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47 results for “open tools”
Introductory Motus Prioritization Tool Data (Open first)
Addressing survival and movement of priority migratory avian species of concern along the Pacific Flyway is paramount for their conservation. Yet, the migratory life stage is understudied in many avian species. The Motus radiotelemetry receiver network is an established system for tracking survival and movement of avian species. This network is an international collaborative that successfully identifies stopover site duration, connected migratory routes, post-fledging dispersal and survival, and adult survival and fidelity on a landscape-scale; parameters that cannot be easily estimated using non-tagged birds. While the Motus network is highly connected in eastern North America, the western part of the continent is lagging in coverage and connectivity, limiting the ability to obtain sample sizes large enough to robustly model demographic parameters from tagged birds. Thus, the expansion of the Motus network is a high priority for Pacific Flyway State Agencies. To date, no method exists for determining priority locations for new Motus receiving stations. With collaborations from States and the Canadian Province of British Columbia, we used eBird citizen scientist data to prioritize strategic locations for new Motus receiving stations throughout the Pacific Flyway. We model priority species’ co-occupancy of varying abundance states (i.e., absent, present, abundant, abundant in multiple weeks) with spatially varying Landsat (red and near infrared), water, land cover types, and weather covariates while accounting for variable detection with temporally varying survey effort covariates. Using occupancy model predictions, we identify high-use areas of the Pacific Flyway for establishing new Motus receiving towers that have high probabilities of intercepting high presence and /or abundance of multiple species of interest in a series of predictive occupancy maps. This package contains required files to recreate the data analysis, print out maps based on predictions from the
Zambezi dataset to "WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the Water-Energy-Food-Climate Nexus"
<p>This is the dataset used in the HESS publication "<a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-167/">WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the Water-Energy-Food-Climate Nexus</a>"</p> <p>The dataset describes the water-energy-food nexus of the Zambezi River Basin used as input to the <a href="https://github.com/RaphaelPB/WHAT-IF">WHAT-IF model</a>.</p> <p>The file Data_Organization.pdf, summarizes the available data. For more info look at the <a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-167/">publication</a> and/or <a href="https://github.com/RaphaelPB/WHAT-IF">Github</a>.</p>
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021). in Floristic, Vegetation And Climate Assessment Of The Early/Middle Miocene Parschlug Flora Indicates A Distinctly Seasonal Climate
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021).
Supporting material for: MoonIndex, an Open-Source Tool to Generate Spectral Indexes for the Moon from M3 Data
<p>Supplementary material for the paper called: MoonIndex, an Open-Source Tool to Generate Spectral Indexes for the Moon from M3 Data. The data without "indexes" in the name are map-projected M3 cubes, they can be used in the python library <i><strong>MoonIndex </strong></i>to obtain the spectral indexes stored in the files with "indexes" in the name.</p><p>This research was done on the framework of the EXPLORE project, that has received funding from the European Union's 2020 research and innovation program under grant agreement No 101004214. </p>
Open Access Finding Tools Study Sample
<p>We used five tools - Google Scholar, Lazy Scholar, Open Access Button, Unpaywall, and Kopernio - to search for open access versions of 1,000 articles to see how successful each one was. This dataset shows our results. See the ReadMe file for more detail.</p>
CaImAn: An open source tool for scalable Calcium Imaging data Analysis
<p>Advances in fluorescence microscopy enable monitoring larger brain areas <em>in-vivo</em> with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. We present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to preprocessing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good scalability on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data.</p> <p>To benchmark the performance of CaImAn we collected and combined a corpus of manual annotations from multiple labelers on nine mouse two-photon datasets, that are contained in this open access repository. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.</p> <p>In order to reproduce the results of the paper or download the annotations and the raw movies, please refer to the readme.md at:</p> <p>https://github.com/flatironinstitute/CaImAn/blob/master/use_cases/eLife_scripts/README.md</p> <p> </p>
Dataset from: Leveraging open tools to realize the potential of self-archiving: A cohort study in clinical trials
<p>This record includes the data associated with the study "Leveraging open tools to increase the potential of self-archiving to increase discoverability: A cohort study in clinical trials". The code used to generate these data is available under an open license in GitHub (<a href="https://github.com/delwen/oa-archiving-permissions">https://github.com/delwen/oa-archiving-permissions</a>). The deposit includes:</p> <p>- `intovalue.csv`: download of the IntoValue dataset (IntoValue 1 and IntoValue 2), which is actively maintained in GitHub (<a href="https://github.com/maia-sh/intovalue-data">https://github.com/maia-sh/intovalue-data</a>). The data was downloaded on 17 December 2022. More information on the generation of this dataset can be found at: <a href="https://doi.org/10.5281/zenodo.5141343">https://doi.org/10.5281/zenodo.5141343</a>. This data corresponds to the start of the trial screening flow diagram in the manuscript (n = 3,788).</p> <p>- `oa-unpaywall.csv`: dataset containing the results of the Unpaywall API query (query date: 17 December 2022). The dataset queried includes the following adaptations from `intovalue.csv`:</p> <ul> <li>As updated registry data had been downloaded on 1 November 2022, the IntoValue inclusion criteria were re-applied: <ul> <li>Interventional</li> <li>Study completion date between 2009 and 2017</li> <li>Complete based on study status</li> <li>Conducted by a German university medical center.</li> </ul> </li> <li>The dataset was further limited to: <ul> <li>Unique trials (trials from IntoValue 2 were preserved)</li> <li>Unique publications with a DOI</li> </ul> </li> </ul> <p>- `oa-syp-permissions.csv`: dataset containing the results of the Shareyourpaper API query (query date: 17 December 2022). The dataset queried is the same as in `oa-unpaywall.csv`.</p> <p>- `oa-merged-data.csv`: dataset containing the merged Unpaywall and Shareyourpaper data for all clinical trial results publications considered in this study. The dataset was further limited to journal articles that resolved in Unpaywall and were published between 2010 - 2020 (based on the publication date in Unpaywall). This is the main dataset underlying the analyses in the manuscript.</p>
Experimental data for "An open-access database for the assessment of particle damper simulation tools"
<p>Experimental data for "An open access database for the assessment of particle damper simulation tools"</p>
Alert Type Frequency Assessment of Open-Source Static Analysis Tools and Codebases
<p>This includes all data needed to replicate and validate our frequency analysis of static analysis (SA) alerts produced using open-source SA tools on several OSS codebases. It includes instructions how to get and run the SA tools, a Dockerfile to conveniently get and use the SA tools, raw SA tool output, some python scripts to parse that output, parsed SA data and aggregate analyses, and SA data augmented with CERT coding rule and CWE data. </p> <p>The SA tools used:</p> <ul> <li>clang-tidy version 15.07 </li> <li>cppcheck version 2.9 </li> <li>CERT Rosecheckers </li> </ul> <p>The codebases analyzed:</p> <ul> <li>zeek version 5.1.1</li> <li>git version 2.39.0</li> <li>dos2unix version 7.4.3</li> </ul>
Quantitative analysis of subcellular distributions with an open-source, object-based tool
<p>The subcellular localization of objects, such as organelles, proteins, or other molecules, instructs cellular form and function. Understanding the underlying spatial relationships between objects through colocalization analysis of microscopy images is a fundamental approach used to inform biological mechanisms. We generated an automated and customizable computational tool, the SubcellularDistribution pipeline, to facilitate object-based image analysis from 3D fluorescence microcopy images. To test the utility of the SubcellularDistribution pipeline, we examined the subcellular distribution of mRNA relative to centrosomes within <i>Drosophila</i> embryos. Centrosomes are microtubule-organizing centers, and RNA enrichments at centrosomes are of emerging importance. Our open-source and freely available software detected RNA distributions comparably to commercially available image analysis software. The SubcellularDistribution pipeline is designed to guide the user through the complete process of preparing image analysis data for publication, from image segmentation and data processing to visualization.</p>
OLAF, Open Library of Affective Foods: A tool to investigate the emotional impact of food in adolescents
<p>The Open Library of Affective Foods (OLAF) is a set of original food pictures created to reliably select food pictures based on the emotions they prompt, as indicated by affective ratings of valence, arousal, and dominance and by an additional food craving scale. OLAF images were designed to allow simultaneous use with affective images from the International Affective Picture System (IAPS), which is a well-known instrument to investigate emotional reactions in the laboratory. The ultimate goal of the OLAF is to contribute to understanding how food is emotionally processed in healthy individuals and in patients who suffer from eating and weight-related disorders.</p> <p>The present normative data, which was based on a large sample of an adolescent population, indicate that when viewing affective non-food IAPS images, valence, arousal, and dominance ratings were in line with expected patterns based on previous emotion research. Moreover, when viewing food pictures, affective and food craving ratings were consistent with research on food cue processing. As a whole, the data supported the methodological and theoretical reliability of the OLAF ratings, therefore providing researchers with a standardized tool to reliably investigate the emotional and motivational significance of food.</p>
Komplementäres Datenmodell für das Open-Source-Tool OptIES
<p>Der hier verfügbare Datensatz stellt das komplementäre Datenmodell zum Open-Source-Tool <a href="https://github.com/znes/OptIES" target="_blank" rel="noopener">OptIES</a> dar. </p> <p>Das Tool <code>OptIES</code> dient der Optimierung regionaler Energiesysteme und wurde im Rahmen des Forschungsprojekts "OptIES Dörpum - Offene Optimierung sektorgekoppelter regionaler Energiesysteme am Beispiel des IES Dörpum" entwickelt. Es basiert auf der offenen Software-Toolbox <a href="https://github.com/PyPSA/PyPSA" target="_blank" rel="noopener">PyPSA</a>.</p> <p>Das Forschungsprojekt <a href="https://www.uni-flensburg.de/eum/forschung/laufende-projekte/opties-doerpum" target="_blank" rel="noopener">OptIES Dörpum</a> wurde durch die <a href="https://www.uni-flensburg.de/" target="_blank" rel="noopener">Europa-Universität Flensburg</a> und die <a href="https://www.ecowert360.com" target="_blank" rel="noopener">EcoWert360° GmbH</a> bearbeitet und durch das Förderprogramm HWT Energie und Klimaschutz der <a href="https://www.eksh.org/" target="_blank" rel="noopener">Gesellschaft für Energie und Klimaschutz Schleswig-Holstein (EKSH)</a> finanziert. Es stellte die wissenschaftliche Begleitung des Praxisprojekts <a href="https://www.aktivregion-nf-nord.de/fileadmin/user_upload/KT_Klimawandel_Energie/Projekte/IES_D%C3%B6rpum/07.51_-_Beschreibung_-_Projekt_57_IES_D%C3%B6rpum.pdf" target="_blank" rel="noopener">IES Dörpum</a> dar.</p> <p>Ziel des Forschungsprojekts war es, lokale sowie nationale Herausforderungen der Energiewende durch Einordnung der kommunalen Bestrebungen in das nationale Energiesystem zu beleuchten und analysieren. Das vorliegende Tool dient der Optimierung des regionalen Energiesystems zur Untersuchung und Bewertung von Betriebsstrategien und Entwicklungspfaden des isolierten Systems. Im weiteren Projektverlauf wurde das regionale System unter Berücksichtigung des übergelagerten Gesamtsystems optimiert, um integrierte Handlungsempfehlungen für die erfolgreiche Energiewende auf kommunaler und nationaler Ebene abzuleiten.</p> <p>Unter den hochgeladenen Dateien findet sich der Projektabschlussbericht mit einer ausführlichen Darstellung des Projekts, der entwickelten Modelle und Tools, der durchgeführten Berechnungen und Analysen sowie der Ergebnisse. Zudem werden die im Rahmen des Projekts angefertigten Masterarbeiten von Matthias Winschu und Mohsen Mansouri bereitgestellt.</p> <p>Die hier veröffentlichten Daten enthalten Informationen zu Verbrauchs- und Erzeugungseinheiten innerhalb des IES Dörpum, welche für Modellrechnungen mit der Software-Toolbox notwendig sind. Die Daten enthalten sowohl synthetische als auch reale Daten in anonymisierter Form. Es werden die innerhalb von <code>PyPSA</code> als Standard definierten Einheiten verwendet (also z. B. MW). Für die Erstellung der synthetische Zeitreihen ist das Jahr 2019 als Referenzjahr herangezogen. </p> <p>Zur Erstellung der synthetischen Lastzeitreihen (Strom und Wärme) wurden Jahresgesamtverbräuche der letzten Jahre herangezogen und basierend auf der Arbeit von <a href="https://doi.org/10.1186/s42162-022-00201-y">Büttner et al.</a> zeitlich verteilt. Zur Erstellung der synthetischen Lastzeitreihen aus Ladevorgängen von Elektrofahrzeugen wurden Daten der Studie <a href="https://www.mobilitaet-in-deutschland.de/">Mobilität in Deutschland</a> innerhalb des Tools <a href="https://github.com/RAMP-project/RAMP-mobility">RAMP-mobility</a> verwendet. Die synthetische Zeitreihe der potentiellen Erzeugung durch PV ist mithilfe <a href="https://www.renewables.ninja/">renewables_ninja</a> erzeugt. Alle weiteren Daten zur Parametrisierung der Komponenten sowie die synthetische Gaslast und Eigenverbräuche der enthaltenen Biogasanlage sind aufgrund von Angaben der Biogas Dörpum GmbH festgelegt und mithilfe der Daten der <a href="https://github.com/PyPSA/technology-data">PyPSA technology data</a> ergänzt (insbesondere Kostenannahmen).</p> <p>Die gemessenen Zeitreihen ergeben sich aus dem Zeitraum Mitte Februar 2023 bis Mitte Febraur 2024. Aufgrund von Hard- und Softwarefehlern stehen keine lückenlosen Messungen zur Verfügung, insbesondere die Messungen der Ladevorgänge durch E-Fahrzeuge sind beeinträchtigt. Eine Übersicht über die Verfügbarkeit der gemessenen Daten ist der Abbildung <strong>Verfuegbarkeit_Messungen.png</strong> zu entnehmen.</p> <p><strong>buses.csv - </strong>enthält Informationen, wie Name, Energieträger und wenn anwendbar, die Spannungsebene, der verschiedenen im Modell abgebildeten Knoten. </p> <p><strong>generators.csv - </strong>stellt alle Erzeugungseinheiten mit ihren zugehörigen Knoten, installierten Kapazitäten und Kostenparametern dar. </p> <p><strong>lines.csv - </strong>beschreibt Verlauf, Länge und Art der verbauten Kabel und die zugehörigen elektrischen Parameter. </p> <p><strong>links.csv - </strong>liefert Informationen zu allen kontrollierbaren Komponenten, die zwei Knoten zweier beliebiger Energieträger verbinden. Im vorliegenden Beispiel können das KWK-Anlagen sowie Be- und Entladeeinheiten für Wärmespeicher sein. </p> <p><strong>loads.csv - </strong>beschreibt alle Verbrauchseinheiten mit deren Energieträger und zugehörigem Knoten<strong>. </strong></p> <p><strong>storage_unit.csv - </strong>stellt die vorhandenen Batteriespeicher mit Kapazitäten, Kosten- und Effizienzparametern dar. </p> <p><strong>stores.csv - </strong>enthält Informationen zu anderen Speichereinheiten für Wärme oder Biogas mit Kapazitäten, Kosten- und Effizienzparametern.</p> <p><strong>el_load_synth.csv - </strong>stellt synthetisch erzeugte stündliche elektrischen Lastzeitreihen für alle im Model abgebildeten Anschlussnehmer bereit. Unter 'BGA_AC' ist der elektrische Eigenverbrauch der Biogasanlage angenommen. 'LS1' und 'LS2' beinhalten synthetische elektrische Lastzeitreihen zum Verbrauch durch Ladevorgänge von Elektrofahrzeugen unter der Annahme von zehn ('LS1') und fünf ('LS2') Nutzer*innen.</p> <p><strong>ev_load_synth.csv - </strong>stellt zusätzliche, synthetische elektrische Lastzeitreihen zum Verbrauch durch Ladevorgänge von Elektrofahrzeugen bereit. Die verschiedenen Zeitreihen variieren in der Annahme der Anzahl der Nutzer*innen, diese ist in dem Spaltentitel vermerkt.</p> <p><strong>heat_load_synth.csv -</strong> hält synthetische Daten zum Wärmeverbrauch des angeschlossenen Nahwärmenetzes und zum Eigenverbrauch der Biogasanlage in stündlicher Auflösung bereit. Weiterhin ist der Wärmeeigenbedarf der Biogasanlage angenommen.</p> <p><strong>gas_load_synth.csv -</strong> liefert stündliche synthetische Daten zum Gasverbrauch eines Satelliten-BHKWs. </p> <p><strong>pot_pv_timeseries_synth.csv - </strong>enthält Daten zu potenziellen Stromeinspeisezeitreihen aus Photovoltaik in stündlicher Auflösung. </p> <p><strong>el_load_real.csv - </strong>stellt real gemessene elektrische Lastzeitreihen für alle im Model abgebildeten Anschlussnehmer in einer zeitlichen Auflösung von fünf Minuten bereit. Bei Messlücken größer als fünf Minuten wurde interpoliert. Unter 'BGA_AC' ist der elektrische Eigenverbrauch der Biogasanlage angenommen. 'LS1' und 'LS2' beinhaltet die selbe gemessene elektrische Lastzeitreihe zum Verbrauch durch Ladevorgänge von Elektrofahrzeugen. Diese wurde bei Messlücken >1h interpoliert. </p>
SoK: Taxonomy of Attacks on Open-Source Software Supply Chains - Visualization Tool Screenshots & Selected Papers
<p>This artifact complements the paper "SoK: Taxonomy of Attacks on Open-Source Software Supply Chains", submitted at IEEE S&P 2023.</p> <p>The papers selected during the Systematic Literature Review (SLR) are presented in the CSV file.</p> <p>This screenshots display the main features of the visualization tool that allows to explore the taxonomy of attacks on OSS supply chains, as well as the related safeguards and the selected references.</p>
TrainRuns.jl: an Open-Source Tool for Running Time Estimation - Supplement Data
<p>This additional data contains the initial data and the calculated results for comparing FBS and TrainRuns.jl.</p> <p><strong>File description</strong></p> <ul> <li><em>local.yaml</em>: input parameters for the local train</li> <li><em>freight.yaml</em>: input parameters for the freight train</li> <li><em>running_path.yaml</em>: input parameters for the path</li> <li><em>freight_FBS.csv</em>: export of calculation from FBS for the freight train</li> <li><em>freight_TrainRuns.csv</em>: export of calculation from TrainRun.jl converted in FBS units for the freight train</li> <li><em>freight_diff.csv</em>: the calculated difference between FBS.csv and TrainRuns.csv for the freight train</li> <li><em>local_FBS.csv</em>: export of calculation from FBS for the local train</li> <li><em>local_TrainRuns.csv</em>: export of calculation from TrainRun.jl converted in FBS units for the local train</li> <li><em>local_diff.csv</em>: the calculated difference between FBS.csv and TrainRuns.csv for the local train</li> <li><em>running_path.csv</em>: converted running_path.yaml for displaying</li> <li><em>comparison.tex</em>: LaTeX code for the graph in comparison.pdf</li> </ul> <p><strong>Sources</strong></p> <p>The calculations in FBS were done with the file 'Ostsachsen_V220.railml'. FBS needs a commercial license, which can be purchased. License for 'Ostsachsen_V220.railml' is Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0).<br> The file 'Ostsachsen_V220.railml' can be found at:<br> https://www.railml.org/en/user/exampledata.html (last accessed 2022-06-06 with login) -> "Real world railway examples from professional tools" -> "East Saxony railway network by FBS" -> "Ostsachsen_V220.railml"</p> <p>Other sources are mentioned in the files.</p>
Reproducible Evaluation of Open-Source Tools for Prostate Segmentation on Public Datasets
<p>Segmentation of the prostate and surrounding regions is important for a variety of clinical and research applications. Our goal is to evaluate the generalizability of publicly available state-of-the-art AI models on publicly available datasets. To compare the AI generated segmentations to the available manually annotated ground-truth, quantitative measures such as Dice Coefficient and Hausdorff distance, along with shape radiomics features, were analyzed. Our study also aims to show how cloud-based tools can be used to analyze, store, and visualize evaluation results.<strong> </strong></p> <p>Three open-source pre-trained AI prostate segmentation tools were evaluated against expert annotations, on three publicly available MRI prostate collections, available in NCI Imaging Data Commons[1]. Two pre-trained models originate from the nnU-Net framework[2], the last pre-trained model originates from Prostate158 paper[4]. ProstateX[5], QIN-Prostate-Repeatability[6] and PROSTATE-MRI-US-Biopsy[7]. Expert annotations of the the whole prostate gland, peripheral zone (PZ) and transition zone (TZ) of the prostate are available for ProstateX collection, whole prostate gland and PZ for QIN-Prostate-Repeatability collection, and whole prostate gland for PROSTATE-MRI-US-Biopsy collection.</p> <p>We rely on the DICOM standard to encode our segmentation and radiomics results. The DICOM standard aims to achieve interoperability and FAIR[10] principles. Encoding our results in DICOM representation allows us to leverage DICOM-reliant tools, such as Google Cloud Computing tools for storage,computation, analysis and visualization. Open-source DICOM-based visualization tools such as OHIF[8] viewer can also be used to look qualitatively at the AI and expert annotations and the referenced images.. DICOM Segmentation objects are used to encode the AI models predictions, using dcmqi[11], DICOM Structured Reports on the other hand are used to encode radiomics features[3] extracted from the AI and expert annotations, using dcmqi and highdicom[12]. </p> <p>This dataset is organized in three parts: </p> <p>AI_SEGMENTATIONS_DICOM.zip, AI_STRUCTURED_REPORTS_DICOM.zip and EXPERT_SRUCTURED_REPORTS_DICOM..zip. All zip files contain DICOM objects only, sorted based on DICOM attributes, following this pattern:</p> <p>PatientID/<br> └───Modality-%StudyInstanceUID/<br> └───%SeriesInstanceUID-%SeriesDescription.dcm.</p> <p>AI_SEGMENTATIONS_DICOM.zip contains all the pre-trained AI models evaluated segmentation results, encoded as DICOM Segmentation objects. AI_STRUCTURED_REPORTS_DICOM..zip contains firstorder and shape radiomics features extracted for the AI segmentation results, such as Segmentation Volume, encoded as DICOM Structured Reports. EXPERT_SRUCTURED_REPORTS_DICOM.zip contains firstorder and shape radiomics features extracted for the expert annotations (for ProstateX, QIN-Prostate-Repeatability and PROSTATE-MRI-US-Biopsy collections) stored a DICOM Structured Reports objects.</p> <p>Code repository containing evaluation cloud-based notebooks and results/metadata .csv tables is available here:<br><a href="https://github.com/ImagingDataCommons/idc-prostate-mri-analysis">https://github.com/ImagingDataCommons/idc-prostate-mri-analysis</a></p> <h2>Additional Notes</h2> <p><strong> </strong>This project has been funded in whole or in part with Federal funds from the NCI, NIH, under task order no. HHSN26110071 under contract no. HHSN261201500003l.<br>https://portal.imaging.datacommons.cancer.gov/</p> <p>nnU-Net: <a href="https://github.com/MIC-DKFZ/nnUNet">https://github.com/MIC-DKFZ/nnUNet</a> <br>Prostate158: <a href="https://github.com/Project-MONAI/model-zoo/tree/dev/models/prostate_mri_anatomy">https://github.com/Project-MONAI/model-zoo/tree/dev/models/prostate_mri_anatomy</a><br>Pyradiomics: <a href="https://github.com/AIM-Harvard/pyradiomics">https://github.com/AIM-Harvard/pyradiomics</a> <br>Highdicom: <a href="https://github.com/herrmannlab/highdicom">https://github.com/herrmannlab/highdicom</a> <br>DCMQI: <a href="https://github.com/QIICR/dcmqi">https://github.com/QIICR/dcmqi</a> <br>Github repo: <a href="https://github.com/ImagingDataCommons/idc-prostate-mri-analysis">https://github.com/ImagingDataCommons/idc-prostate-mri-analysis</a></p> <h2>Related information</h2> <p>ProstateX - <a href="https://doi.org/10.7937/K9TCIA.2017.MURS5CL">https://doi.org/10.7937/K9TCIA.2017.MURS5CL </a><br><br>QIN-Prostate-Repeatability - <a href="https://doi.org/10.7937/K9/TCIA.2018.MR1CKGND">https://doi.org/10.7937/K9/TCIA.2018.MR1CKGND</a><br><br>PROSTATE-MRI-US-BIOPSY - <a href="https://doi.org/10.7937/TCIA.2020.A61IOC1A">https://doi.org/10.7937/TCIA.2020.A61IOC1A</a></p> <h2>References </h2> <p>[1] Fedorov A, Longabaugh WJ, Pot D, Clunie DA, Pieper S, Aerts HJ, Homeyer A, Lewis R, Akbarzadeh A, Bontempi D, Clifford W. NCI imaging data commons. Cancer research. 2021 Aug 8;81(16):4188.</p> <p>[2] Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods. 2021 Feb;18(2):203-11.</p> <p>[3] Van Griethuysen JJ, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RG, Fillion-Robin JC, Pieper S, Aerts HJ. Computational radiomics system to decode the radiographic phenotype. Cancer research. 2017 Nov 1;77(21):e104-7.</p> <p>[4] Adams, Lisa C., Marcus R. Makowski, Günther Engel, Maximilian Rattunde, Felix Busch, Patrick Asbach, Stefan M. Niehues, et al. 2022. “Prostate158 - An Expert-Annotated 3T MRI Dataset and Algorithm for Prostate Cancer Detection.” Computers in Biology and Medicine 148 (September): 105817.</p> <p>[5] Natarajan, S., Priester, A., Margolis, D., Huang, J., & Marks, L. (2020). Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy) (version 2) [Data set]. The Cancer Imaging Archive. DOI: 10.7937/TCIA.2020.A61IOC1A</p> <p>[6] Fedorov, A; Schwier, M; Clunie, D; Herz, C; Pieper, S; Kikinis, R; Tempany, C; Fennessy, F. (2018). Data From QIN-PROSTATE-Repeatability. The Cancer Imaging Archive. DOI: 10.7937/K9/TCIA.2018.MR1CKGND</p> <p>[7] Natarajan, S., Priester, A., Margolis, D., Huang, J., & Marks, L. (2020). Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy) (version 2) [Data set]. The Cancer Imaging Archive. DOI: 10.7937/TCIA.2020.A61IOC1A</p> <p>[8] Open Health Imaging Foundation Viewer: An Extensible Open-Source Framework for Building Web-Based Imaging Applications to Support Cancer Research. Erik Ziegler, Trinity Urban, Danny Brown, James Petts, Steve D. Pieper, Rob Lewis, Chris Hafey, and Gordon J. Harris</p> <p>[9] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. Journal of digital imaging. 2013 Dec;26(6):1045-57.</p> <p>[10] Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, Bouwman J. The FAIR Guiding Principles for scientific data management and stewardship. Scientific data. 2016 Mar 15;3(1):1-9.</p> <p>[11] Herz C, Fillion-Robin JC, Onken M, Riesmeier J, Lasso A, Pinter C, Fichtinger G, Pieper S, Clunie D, Kikinis R, Fedorov A. DCMQI: an open source library for standardized communication of quantitative image analysis results using DICOM. Cancer research. 2017 Nov 1;77(21):e87-90.</p> <p>[12] Bridge CP, Gorman C, Pieper S, Doyle SW, Lennerz JK, Kalpathy-Cramer J, Clunie DA, Fedorov AY, Herrmann MD. Highdicom: A python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology. Journal of Digital Imaging. 2022 Aug 22:1-9.</p> <p> </p>
Dataset: An Open Dataset and Collection Tool for BMS Point Labels
<p>This dataset consists of a set of CSV files each representing a "dump" of point labels from a set of buildings. Most of the files only contain a single column (point labels); several of them also contain other dimensions such as engineering units, Haystack tags and other BACnet object properties.</p>
A New Open-source Geomagnetosphere Propagation Tool (OTSO) and its Applications - Data
<p>Data files for the computations done with OTSO for the asymptotic cones and effective cut-off rigidities for several neutron monitor stations during three ground-level enhancement events (GLE 66, 70, 71). The computations for GLE 66 and 71 were done using three external geomagnetic field models (TSY89, TSY96, TSY01). Data for the global map of effective cut-off rigidities during GLE70 is also included. Data is in CSV format.</p>
Open data and research tools
<p>From article: Government Innovation Laboratories in South America: Congruences and Peculiarities</p> <p>Journal: Brazilian Administration Review (BAR)</p>
[Tool demo] Prospector: a Tool to Find Fixes to Known Vulnerabilities of Open-Source Projects
<p> </p> <p><strong>>>>> TOOL DEMO SCREENCAST</strong>: please download file <strong><a href="https://zenodo.org/record/7974442/files/tool_demo_final.mp4?download=1">tool_demo_final.mp4</a></strong>. <strong><<<<</strong></p> <p> </p> <p><strong>ABSTRACT</strong>: Though vulnerability databases are key for monitoring known vulnerabilities in open-source projects, they rarely contain information about the code changes that fix the flaws they describe. Finding them is time-consuming and error-prone as it involves the analysis of multiple, unstructured resources. </p> <p>In this paper we present \prospector, a tool that supports mapping<br> vulnerability advisories from vulnerability databases onto the corresponding fix in the source code. \prospector employs a set of heuristics that mimics and automates the<br> strategies that would be employed by human security experts.</p> <p>Given an advisory expressed in natural language, \prospector processes the commits found in the target source code repository, ranks them based on a set of predefined rules, and produces a report that the user can inspect to<br> determine which commits to retain as the actual fix.<br> The tool is publicly available and is released under the Apache 2.0 license.</p>
QAnubis - open source coding tool
<p>QAnubis - open source coding tool</p> <p>The main activity of qualitative research is data analysis, which involves a process of collecting and analyzing narrative data, including texts, photos, audiovisual elements, and various digital file formats. Different approaches and methodologies are available for qualitative data investigation, with a greater emphasis on coding, where artifacts and their contents are categorized under a finite set of categories. Similar to other fields, efforts are made to develop and enhance computerized tools known as Computer-Assisted Qualitative Data Analysis Software (CAQDAS) that aim to increase the efficiency and effectiveness of the analysis process by providing means to assist in data identification, organization, interpretation, exploration, and integration.</p> <p>To contribute to the field of qualitative analysis, this work presents an open-source web tool called QAnubis for performing coding on PDF files while preserving their original content and formatting.</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.