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10,119 results for “IT Support”
A high-resolution three-year dataset supporting rooftop photovoltaics (PV) generation analytics
<p>This dataset includes measured photovoltaic (PV) power generation data and on-site weather data collected from 60 grid-connected rooftop PV stations in Hong Kong over a three-year period (2021-2023). The PV power generation data was collected at 5-minute intervals. The meteorological data was collected at 1-minute intervals from an on-site weather station. The metadata was represented using Brick schema was developed, which simplifies the data comprehension and the development of smart analytics applications. The detailed Brick model is stored in the .ttl file format, which can be accessed for retrieving metadata through the use of SPARQL queries.This dataset can be used in various applications - PV generation benchmarking, PV degradation analysis, PV fault detection, solar radiation and PV power generation forecasting, and the simulation and design of PV systems.</p>
AI4Life-MDC24 Challenge data: SUPPORT method dataset
<p>This is a subset of the Supporting data for <em>Eom, M., Han, S., Park, P. et al. Statistically unbiased prediction enables accurate denoising of voltage imaging data. Nat Methods <strong>20</strong>, 1581–1592 (2023). https://doi.org/10.1038/s41592-023-02005-8</em><br>For more details, please see the research publication "<a href="https://www.nature.com/articles/s41592-023-02005-8">Statistically unbiased prediction enables accurate denoising of voltage imaging data</a>".</p> <p>The selected <strong>subset</strong> contains low-SNR images from the Penicillium dataset in the form of a single tiff file.<br><br>The original dataset is available at <a href="../record/8176722">https://zenodo.org/record/8176722</a><br><br><br>AI4Life has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101057970. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</p>
BIR-MicroED: selected area electron diffraction datasets from static microcrystals on extra thick carbon support films (biotin, Zn(II)-methionine, Zn(II)-histidine) at 300 keV
<p>This deposition contains a series zip files each containing electron diffraction datasets in .tvips file format. Each folder collects data acquired from crystals of a particular compound under the same conditions (electron energy, temperature). Zip files are named according to the format: <em>"CompoundName</em>_<em>AcceleratingVoltage</em>_<em>Temperature</em>.zip"</p> <p>Diffraction datasets within each folder are named according to the format: <em>CompoundName</em>_static_diffraction_<em>AcceleratingVoltage</em>_<em>Temperature</em>_series#.tvips</p>
Supporting Material: Scientific Literature on the Sustainable Development Goals (SDGs). Scopus - May 2022
<p>This is a supplementary dataset for an article analysing the scientific literature related to the Sustainable Development Goals (SDGs) using Scopus-indexed journals. </p> <p>Data were retrieved in May 2022. Scopus was searched in the Title, Abstract, and Keywords fields looking for each of the 17 SDGs (search query example: TITLE-ABS-KEY (“SDG1” or "SDG 1").</p> <p>The dataset includes the following information for each of the 4808 scientific publication:</p> <p>ID: an identificatory alphanumerical number given by the authors</p> <p>Primary SDG: the main SDG the document focus on (MULTIPLE in case of more than one, ALL in case of all the SDGs)</p> <p>Year: Year of publication</p> <p>Title: title of the publication</p> <p>Abstract: abstract of the publication</p> <p>Index keywords: keywords of the publication</p> <p> </p>
Dataset in support of an AI-aided chronic mixture risk assessment along a small European river
<p>Here, we make available a dataset to perform an AI-aided multi-scenario chronic mixture risk assessment. In 2021, river-water samples were collected at six sampling sites along the Holtemme River in Central Germany using large-volume solid phase extraction. The extracts were analysed by target chemical analysis for contaminants of emerging concern. The dataset of the chemical analysis was already published and can be found at DOI: 10.5281/zenodo.10892038. Furthermore, a detailed description of the dataset can be found at DOI: 10.1016/j.dib.2024.110510.</p> <p> </p>
Infrared thermography of turbulence patterns of operational wind turbine rotor blades supported with high-resolution photography: KI-VISIR Dataset
<h2>Abstract</h2> <p><span>With increasing wind energy capacity and installation of wind turbines, new inspection techniques are being explored to examine wind turbine rotor blades, especially during operation. A common result of surface damage phenomena (such as leading-edge erosion) is the premature transition of laminar to turbulent flow on the surface of rotor blades. In the KI-VISIR (Künstliche Intelligenz Visuell und Infrarot Thermografie – Artificial Intelligence-Visual and Infrared Thermography) project, infrared thermography is used as an inspection tool to capture so-called thermal turbulence patterns (TTP) that result from such surface contamination or damage. To compliment the thermographic inspections, high-resolution photography is performed to visualise, in detail, the sites where these turbulence patterns initiate. A convolutional neural network (CNN) was developed and used to detect and localise the turbulence patterns. A unique dataset combining the thermograms and visual images of operational wind turbine rotor blades has been provided, along with the simplified annotations for the turbulence patterns. Additional tools are available to allow users to use the data requiring only basic Python programming skills.</span></p>
Code for manuscript "Organic ligands in whale excrement support iron availability and reduce copper toxicity to the surface ocean" by Monreal et al.
<p>.zip file containing GitHub repository titled "ligands-in-whale-excrement" (<a href="https://github.com/patrickmon38/ligands-in-whale-excrement/tree/main">https://github.com/patrickmon38/ligands-in-whale-excrement/tree/main</a>)<br><br><strong>README from GitHub: </strong></p> <div> <h3>Code used to generate figures for the manuscript "Organic ligands in whale excrement support iron availability and reduce copper toxicity to the surface ocean" by Monreal et al. are found in this repository.</h3> </div> <div> <p>In press at Communications Earth & Environment</p> <p> </p> </div> <p>Most data (all except .mzXML data) called in code is from Github_Data_For_Whale_Ligand_Manuscript.xlsx in this repository.</p> <p> </p> <p>Mass spec data from .mzXML files are has been depositied and is available for download in the Mass Spectrometry Interactive Virtual Environment (MassIVE). LC-ESI-MS (Orbitrap) and LC-FT-ICR-MS raw data can be accessed there under MSV000094994 (doi:10.25345/C50000B5D) and MSV000094995 (doi:10.25345/C5V98034P), respectively.</p> <p> </p> <p>html output from Rmarkdown file can be viewed directly at <a href="https://html-preview.github.io/?url=https://github.com/patrickmon38/ligands-in-whale-excrement/blob/main/Figures_for_GitHub_Whale_Excrement_Manuscript.html" rel="nofollow">https://html-preview.github.io/?url=https://github.com/patrickmon38/ligands-in-whale-excrement/blob/main/Figures_for_GitHub_Whale_Excrement_Manuscript.html</a>.</p> <p>If trying to run Rmarkdown on your own system, you will need to download the .xlsx and .mzXML files and change paths accordingly.</p> <p>Co-authors of this manuscript:</p> <h5>Patrick J. Monreal (University of Washington)</h5> <h5>Matthew S. Savoca (Stanford University)</h5> <h5>Lydia Babcock-Adams (National High Magnetic Field Laboratory)</h5> <h5>Laura E. Moore (University of Washington)</h5> <h5>Angel Ruacho (Univesrity of Washington)</h5> <h5>Dylan Hull (University of Washington)</h5> <h5>Logan J. Pallin (Unversity of California, Santa Cruz)</h5> <h5>Ross C. Nichols (Unversity of California, Santa Cruz)</h5> <h5>John Calambokidis (Cascadia Research Collective)</h5> <h5>Joseph A. Resing (Unviersity of Washington/CICOES/NOAA)</h5> <h5>Ari S. Friedlaender (Unversity of California, Santa Cruz)</h5> <h5>Jeremy Goldbogen (Stanford University)</h5> <h5>Randelle M. Bundy (University of Washington)</h5> <p> </p> <div> </div> <div><strong>If there are issues or questions with the code, author for contact is Patrick Monreal (<a href="mailto:pmonreal@uw.edu">pmonreal@uw.edu</a>).</strong></div>
Data supporting the Master thesis "Monitoring von Open Data Praktiken - Herausforderungen beim Auffinden von Datenpublikationen am Beispiel der Publikationen von Forschenden der TU Dresden"
<p>Data supporting the Master thesis "Monitoring von Open Data Praktiken - Herausforderungen beim Auffinden von Datenpublikationen am Beispiel der Publikationen von Forschenden der TU Dresden" (Monitoring open data practices - challenges in finding data publications using the example of publications by researchers at TU Dresden) - Katharina Zinke, Institut für Bibliotheks- und Informationswissenschaften, Humboldt-Universität Berlin, 2023</p> <p>This ZIP-File contains the data the thesis is based on, interim exports of the results and the R script with all pre-processing, data merging and analyses carried out. The documentation of the additional, explorative analysis is also available. The actual PDFs and text files of the scientific papers used are not included as they are published open access.</p> <p>The folder structure is shown below with the file names and a brief description of the contents of each file. For details concerning the analyses approach, please refer to the master's thesis (publication following soon).</p> <p>## Data sources </p> <p>Folder 01_SourceData/</p> <p>- PLOS-Dataset_v2_Mar23.csv (PLOS-OSI dataset)</p> <p>- ScopusSearch_ExportResults.csv (export of Scopus search results from Scopus)</p> <p>- ScopusSearch_ExportResults.ris (export of Scopus search results from Scopus)</p> <p>- Zotero_Export_ScopusSearch.csv (export of the file names and DOIs of the Scopus search results from Zotero)</p> <p>## Automatic classification </p> <p>Folder 02_AutomaticClassification/</p> <p>- (NOT INCLUDED) PDFs folder (Folder for PDFs of all publications identified by the Scopus search, named AuthorLastName_Year_PublicationTitle_Title) </p> <p>- (NOT INCLUDED) PDFs_to_text folder (Folder for all texts extracted from the PDFs by ODDPub, named AuthorLastName_Year_PublicationTitle_Title)</p> <p>- PLOS_ScopusSearch_matched.csv (merge of the Scopus search results with the PLOS_OSI dataset for the files contained in both)</p> <p>- oddpub_results_wDOIs.csv (results file of the ODDPub classification)</p> <p>- PLOS_ODDPub.csv (merge of the results file of the ODDPub classification with the PLOS-OSI dataset for the publications contained in both)</p> <p>## Manual coding </p> <p>Folder 03_ManualCheck/</p> <p>- CodeSheet_ManualCheck.txt (Code sheet with descriptions of the variables for manual coding)</p> <p>- ManualCheck_2023-06-08.csv (Manual coding results file)</p> <p>- PLOS_ODDPub_Manual.csv (Merge of the results file of the ODDPub and PLOS-OSI classification with the results file of the manual coding)</p> <p>## Explorative analysis for the discoverability of open data<br> <br>Folder04_FurtherAnalyses </p> <p>Proof_of_of_Concept_Open_Data_Monitoring.pdf (Description of the explorative analysis of the discoverability of open data publications using the example of a researcher) - in German</p> <p>## R-Script </p> <p>Analyses_MA_OpenDataMonitoring.R (R-Script for preparing, merging and analyzing the data and for performing the ODDPub algorithm)</p>
Data supporting research on journalistic production on science by press offices of universities and university centers in Rio Grande do Sul (2016)
<p>These data are part of a final paper called Scientific Journalism in Community Higher Education Institutions in Rio Grande do Sul. The work was developed at the University of Vale do Taquari - Univates, between 2016 and 2017. The data is in Portuguese. The abstract of the paper is available below. Science occupies an important place in today's society. It is what has allowed us to reach our current stages of intellectual development and also to achieve memorable feats as a species. Science is produced, to a large extent, in the academic environment. This monograph focuses its efforts on trying to understand how 15 universities and university centers in the state of Rio Grande do Sul, partners in the Consortium of Community Universities of Rio Grande do Sul (Comung), carry out the dissemination of their academic production. It is understood that it is important to disseminate scientific information to the population so that individuals can critically evaluate the actions developed in the field of science. The general objective of this study is to investigate the production of scientific news in higher education institutions linked to Comung, as well as to characterize the relationships between the actors and processes related to the journalistic dissemination of science produced in these same institutions. The qualitative and quantitative analysis of the scientific dissemination texts was directed to the news published by the organizations, through an exploratory study that mapped all the production of the press offices between January and August 2016. Subsequently, a qualitative analysis of the discourse of press officers was carried out, after applying a questionnaire. Throughout the investigation, the hypothesis that the institutions disseminate their scientific production was confirmed, but they do so in markedly different ways. The study is available at this link: https://www.univates.br/bdu/items/4cdf0835-1fbe-425a-a216-2a511e9aabf8. </p>
DATA SUPPORTING RESEARCH ON THE DEVELOPMENT OF PALEONTOLOGY IN BRAZIL (NETWORK VISUALIZATIONS)
<p>The documents made available present the data set that was processed in Lucas George Wendt's dissertation, presented in 2024 in the Postgraduate Program in Information Science (PPGCIN) of the Federal University of Rio Grande do Sul (UFRGS). The study is entitled: Brazilian Paleontology: a scientometric analysis based on the Lattes Curriculum. The abstract is as follows. This research sought to carry out a scientometric analysis of Paleontology in Brazil based on data collected in the Lattes Curriculum. The general objective of this dissertation is to analyze the scientific field of Paleontology diachronically and through a scientometric study - which will be explained based on the personal information of the researchers collected in their profiles and the scientific literature produced and registered in the Lattes Curriculum of the Lattes Platform. The literature review presented the concepts of Information Science, the area that, in this study, seeks to understand Paleontology through its research instruments; Scientific Communication, the main subject analyzed in this study; Metric Information Studies, the theoretical-methodological framework used in this research; Scientometrics, the theoretical scope used to understand in greater depth the constitution of the field of national Paleontology. Finally, references were also presented that help in the understanding of Paleontology in its national, South American, North American and European contexts. The research used a mixed approach of qualitative and quantitative elements. The data were generated from the CVs of researchers registered on the Lattes Platform, collected using the Brapci Bibliometric Tools tool and analyzed in specific software for metric analysis. To achieve the research objectives, data from 1,465 researcher profiles were analyzed. Regarding the full articles published in journals, 43,333 articles were considered valid. Regarding the keywords of the articles, 91,922 keywords were analyzed for word clouds and 84,771 for relationship networks. Of the academic orientations, 1,182 profiles generated 51,400 valid orientations. The aspect of the current employment relationship had 1,256 profiles considered. Regarding academic backgrounds, 1,465 profiles generated 4,556 academic backgrounds analyzed. The main contribution of this study is the realization of an unprecedented mapping of the panorama of Paleontology in Brazil, since there are no other studies that establish the same relationships that this research sought to establish. Regarding the results, based on the data collected and analyzed, the general metric indicators linked to the scientific production associated with Brazilian Paleontology were presented based on the information collected in the Lattes Curriculum; the directions of research in Paleontology that currently constitute this field in Brazil were mapped, as well as their thematic associations with other fields of knowledge; the training of PhD researchers who work with Paleontology or who have their production associated with Paleontology in terms of their academic training was characterized; and where the scientific knowledge in Paleontology or associated with Paleontology is produced was identified. The results of this study are relevant to understanding Brazilian Paleontology, highlighting its national orientation in fossil studies, doctoral training in local institutions and predominant activity in national organizations. These elements are important to consolidate Brazilian paleontological science globally. Regarding interdisciplinary relations, a clear proximity between Paleontology and Geosciences is observed, influenced by the history and current dynamics of the field. The study is available in full at this link: https://lume.ufrgs.br/handle/10183/278682.</p>
Supporting files for Turečková et al. 2024 "A New Abscisic Acid Conjugate, ABA‑L‑Glutamate, Determined in Different Plant Species by Combined Immunoaffinity Chromatography‑Tandem Mass Spectrometry"
Open the record for dataset details and reuse information.
Supporting data for "Benchmarking the integration of hexagonal boron nitride crystals and thin films into graphene-based van der Waals heterostructures"
<p>Dataset for the publication "Benchmarking the integration of hexagonal boron nitride crystals and thin films into graphene-based van der Waals heterostructures"</p>
[Data] Acoustic emission signature of martensitic transformation in Laser Powder Bed Fusion of Ti6Al4V-Fe, supported by operando X-ray diffraction
<p>The dataset for this study focuses on investigating Acoustic Emission (AE) monitoring in the Laser Powder Bed Fusion (LPBF) process, using premixed Ti6Al4V-(x wt%) Fe, where x = 0, 3, and 6. By employing a structure-borne AE sensor, we analyze AE data statistically, uncovering notable discrepancies within the 50-750 kHz frequency range. Leveraging Machine Learning (ML) methodologies, we accurately predict composition for particular processing conditions. These fluctuations in AE signals primarily arise from unique microstructural alterations linked to martensitic phase transformation, corroborated by operando synchrotron X-ray diffraction and post-mortem SEM and EBSD analysis. Moreover, cracks are evident at the periphery of the printed parts, stemming from local inadequate heat input during the blending of Ti6Al4V with added Fe powder. These cracks are discerned via AE signals subsequent to the cessation of the laser beam, correlating with the presence of brittle intermetallics at their junction. This study highlights for the first time the potential of AE monitoring in reliably detecting footprints of martensitic transformations during the LPBF process. Additionally, AE is shown to prove valuable for assessing crack formations, particularly in scenarios involving premixed powders and necessitating precise selection of processing parameters, notably at part edges.</p>
Dataset supporting the tool 'delfies: a Python package for the detection of DNA breakpoints with neo-telomere addition'
<h2>Purpose</h2> <p><br>These data can be used to test my tool <a href="https://github.com/bricoletc/delfies">delfies </a>on real data, to get a concrete sense of its inputs/outputs and test that it is <br>properly installed.</p> <h2>Description</h2> <h3>Genome</h3> <p>I downloaded the genome of <em>Oscheius onirici</em>, accession: <a href="https://www.ebi.ac.uk/ena/browser/view/GCA_932521025.1">GCA_932521025</a>.</p> <p>I subsampled the genome to the last 2kbp of chromosome I, which contains an elimination breakpoint, <br>using `seqkit` v2.8.2, giving the FASTA file in this release.</p> <h3>Sequencing data</h3> <p>I then downloaded the following sequencing data for *O. onirici*, from the European Nucleotide Archive:</p> <ul> <li>ERR5967937: Illumina NovaSeq 6000 paired end short reads. Reads are 2x150bp with average per-base quality of Q27.</li> <li>ERR10796202: Oxford Nanopore PromethION long reads. Reads have average length 11.9kbp and average per-base quality Q11.4.</li> <li>ERR7979900: Pacific Biosciences (PacBio) Sequel II long reads. Reads have average length 11.1kbp and average per-base quality Q28.<br><br></li> </ul> <p>And aligned them to the above genome with `minimap2` version 2.26-r1175, using the following presets: <br>"map-ont" for the Nanopore data, "map-hifi" for the PacBio data, "sr" for the Illumina data.</p> <p>After sorting with `samtools`, this gives the BAM files in this release.</p> <h3>Running delfies</h3> <p>I then ran `delfies` version 0.6.0 on each BAM and genome, as:</p> <p>```sh<br>delfies --threads 16 \<br> --telo_forward_seq TTAGGC \<br> --breakpoint_type all \<br> --min_mapq 20 \<br> --min_supporting_reads 6 \<br> \${genome} \${bam} \${odirname}<br>```</p> <p>The three resulting output directories are in this release, prefixed with `delfies_`.</p> <p><strong>A single, identical breakpoint is found using all three BAMs</strong> (see files '*breakpoint_locations.bed').</p> <h3>Data source</h3> <p>The above raw data were produced and released by the Wellcome Sanger Institute as part of projects <br><a href="https://www.ebi.ac.uk/ena/browser/view/PRJEB51305">PRJEB51305</a> and <a href="https://www.ebi.ac.uk/ena/browser/view/PRJEB59023">PRJEB59023.</a></p>
Supporting data for the manuscript "Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads"
<p>Supporting data for the manuscript "Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads".</p> <p>The archive contains files that are necessary to reproduce the cell line benchmarks from the paper, including:</p> <ul> <li>Scripts and command lines</li> <li>Original VCF outpurs of all tools used in benchmarking</li> <li>Minda evaluations and truthset VCF files</li> <li>Full Severus outputs + visualizations</li> <li>truvari calls</li> </ul>
Supporting datasets for Mexico-Texas heatwave analysis
<p>This repository contains supporting data for the manuscript titled "Contributions of Atmospheric Ridging and Low Soil Moisture to the Record-Breaking June 2023 Mexico-Texas Heatwave" by Kalashnikov et al. (2025). Please contact me with any questions at dkalashnikov@ucmerced.edu. -Dmitri</p>
Supporting Materials for "Standardised Drone Procedures for Phytosociological Data Collection"
<div> <div> <div>By integrating drone-derived data with traditional Braun-Blanquet field methods, this study demonstrates the efficacy of a standardized drone-truthing methodology for precise drone plot assignments. </div> </div> </div> <div> <p>The provided dataset includes:</p> <ul> <li><strong>"drone_images.zip"</strong>: High-resolution drone imagery of forest and grassland plots.</li> <li><strong>"R_Script_and_Data.zip"</strong>: Species-abundance data obtained through expert photo interpretation and an R script for performing supervised classification.</li> </ul> <p> </p> </div>
Support Data to "Interface flexibility controls the nucleation and growth of supramolecular networks"
<p><strong>Overview</strong></p> <p>This repository contains the inputs and support data for the publication "Interface Flexibility Controls the Nucleation and Growth of Supramolecular Networks," which is currently under review in Nature Chemistry.</p> <p><strong>Folder Structure</strong><br>Each subfolder is named after the primary method used to obtain the data. The internal structure may vary depending on whether it was more convenient to organize the data by the figure they were used for or by the structures analyzed. Each folder includes a detailed README.md file providing further information.<br><br><strong>Notes<br></strong>In the second version we added the data for Figure S27, which was not uploaded before due oversight.</p>
Dataset and Supporting Files for DPDGPT
<ol> <li>ContextRico-DP: consists of 1,609 UI screenshots,1,524 mobile UIs with 13 dark pattern types and 1,871 instances, and 85 web UIs with 8 dark pattern types and 144 instances.</li> <li>groundtruth.json: the ground truth of ContextRico-DP after correcting some labels.</li> <li>DPfeatures.xlsx : the DP features we summary, including visual and textual features of Dark patterns.</li> <li>correction.xlsx: a list of images that we have correct the label with, containing the image name, the original label, the new label, reason for correction.</li> </ol>
Monod supporting data
<p>The release of the Monod package (<a href="https://github.com/pachterlab/monod">https://github.com/pachterlab/monod</a>) is accompanied by the analysis of several 10X and seqFISH+ datasets to identify trends in transcriptional regulation mechanisms and parameters. This data deposit contains the copy-number data as well as the results of the Monod inference pipeline.</p> <p>monod_examples_loom_files.tar.gz: raw loom files generated by pseudoaligning Desai, Allen, Mayère, Hwang, Lu, Takei, Khateb and 10x datasets.</p> <p>monod_examples_fit_results.tar.gz: search results and figures from fitting models to the datasets.</p> <p>monod_examples.zip: a copy of the project repository (<a href="https://github.com/pachterlab/monod_examples">https://github.com/pachterlab/monod_examples</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.