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29
datasets available to search
ShareScore release 0.9.0
Dataset results
29 results for “digital infrastructure”
UKRI Digital Research Infrastructure Mapping Survey Dataset (for Net Zero Scoping Project)
<p>This dataset was generated as an output for the DRI Mapping exercise carried out during the UKRI Net Zero Digital Research Infrastructure (DRI) Scoping Project undertaken from 2021-2023. The "README.md" provides more information about the dataset and how to use it.</p> <p>The report associated with this dataset is available at:</p> <p>https://doi.org/10.5281/zenodo.7805987</p>
Dataset: Global X Data Center & Digital Infrastructure ETF (DTCR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Towards a cashless society - Examining the Impact of Digital Infrastructure on mPayment Transactions (A Cross-Region Analysis).xlsx
<p>Data collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (<em>Project ID: HORIZON MSCA-SE 101086381). </em></p>
Assessing digital infrastructure in internet use: A comparative study of South East Asia and the Balkan Region
<p>Data used for assessing digital infrastructure in internet use - comparison of South East Asia and Balkan Region. Data on mobile cellular, fixed broadband, GDP, Key global ICT indicators . Data collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (Project ID: HORIZON MSCA-SE 101086381)</p>
Are there good ethical reasons why for profit publishers should no longer exist under the conditions of digital infrastructures? And what does this have to do with ethics as a reflexive discipline?
<p>Talk at the <a href="https://www.digital-philosophy.org/">Philosophy [in:of:for:and] Digital Knowledge Infrastructures</a> online workshop (08/09/2022).</p>
Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: Traffic Sign and Day 1 Video
<p>This dataset contains three files. The first is raw video files collected from a GoPro camera that was dash mounted and driven around the UTEP campus. The telemetry from these files was extracted using the process outlined here (https://lucaselbert.medium.com/extracting-gopro-gps-and-other-telemetry-data-fadf97ed1834). The videos were manual evaluated to record the time in the video where a sign appeared, and the time stamp was noted. The Python file compared the timestamps from the manual file and the GoPro telemetry to create a combined data set for each route driven that includes the type of sign and the location. This data is in the Microsoft Excel file.</p> <p> </p> <p>Note that this data set is split into two because of the size of the videos. This is the video data from day 1 of 2 of data collection.</p>
Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: Traffic Sign and Day 2 Video
<p>This dataset contains three files. The first is raw video files collected from a GoPro camera that was dash mounted and driven around the UTEP campus. The telemetry from these files was extracted using the process outlined here (https://lucaselbert.medium.com/extracting-gopro-gps-and-other-telemetry-data-fadf97ed1834). The videos were manual evaluated to record the time in the video where a sign appeared, and the time stamp was noted. The Python file compared the timestamps from the manual file and the GoPro telemetry to create a combined data set for each route driven that includes the type of sign and the location. This data is in the Microsoft Excel file.</p> <p> </p> <p>Note that this data set is split into two because of the size of the videos. This is the video data from day 2 of 2 of data collection.</p>
Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: LIDAR Point Cloud of a Portion of UTEP Campus
<p>This Autodesk ReCap file is a combination of numerous individual LiDAR scans captured using a Leica Terrestial LiDAR system. The scan includes some black and white and some color scans. The area of campus generally focuses on the southwestern portion of campus including the Interdisciplinary Research Building, the Mining Minds roundabout, the Sun Bowl 2 Parking Lot, the University Bookstore, and the Sun Bowl Parking Garage, and roads including University Ave. and Sun Bowl Drive.</p>
Assessing Digital Infrastructure in Internet Use: A Comparative Study of Southeast Asia and Balkan Region
<p>Data used for assessing digital infrastructure in internet use - comnparison of South East Asia and Balkan Region. Data on mobile cellular, fixed broadband, GDP, Key global ICT indicators. Data collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (<em>Project ID: HORIZON MSCA-SE 101086381). </em></p>
Towards big religious data, RESILIENCE Research Infrastructure for Data on Religion in the Digital Age
<p>Data in and for religion is arguably as old as humanity. Religious significance has been attached to an immense variety of artifacts and documents, often in written form, in nearly all spoken and written languages over the past millennia. The rise of the digital age gives to the scholar in religious studies the opportunity to build research over a much wider array of data than ever before; institutions which have data repositories (such as libraries, museums, universities, etc.) similarly have the chance to make their collections available to a larger community. On the other hand, however, there is a serious risk that a considerable amount of data gets lost during the "Digital transition". This paper presents the approach of the RESILIENCE Research Infrastructure in dealing with the issue of big data and data loss within the field of religious studies.</p>
How digital infrastructures empower humanities researcher(s): The PARTHENOS Training Suite
<p>1<sup>st</sup> Project Presentation</p>
FAIR Digital Object and DiSSCo Research Infrastructure Design
<p>This presentation is about how <a href="https://dissco.eu">DiSSCo</a> (Distributed System of Scientific Collections) is using the FAIR Digital Object as the cornerstone of a research infrastructure design. DiSSCo -- a new Research Infrastructure currently in the preparation phase -- will digitally unify European natural science assets under common curation and access and will ensure that data is Findable, Accessible, Interoperable and Reusable (FAIR). DiSSCo will integrate a fragmented landscape of natural science collections, transforming it into a unified, robust, quality ensured knowledge base of unprecedented scale for bio- and geodiversity.</p> <p>With a brief historical context of FAIR Digital Object, this presentation introduces the concept of Digital Specimen. Then it highlights the incorporation of outputs from several <a href="https://rd-alliance.org/">RDA</a> (Research Data Alliance) interests and working groups in the design decision.</p>
Figure 1 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 1 Conceptual model of a Knowledge Object (KO) containing a payload, machine-actionable service and deployment specifications, metadata and a unique persistent identifier. We are exploring aligning our conceptual model with emerging best practices for FAIR Digital Objects. Derived from Wittenburg et al's Digital Objects as Drivers towards Convergence in Data Infrastructures (Wittenburg et al. 2019).
Figure 3 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 3 This figure illustrates the dual nature of Knowledge Objects: knowledge-as-resource and knowledge-as-service. A KO can be curated and maintained in a repository, pass metadata to a knowledge graph or deployed into applications. Different to other digital objects, the methods to deploy the KO to applications via custom or generic runtimes called by microservices are built into the KO.
Figure 2 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 2 (L) Sample KO as viewed from the KGrid Library, from which the KO can be implemented in a hosted runtime environment or downloaded. (R) Sample output results from deploying the KO.
Digital infrastructure and green economic efficiency
<p>随着数字化的发展,数字基础设施在其中发挥着巨大的作用。与此同时,全球生态问题和经济发展也困扰着人类。因此,探索数字基础设施和绿色经济效率的作用,有利于全球环境改善和经济发展。</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.
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.