D : 4D, a proxy for effects of testosterone in the foetus) correlates with the propensity to trust, but not with the accuracy of trusting decisions. Trust game players (n = 144) trusted less when they had lower 2D : 4D (high prenatal testosterone), but their ability to detect the strategy of other players was constant (and better than chance) across all levels of digit ratio. Our results suggest that early prenatal organizing effects of testosterone in the foetus might impair rather than boost economic outcomes, by promoting indiscriminate social suspicion.
Digital Geological Map of Jordan
<p>This dataset contains a geopackage with the georeferenced geological map of Jordan. The data covers most of Jordan with a focus on the western part of the country. Note: use the pdf for the geological unit abbreviations. EPSG code is 3857. </p>
ScienceDex guides
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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.