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3,118 results for “resources”
nNPipe: A neural network pipeline for automated analysis of morphologically diverse catalyst systems - Resources
<p>This dataset comprises of resources required to replicate the results described in "<em>nNPipe</em>: A neural network pipeline for automated analysis of morphologically diverse catalyst systems". <em>nNPipe </em>is a deep learning based method in which two deep convolutional neural networks are used for the automated analysis of 2048x2048 HRTEM images.</p> <p>The file contains:<br> - Relevant experimental images as well as ground truth for Pd/C and Au/Ge systems.<br> - A workflow file explaining the nNPipe workflow.<br> - Mathematica 12.1 code for the generation of computational models.<br> - MATLAB code for HRTEM multislice simulations using MULTEM, as well as code required to form respective training datasets.<br> - Weights and files required for training the YOLOv5x module.<br> - Weights and files required for training the SegNet module.<br> - Mathematica 12.1 code required for reconstruction of 2048x2048 binary segmented maps of HRTEM images. </p>
Human resources for research and innovation in Italy
<p>Human resources play a crucial role in enabling research and innovation. Key players include university students, PhD and master graduates, researchers holding a European grant supporting excellent researchers in carrying out ground-breaking, high-risk, high-gain, frontier research projects, entrepreneurs engaged in spin-offs, startups or innovation project supported by Horizon 2020 <em>SME instrument</em> grants.</p> <p>Data is generally available, but often it is not easy to use due to different formats and vocabularies and the variety of geographical references (city names, province, region or zip codes).</p> <p>This file collection is part of ongoing research work carried out by the sustainability unit at Area Science Park. Data is collected from a variety of open sources, curated and prepared for further analysis. The focus is on Italy; geographical references use EUROSTAT NUTS-2 and NUTS-3 taxonomy.</p> <p>Data available: </p> <ul> <li>Maps of NUTS2 and NUTS2 regions in Italy</li> <li>NUTS2 and NUTS3 names in Italian</li> <li>Universities </li> <li>Phd and Masters graduates since 2010</li> <li>University spin-offs </li> <li>Innovative Startups</li> <li>Horizon 2020 grants for researchers: "<em>Marie Skłodowska Curie</em>" and "<em>European Research Council</em>"</li> <li>Horizon 2020 grants "<em>SME instrument</em>"</li> </ul> <p>Python scripts for data preparation are available in script.zip; development version is available on <a href="https://gitlab.com/area-science-park-sustainability/it_regional_innovation">this GitLab repository</a><br> Some examples of visual representation of the data are available in .pdf format and as <a href="https://app.powerbi.com/view?r=eyJrIjoiMWMyMjA1OWQtMzJmNi00NWJmLTk1OTctMzczZWUxYjYzYzFmIiwidCI6ImQ0YWFmY2E2LWJmMzUtNDUxNS1iMDZhLTQ5NzNjZGZiYmVkMyIsImMiOjh9&pageName=ReportSection3090d63ae7727ef701e8">online interactive visualization report.</a></p>
Hourly LC impacts - Resource use - minerals and metals - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Resource use - minerals and metals, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
njdowdy/tpt-taxonomy: TPT Taxonomic Resource v2.0.0
<p>This is the second public release of the taxonomic resources generated for the Terrestrial Parasite Tracker project. Some names given by list providers may be omitted due to being flagged for curatorial review for various reasons.</p>
Goidelex: A Lexical Resource for Old Irish
<p>Goidelex is an openly accessible relational database in CSV format, linked by formal relationships. The launch version documents 695 headwords with extensive linguistic annotations, including orthographic forms using a normalised orthography, automatically generated phonemic transcriptions, and information about morphosyntactic features, such as gender, inflectional class, etc. Metadata in JSON format, following the Frictionless standard, provides detailed descriptions of the tables and dataset. The database is designed to be fully compatible with the Paralex and CLDF standards and is interoperable with existing lexical resources for Old Irish such as CorPH and DIL. It is suited to both qualitative and quantitative investigation into Old Irish morphology and lexicon, as well as to comparative research. </p>
EOSC Providers and Resources data-dump
<p><span><span>The EOSC Providers and Resources dataset contains the metadata descriptions (EOSC Profiles) of the EOSC Providers and the Resources (e.g</span>. catalogues, services, data sources, training material and interoperability guidelines) they onboarded to the EOSC Catalogue and Marketplace during the <a href="https://eoscfuture.eu">EOSC Future project</a>.</span></p> <p><span>The dataset is based on a data extraction, provided by the ATHENA Research Center, using the public API of the EOSC Service Registry, which is part of the EOSC Resource Catalogue. The information provided here is a snapshot: a historical record of (a subset of) the information about various providers and resources recorded within the EOSC Catalogue and Marketplace at the end of the EOSC Future project in April 2024.</span></p> <p><span>A curation process, using a <a href="https://gitlab.desy.de/paul.millar/eosc-datadump">publicly accessible data curation workflow</a>, designed and implemented at DESY, was used to remove all known personal or sensitive data in line with GDPR and the <a href="https://eoscfuture.eu/privacy-policy/">EOSC Future Privacy Policy</a>. This process is described in the <code>methodology.md</code> file within this dataset.</span></p> <p><span>From April 2024, the EOSC Portal was phased out. On 24th April 2024, the European Commission <a href="https://open-science-cloud.ec.europa.eu/news/commission-announces-eosc-eu-nodes-web-presence">announced</a> the next phase of EOSC with the launch of the initial web presence of the <a href="https://open-science-cloud.ec.europa.eu">EOSC EU Node</a>. The goal of publishing this dataset is to enable other EOSC-related projects such as <a href="https://oscars-project.eu">OSCARS</a>, <a href="https://www.eosc-beyond.eu">EOSC Beyond</a> as well as researchers more broadly, to both reuse and build further on this work.<br></span></p>
Development of ferret immune repertoire reference resources and single-cell-based high- throughput profiling assays
<p>We performed long read transcriptome sequencing of ferret splenocyte and lymph node samples full-length, non-chimeric circular consensus sequencing (CCS) reads to obtain over 120,000 high-quality immunoglobin (Ig) and T cell receptor (TCR) transcripts.</p>
DS_Wave_BiMEP: Wave resource at BiMEP (Spain)
<p>This Technical Note extends the first published dataset obtained from the TRIAXYS buoy deployed at BiMEP. It covers two periods: i) December 2016 to October 2017; ii) March 2018 to July 2018</p> <p>Sensor is located at 87 m water depth, 300 m up-wave of the OCEANTEC's Wave Energy Converter, MARMOK- A5 (43°28'12.19"N, 2°52'17.88"O). Statistical wave data are calculated from 20-min time series.</p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment 2
<p>1996-1999, entomofauna inventory in cities and countrysides on 20 different habitats in The Netherlands using pitfalls and sweeping nets Jagers op Akkerhuis G, Dimmers W (2016). Alterra (NL) - Comparison entomofauna in cities en countrysides. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/guxqfv">https://doi.org/10.15468/guxqfv</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment 3
<p>1996-1999, entomofauna inventory in cities and countrysides on 20 different habitats in The Netherlands using pitfalls and sweeping nets Jagers op Akkerhuis G, Dimmers W (2016). Alterra (NL) - Comparison entomofauna in cities en countrysides. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/guxqfv">https://doi.org/10.15468/guxqfv</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment
<p>1996 till 2000, entomofauna inventory on clay digged off riversides and on reference site in The Netherlands using pyramidtraps Faber J, Dimmers W (2016). Alterra (NL) - Entomofauna inventory in riverside grasslands. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/hlhr1r">https://doi.org/10.15468/hlhr1r</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment 7
<p>Spring 2010 and Summer 2011, microarthropod fauna inventory for foodweb analysis in five European countries using soil cores and tullgren extraction</p> <p>Bloem J, Dimmers W (2016). Alterra (NL) - Microarthropods inventory in European countries. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/zkqto2">https://doi.org/10.15468/zkqto2</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment 5
<p>2001 till 2002 and 2004, inventory of entomofauna in forestwalls banks, diches, road verges in The Netherlands using pitfalls and sweeping net</p> <p>Jagers op Akkerhuis G, Dimmers W (2016). Alterra (NL) - Comparison of entomofauna in four different habitats. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/mkoqqh">https://doi.org/10.15468/mkoqqh</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment 6
<p>2007, 2011 and 2012, microarthropod fauna inventory in a nature restauration experiment (re-introduction) on a calcareous grassland and three reverence sites in the province of Limburg using pF-cores</p> <p>Smits N, Dimmers W (2016). Alterra (NL) - Microarthropods inventory in calcareous grasslands. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/28jocn">https://doi.org/10.15468/28jocn</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment 4
<p>1993 till 2001, entomofauna inventory in cattle grazed versus non-grazed dune grassland using pitfalls</p> <p>van Wingerden W, Dimmers W (2016). Alterra (NL) - Entomofauna inventory in cattle grazed dune grassland. Version 2.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/zp5oif">https://doi.org/10.15468/zp5oif</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment 8
<p>2000-2003, microarthropod fauna inventory of arable land and grassland on sandy soil using pF-cores. Points of interest are biodiversity, nutrients and disease suppression</p> <p>Faber J, Dimmers W (2016). Alterra (NL) - Microarthropods inventory in grassland and arable land. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/fwvi7m">https://doi.org/10.15468/fwvi7m</a> accessed via GBIF.org</p>
Resource Metadata Harvested from Government and Research Open Data Portals
<p>This dataset consists of resource metadata harvested from the APIs of hundreds of government and research data portals from all over the world. This dataset was harvested between the 13<sup>th</sup> and 15<sup>th</sup> of September 2018. The metadata harvested from these portals was translated to a single metadata format (see <em>metadata_format.odt</em>). An overview of all harvested domains is given in <em>portal_list.txt</em>.</p> <p>The harvested data is divided into five gzipped json-lines files, based on the ‘type’ of the resource that is derived from the data of the APIs:</p> <ul> <li><em>dataset_metadata.jsonl.gz</em>: Resources classified as a Dataset, or subsets of dataset (e.g. Dataset:Image and Dataset:Audio) [6 246 250 resources]</li> <li><em>document_metadata.jsonl.gz</em>: Resources classified as a Document, or subset of document (e.g. Document:Paper:Conference and Document:Book) [15 626 541 resources]</li> <li><em>software_metadata.jsonl.gz</em>: Resources classified as Sofware (including Software:Model) [42 036 resources]</li> <li><em>service_metadata.jsonl.gz</em>: Resources classified as a service (e.g. WMS, APIs) [1257 resources]</li> <li><em>other_metadata.jsonl.gz</em>: Resources of which the ‘type’ could not be determined from the data the API returned. This set still contains many datasets [1 502 979 resources]</li> </ul>
Resource heterogeneity leads to unjust effort distribution in climate change mitigation
<p>Climate change mitigation is a shared global challenge that involves the collective action of a set of individuals with different tendencies to cooperation. However, we lack an understanding of the effect of resource inequality when diverse actors interact together toward a common goal. Here, we report the results of a collective-risk dilemma experiment in which groups of individuals were initially given either equal or unequal endowments. We found that the effort distribution was highly inequitable, with participants with fewer resources contributing significantly more to the public goods than the richer - sometimes twice as much. An unsupervised learning algorithm classified the subjects according to their individual behavior, finding the poorest participants within two "generous clusters'" and the richest into a "greedy cluster''. Our results suggest that policies would benefit from educating about fairness and reinforcing climate justice actions addressed to vulnerable people instead of focusing on understanding generic or global climate consequences.</p> <p>Vicens J, Bueno-Guerra N, Gutiérrez-Roig M, Gracia-Lázaro C, Gómez-Gardeñes J, Perelló J, et al. (2018) Resource heterogeneity leads to unjust effort distribution in climate change mitigation. PLoS ONE 13(10): e0204369. https://doi.org/10.1371/journal.pone.0204369</p>
A decade of Semantic Web research through the lenses of a mixed methods approach (Resources)
<p>This work has been submitted to <a href="http://www.semantic-web-journal.net/content/decade-semantic-web-research-through-lenses-mixed-methods-approach">Semantic Web Journal</a>. We provide here resources to reproduce our approach.</p> <p>In this paper, we aim to provide a broader and more complete picture of Semantic Web topics and trends by adopting a mixed methods methodology, which allows a combined use of both qualitative and quantitative approaches. Concretely, we build on a qualitative analysis of the main seminal papers, which adopt a top-down approach, and on quantitative results derived with three bottom-up data-driven approaches (<a href="https://technologies.kmi.open.ac.uk/Rexplore/">Rexplore</a>, <a href="http://saffron.insight-centre.org/">Saffron</a>, <a href="https://www.poolparty.biz/">PoolParty</a>), on a corpus of Semantic Web papers published in the last decade. In this process, we both use the latter for “fact-checking” on the former and also to derive key findings in relation to the strengths and weaknesses of top-down and bottom-up approaches to research topic identification.</p> <p>Please access the full set of resources at: <a href="https://aic.ai.wu.ac.at/qadlod/SW/">https://aic.ai.wu.ac.at/qadlod/SW/</a></p>
Back to the edge: relative coordinate system for use-wear analysis [complement to Online Resource 6]
<p>Raw data, and R markdown scripts and HTML outputs of the statistical procedures.</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></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.