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1,549 results for “Benchmarking”
LakeBeD-US: Ecology Edition - a benchmark dataset of lake water quality time series and vertical profiles
LakeBeD-US: Ecology Edition is a harmonized lake water quality dataset containing time series and vertical profiles of 21 lakes in the United States monitored by long-term monitoring institutions. These institutions include the North Temperate Lakes Long-Term Ecological Research program (NTL-LTER), Niwot Ridge Long-Term Ecological Research program (NWT-LTER), National Ecological Observatory Network (NEON), and the Carey Lab at Virginia Tech as part of the Virginia Reservoirs Long-Term Research in Environmental Biology (LTREB) site in collaboration with the Western Virginia Water Authority. The data include depth-discrete observations of 17 water quality variables including temperature, dissolved oxygen, chemical properties, Secchi depth, and more. Observations are divided into data collected by automated sensors at a relatively high temporal frequency and manually sampled data at a relatively low temporal frequency. All data were collected in situ. The data are available as Apache Parquet files, and the included R scripts give guidance on how to utilize and query the dataset in R. LakeBeD-US: Ecology Edition is an ecological science-oriented companion to LakeBeD-US: Computer Science Edition. The Computer Science Edition is available on the Hugging Face Hub.
MACREL software benchmark data set: Simulated metagenomes with sequencing quality, errors profile and abundance distributions derived from real samples
<p>These metagenomes were used in the benchmarking of FACS pipeline, and were designed after NGLess benchmark dataset (doi.org/10.5281/zenodo.2560288). Metagenomes were simulated with <a href="https://www.niehs.nih.gov/research/resources/software/biostatistics/art/index.cfm">ART-bin-MountRainier-2016.06.05</a> using real abundance profiles (.abund files) available <a href="https://doi.org/10.5281/zenodo.2560288">elsewhere</a>, and <a href="http://progenomes1.embl.de/data/repGenomes/representatives.contigs.fasta.gz">proGenomes' representative contigs</a> as reference genomes. There are available metagenomes with 40, 60 and 80 M (million of reads) based in the reference genomes and abundances of the following samples:</p> <pre><code>SAMEA2466916 SAMEA2466953 SAMEA2466965 SAMEA2621107 SAMEA2621229 SAMEA2621247</code></pre> <p>To convert them from the CRAM format back to fastq files:</p> <pre><code> ## 1. converting from cram to bam format: samtools view -b -T refgenome.fa -o file.bam file.cram ## 2. sorting the bam file: samtools sort -n file.bam -o input_sorted.bam # sort reads by identifier-name (-n) ## 3. converting from bam to fastq format: bedtools bamtofastq -i input_sorted.bam -fq output_r1.fastq -fq2 output_r2.fastq </code></pre> <p> </p>
Wind turbine blade simulations under changing environment for benchmarking SHM algorithms
<p>This data set contains the flapwise vibration response simulation of a wind turbine blade under <em>Environmental and Operational Variability</em> (EOV) as well as increasing damage. The blade’s dynamics are represented by means of a 4 element FEM of a cantilever beam, while dynamic loading corresponds to a discretized turbulent wind field calculated with the help of the software <em>TurbSim</em> for prescribed 10-minute average wind speed and turbulence. Rotation effects are ignored. The wind loading is coupled with the structural dynamics considering aeroelastic interactions, based on lift and drag forces calculated from a NACA 64-618 airfoil. Ambient temperature (10-minute average) is used to set the elasticity (Young’s) modulus of the blade material.</p> <p>While on the healthy state, the vibration response of the blade is simulated over a year of temperature and wind speed variations according to the average values measured in an area of north-central Switzerland. In addition, a week of extreme weather (abnormally high temperature in summer) and a month where the blade is subject to increasing damage are also simulated. Damage is represented as a decrement of the stiffness on a single FEM element located on the blade’s root. Damage increments linearly from 0 to 25% decrease of the total stiffness during a period of two weeks, while on the remaining two weeks a 25% stiffness decrement is sustained.</p> <p>The main aim of this data set is to be used as a benchmark of vibration based SHM methods, particularly on damage detection and localization under EOV. To this end, both the blade’s vibration response and the environmental and operational parameters (temperature and wind) used to simulate each response are provided. Further details can be found in the publication attached.</p>
Benchmark and training data for replicating financial and insurance examples
<p>This dataset contains training, validation and out-of-sample test data for two European calls and two examples of portfolio of variable annuity guarantees.</p>
Reference Dataset for Benchmarking Organ Doses Derived from Monte Carlo Simulations of CT Exams
<p>This reference dataset contains CT scanner x-ray source characteristics, filtration profile, de-identified patient image data and size characteristics, voxelized patient models, exam characteristics, x-ray tube current data, and organ dose results in tabular form from Monte Carlo (MC) simulations of abdominal/pelvis CT exams of pregnant patients. This dataset can be used for benchmarking MC simulation codes for CT dosimetry.</p>
RDF Reification Benchmark (REF) using the Biomedical Knowledge Repository (BKR)
<p>This resource can be used for benchmarking different RDF modelling solutions for statement-level metadata, namely: </p> <p>- RDF Reification,</p> <p>- Singleton Property,</p> <p>- RDF* (RDF-star). </p> <p> </p> <p>More details about this resource can be found in the following publication:</p> <p>Fabrizio Orlandi, Damien Graux, Declan O'Sullivan, "Benchmarking RDF Metadata Representations: Reification, Singleton Property and RDF*", <em>15th IEEE International Conference on Semantic Computing (ICSC)</em>, 2021.</p> <p>Pre-print available at: http://fabriziorlandi.net/pdf/2021/ICSC2021_REF-Benchmark.pdf</p> <p> </p> <p>The dataset contains 3 different versions of the Biomedical Knowledge Repository (BKR) knowledge graph, as described in:</p> <p>Vinh Nguyen, Olivier Bodenreider, Amit Sheth. "Don't Like RDF Reification? Making Statements About Statements Using Singleton Property" WWW 2014, doi: 10.1145/2566486.2567973.</p> <p>and,</p> <p>Satya S. Sahoo, Olivier Bodenreider, Pascal Hitzler, Amit Sheth and Krishnaprasad Thirunarayan. "Provenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data" in Sci Stat Database Manag. 2010; 6187: 461–470. doi: 10.1007/978-3-642-13818-8_32</p> <p> </p> <p>The 3 knowledge graphs dumps are packaged as Gzipped RDF files in Turtle (and Turtle*) syntax. </p> <p>BKR-R-fullKGdump.ttl.gz for the Reification method,</p> <p>BKR-S-fullKGdump.ttl.gz for the Singleton method,</p> <p>BKR-star-fullKGdump.ttls.gz for the RDF* (RDF-star) method.</p> <p> </p> <p>The RDF REiFication Benchmark (REF) includes also a set of SPARQL (and SPARQL*) queries that can be used to compare the performance of different triplestores.</p> <p>Details about the SPARQL queries, and the queries themselves, are included in the "REF-Benchmark.tar.gz" archive. The queries are named after the dataset they are designed for (BKR-R or BKR-S or BKR-star), plus they include a letter identifying the query set, and a query number. </p> <p>E.g. the query in the file "BKR-R_F-Q3.rq" is for the BKR-R (standard reification) dataset, it is part of the query set "F" and it is the number 3 of that set "F". Hence, the same query, but translated for the RDF* dataset in SPARQL* syntax, is contained in "BKR-star_F-Q3.rq".</p> <p>Sets "A" and "B" are derived from the queries introduced by V. Nguyen et al. in: "Don't Like RDF Reification? Making Statements About Statements Using Singleton Property" WWW 2014, doi: 10.1145/2566486.2567973. Set "F" has been designed more with RDF* in mind as part of this benchmark (see [Orlandi et al., ICSC 2021]) </p> <p> </p> <p> </p> <p> </p>
A Benchmark Dataset for Semi-Automatic Seismic Interpretation Based on a New Zealand's Seismic Survey
<p>Open access to curated datasets positively impacts on scientific research of machine learning and deep learning techniques. It is a fact that benchmarks and public datasets prepared for data science assist researchers interested in evaluating, testing, and building new data-driven methodologies for specific domain areas.</p> <p>In geosciences, there has been a remarkable growth of public datasets arranged to address machine learning challenges related to the oil and gas industry, particularly for reserves exploration and data interpretation. </p> <p>For these reasons, we present the Taranaki dataset, which is a collection of seismic horizons interpreted for a seismic stratigraphic interpretation study in the Taranaki Basin, offshore New Zealand. This data comprises fourteen seismic horizons that mark stratigraphic discordances in the Tui-3D seismic dataset. We annotated five seismic horizons on 33 inline sections and nine horizons on 19 crossline sections.</p> <p>Besides, we present the results of a series of experiments that compare a method of interpolation and a method of deep learning for seismic segmentation. The deep learning experiments evaluated the result of different image tile sizes to train the model, which is presented separately in this dataset. </p> <p>Finally, we evaluated both methodologies to interpret the horizons of this dataset in selected seismic sections. Also, we assessed the absolute error of each method with the ground truth interpretations proposed in this dataset.</p>
DCSsim (simulated) and DCSsub (sub-sampled) ChIP-seq data for benchmarking DCS tools.
<p>These data are the results from five independent runs of DCSsim and DCSsub for TF, sharp and broad mark signals in 50:50 and 100:0 regulation scenarios.</p> <p> </p> <p>Simulated data from DCSsim: simulated_ChIP-seq_data.zip</p> <ul> <li>Set1: TF 50:50</li> <li>Set2: TF 100:0</li> <li>Set3: Sharp mark 50:50</li> <li>Set4: Sharp mark 100:0</li> <li>Set5: Broad mark 50:50</li> <li>Set6: Broad mark 100:0</li> </ul> <p> </p> <p>Sub-sampled data from DCSsub: sub-sampled_ChIP-seq_data.zip</p> <ul> <li>Set1: Cebpa-ChIP-seq 50:50</li> <li>Set2: Cebpa-ChIP-seq 100:0</li> <li>Set3: H3K27ac-ChIP-seq 50:50</li> <li>Set4: H3K27ac-ChIP-seq 100:0</li> <li>Set5: H3K36me3-ChIP-seq 50:50</li> <li>Set6: H3K36me3-ChIP-seq 100:0</li> </ul>
Detection of Functionally Similar Code Clones: Data, Analysis Software, Benchmark
<p>We analysed 2,800 programs in Java and C for which we knew they are functionally similar. We checked if existing clone detection tools are able to find these functional similarities and classified the non-detected differences. We make all used data, the analysis software as well as the resulting benchmark available here.</p>
Setup of the TPV16 benchmark for dynamic rupture verification
<p>Verification benchmark for simulations with dynamic rupture and local time stepping with SeisSol, version Shaking Corals. More details can be found in the publication Uphoff et al. "Extreme scale multi-physics simulations of the tsunamigenic 2004 Sumatra megathrust earthquake", 2017 and on the project homepage (http://www.seissol.org/).</p>
Dynobench - extended Strogatz benchmark for system identification methods
<p>The dynobench repository contains a benchmark for system identification methods. Currently includes models of 10 dynamical systems: Bacterial respiration, Bar magnets, Glider, Lotka-Volterra, Predator-Prey, Shearflow and Van der Pol from the Strogatz dataset, as well as Lorenz, Coupled phase oscillators and Stuart-Landau models for dynamical systems that often appear in the research community. They also add variety to the benchmark as the Lorenz oscillator model introduces a larger set of state variables (three compared to two), and the coupled phase oscillators model is non-autonomous, which is reflected in the explicit incorporation of time in its equations.</p><p>The repository contains the 'data' folder, where the simulations of ten dynamical systems are stored, simulated under 6 different configurations of data quality. The first dimention modifies the data length and coarseness, where a 'small' dataset includes simulations of 10 seconds with a 0.1 sampling step, and a 'large' dataset includes simulations of 20 seconds with a 0.01 sampling step. The second dimention of data quality modifies the amount of noise in the data, where there are three levels of noise (no noise, moderate levels with 30 dB signal-to-noise ratio and high levels of noise with 13 dB signal-to-noise ratio). The data can be used by itself, without the need to look at the python code.</p><p>The repository also contains the main.py script by which the data can be generated. The 'src' folder contains additional python scripts that are needed to generate the data. The data were created by first randomly setting the initial values for one category, in particular a configuration of 'small', 'noise-free' and 'train' data (using inits_type = "random"). Then, all the other configurations were generated by using the same initial values. Inside the script main.py there is more information about the settings and how to run the script. </p><p>The benchmark was created as a part of the research described in the paper titled <i>Probabilistic grammars for modeling dynamical systems from coarse, noisy, and partial data, </i>written by Omejc et al.<i> </i>(in submission).</p>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWL (v2.0.0 - January 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>January 25, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/January-25%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Inverse Relations-OWLNETS (v2.0.0 - May 2020)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Instance-InverseRelations-OWLNETS</i></p><p><strong>Build Date: </strong>May 10, 2020</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-10%2C-2020">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Standard Relations-OWLNETS (v2.0.0 - January 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Standard Relations-OWLNETS</i></p><p><strong>Build Date: </strong>January 25, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/January-25%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Inverse Relations-OWLNETS (v2.0.0 - January 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Inverse Relations-OWLNETS</i></p><p><strong>Build Date: </strong>January 25, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/January-25%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Standard Relations-OWL (v2.0.0 - May 2020)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Instance-StandardRelations-OWL</i></p><p><strong>Build Date: </strong>May 10, 2020</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-10%2C-2020">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Standard Relations-OWLNETS (v2.0.0 - May 2020)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Instance-StandardRelations-OWLNETS</i></p><p><strong>Build Date: </strong>May 10, 2020</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-10%2C-2020">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Inverse Relations-OWL (v2.0.0 - May 2020)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Instance-InverseRelations-OWL</i></p><p><strong>Build Date: </strong>May 10, 2020</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-10%2C-2020">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWL (v2.0.0 - January 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-Inverse Relations-OWL</i></p><p><strong>Build Date: </strong>January 25, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/January-25%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWLNETS (v2.0.0 - May 2020)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-InverseRelations-OWLNETS</i></p><p><strong>Build Date: </strong>May 10, 2020</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-10%2C-2020">here</a>.</li></ul>
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.