Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,549
datasets available to search
ShareScore release 0.9.0
Dataset results
1,549 results for “benchmarks”
MeetingBank: A Benchmark Dataset for Meeting Summarization
<p>MeetingBank, a benchmark dataset created from the city councils of 6 major U.S. cities to supplement existing datasets. It contains 1,366 meetings with over 3,579 hours of video, as well as transcripts, PDF documents of meeting minutes, agenda, and other metadata. On average, a council meeting is 2.6 hours long and its transcript contains over 28k tokens, making it a valuable testbed for meeting summarizers and for extracting structure from meeting videos. The datasets contains 6,892 segment-level summarization instances for training and evaluating of performance.</p>
UPC Benchmark structure.
<p>This dataset was collected during the experimental evaluation of the anomaly-aware monitoring method and has been published to support the related publication titled On Anomaly-Aware Structural Health Monitoring at the Extreme Edge authored by David Arnaiz<br> (corresponding author david.arnaiz@upc.edu), Eduard Alarcón, Francesc Moll, and Xavier Vilajosana.</p> <p>The benchmark structure is a steel frame reduced-scale model structure, built and maintained by the Department of Civil and Environmental Engineering at the Polytechnic University of Catalonia (UPC). The dataset was obtained using two sensor nodes placed on the structure, and contains 275 different monitoring events. The dataset contains the 3-D acceleration data measured by the nodes, the feature vector computed by the nodes, and (for the test cases) the inference results from their anomaly detection models.</p> <p>More information about the dataset can be seen in the README file and the corresponding publication.</p> <p>This work has been made possible thanks to the funding of the Agència de Gestió d’Ajuts Universitaris de Recerca (AGAUR), grand number 2019 DI 075.</p>
AGREE: a New Benchmark for the Evaluation of Semantic Models of Ancient Greek
<p>AGREE (Ancient Greek Relatedness Embeddings Evaluation) is a benchmark for the evaluation of semantic models of Ancient Greek created at the University of Groningen (The Netherlands). More information about it can be found in the following publication:</p> <p>Silvia Stopponi, Saskia Peels-Matthey, Malvina Nissim, AGREE: a new benchmark for the evaluation of distributional semantic models of ancient Greek, <em>Digital Scholarship in the Humanities</em>, Volume 39, Issue 1, April 2024, Pages 373–392, <a href="https://doi.org/10.1093/llc/fqad087">https://doi.org/10.1093/llc/fqad087</a></p> <p> </p> <p><strong>1. Overview of the repository</strong></p> <p>This benchmark was created from a mix of expert judgements about relatedness between Ancient Greek words and model outputs validated by human experts. The evaluation items are pairs of Ancient Greek lemmas with a high semantic relatedness.</p> <p>The human judgements were collected via two questionnaires, proposing two different tasks to the experts. The evaluation items included in the AGREE benchmark are a selection of the most strictly related pairs of lemmas obtained from the two tasks. Here an overview of the contents of the repository:</p> <ul> <li><strong>1_agree_task1.json</strong> includes all the data collected with the first task. The following labels are used: <ul> <li>'pair': two Ancient Greek lemmas;</li> <li>'frequency': the number of times that the pair was suggested as related by an expert;</li> <li>'POS1': part-of-speech of the first lemma;</li> <li>'POS2': part-of-speech of the second lemma;</li> <li>'benchmark': inclusion of the pair in the AGREE benchmark ('yes'/'no').</li> </ul> </li> <li><strong>2_agree_task2.json </strong>includes all the data collected with the second task. The following labels are used: <ul> <li>'pair': two Ancient Greek lemmas;</li> <li>'origin': <ul> <li>'common_pair' = one of the two pairs proposed to all participants in the second task;</li> <li>'task1' = pairs proposed by experts in the first task;</li> <li>'models_easy_rel' = output of word2vec models, pair considered as strictly related;</li> <li>'models_task1' = pairs proposed by experts in the first task and also output by word2vec models;</li> <li>'models' = output of word2vec language models;</li> <li>'unrelated' = made up pairs of unrelated lemmas (control pairs);</li> </ul> </li> <li>'respondents': number of experts evaluating a pair;</li> <li>'score': average relatedness score given by the experts on a 0-100 scale;</li> <li>'agreement': inter-annotated agreement between all experts who evaluated the block of pairs to which the current pair belongs (when available, i.e. when the block of pairs was presented to more than one participant);</li> <li>'benchmark': inclusion of the pair in the AGREE benchmark ('yes'/'no').</li> </ul> </li> <li><strong>3_agree_final_benchmark.json </strong>includes the final selection of items that constitutes AGREE. The following labels are used: <ul> <li>'pair': two Ancient Greek lemmas;</li> <li>'origin': <ul> <li>'task1': pair either proposed more than once in the first task or proposed only once, but scored >= 70 in the second task;</li> <li>'task2': pair scored by more than one respondent in the second task and with average score >= 70.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>This updated version of the repository includes the individual answers to the two questionnaires (see files 'answers_Task1_postprocessed.xlsx' and 'raw_answers_Task2.xlsx').</p> <p> </p> <p><strong>2. Acknowledgements</strong></p> <div>This work was partially supported by the Young Academy Groningen through the PhD scholarship of Silvia Stopponi.<br> <br>We acknowledge the financial support of Anchoring Innovation. Anchoring Innovation is the Gravitation Grant research agenda of the Dutch National Research School in Classical Studies, OIKOS. It is financially supported by the Dutch ministry of Education, Culture and Science (NWO project number 024.003.012). For more information about the research programme and its results, see the website <a href="https://www.anchoringinnovation.nl">www.anchoringinnovation.nl</a>.<br> <br>We want to thank the experts of Ancient Greek around the world who shared their knowledge of Ancient Greek semantics and donated some of their precious time. Without them the creation of this benchmark would not have been possible.<br> <br>We also want to thank the many colleagues from the University of Groningen, the National Research School OIKOS, and other Universities abroad who contributed to this work with discussion and advice.</div> <div> </div> <div> <br><strong>3. Citation</strong><br>Silvia Stopponi, Saskia Peels-Matthey, Malvina Nissim, AGREE: a new benchmark for the evaluation of distributional semantic models of ancient Greek, <em>Digital Scholarship in the Humanities</em>, Volume 39, Issue 1, April 2024, Pages 373–392, <a href="https://doi.org/10.1093/llc/fqad087">https://doi.org/10.1093/llc/fqad087</a></div> <div> </div> <div> </div> <div> </div> <div> </div> <div> </div> <div> </div>
DIA benchmarking dataset (based on Gotti et al., 2021)
<p>Benchmarking dataset of DIA acquisition methods, based on the manuscript "Extensive and Accurate Benchmarking of DIA Acquisition Methods and Software Tools Using a Complex Proteomic Standard" by <a href="https://pubs.acs.org/doi/10.1021/acs.jproteome.1c00490">Gotti et al. (2021)</a>, for the purpose of R vignette describing analysis of DIA data using the <a href="https://bioconductor.org/packages/release/bioc/html/QFeatures.html">QFeatures</a> platform. The raw data are publicly available on the ProteomeXchange platform under identifier <a href="http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD026600">PXD026600</a>. Subset of files, containing 'overlapped' in the File Name, were searched using the <a href="https://github.com/vdemichev/DiaNN">DIA-NN </a>software. Here, pipeline settings, as well as FASTA files and resulting report.tsv file (here labelled as benchmarkingDIA.tsv) are provided.</p>
Data set for the journal article: Social life cycle assessment of green methanol and benchmarking against conventional fossil methanol
<p>Single File containing:</p> <ul> <li>Green Methanol Inventories: numerical data as displayed in Figure 4, Main social life cycle inventory data of the green methanol system. </li> <li>Conventional Methanol Inventories: numerical data as displayed in Figure 5, Main social life cycle inventory data of the conventional methanol system. </li> <li>Supplementary information: Diagrams and tables describing teh flowsheet of the simulations used in this work: <ul> <li> <p>Green methanol production process (flowsheet and stream table)</p> </li> <li> <p>Syngas production through Steam Methane Reforming (flowsheet and stream table)</p> </li> <li> <p>Conventional methanol production process (flowsheet and stream table)</p> </li> </ul> </li> </ul>
Phables v 1.1.0 benchmarking data and results
<p>This record contains all the benchmarking datasets and results for the Phables manuscript. The datasets used are,</p> <ol> <li>A simulated dataset containing four phages Enterobacteria phage P22, Enterobacteria phage T7, Staphylococcus phage SAP13 TA-2022 and Staphylococcus phage SAP2 TA-2022, referred to as <strong>simPhage</strong></li> <li>Water samples from Nansi Lake and Dongping Lake in n Shandong Province, China (NCBI BioProject number PRJNA756429), referred to as <strong>Lake Water</strong></li> <li>Soil samples from flooded paddy fields from Hunan Province, China (NCBI BioProject number PRJNA866269), referred to as <strong>Paddy Soil</strong></li> <li>Wastewater virome (NCBI BioProject number PRJNA434744), referred to as <strong>Wastewater</strong></li> <li>Stool samples from patients with IBD and their healthy household controls (NCBI BioProject number PRJEB7772), referred to as <strong>IBD</strong></li> </ol> <p>The simulated dataset was assembled using metaSPAdes and the real datasets were preprocessed using Hecatomb. The Lake Water dataset was also assembled using MEGAHIT (referred to as <strong>Lake Water - MEGAHIT</strong>) and metaSPAdes (referred to as <strong>Lake Water - metaSPAdes</strong>). The simulated dataset was run using Phables and evaluated using metaQUAST. All the real datasets were run using PHAMB and Phables and the genomes were evaluated using CheckV.</p>
Bugs4Q: A Benchmark of Existing Bugs to Enable Controlled Testing and Debugging Studies for Quantum Programs
<p>Realistic benchmarks of reproducible bugs and fixes are vital to good experimental evaluation of debugging and testing approaches. Bugs4Q is a benchmark of forty-two real, manually validated Qiskit bugs from three popular platforms (GitHub, StackOverflow, and Stack Exchange) in programming, supplemented with test cases to reproduce buggy behaviors. Bugs4Q Database allows users to access the bugs we collected directly. Bugs4Q Framework provides interfaces for accessing the buggy and fixed versions of the Qiskit programs and executing the corresponding source code and unit tests, facilitating reproducible empirical studies and comparisons of Qiskit program debugging and testing tools.</p>
Supplementary dataset for "Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking"
<p>The supplementary dataset for the paper "Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking". We include the splits for cora, citeseer, and pubmed, the hard negative samples, and the node2vec embeddings. We also include a jupyter file <em>read_data.ipynb</em> to show how to read the non-txt file.</p> <ul> <li>heart_test_samples.npy, heart_valid_samples.npy: the heard negative samples</li> <li>*-n2v-embedding.pt: node2vec embeddings</li> <li>test_samples_index.pt, valid_samples_index.pt: the node index of the selected samples in ogbl-ppa under HeaRT</li> <li>gnn_feature: the input feature of cora, citeseer, pubmed</li> </ul> <p>More details for our code and how to use the dataset are on the code repository: https://github.com/Juanhui28/HeaRT .</p>
Benchmark for Pairs of Papers in Semantic Scholar: 1 hop vs. 2-4 hops version 0.0
<p><strong>Benchmark for Pairs of Papers in Semantic Scholar: 1 hop vs. 2-4 hops (version 0.0)</strong></p> <p>There are two files: valid.txt and test.txt; both files use the same format.</p> <p>Columns 2 and 3 are corpus ids from Semantic Scholar.</p> <p>Column 1 is the distance between the two papers in the citation index.</p> <p>Columns 4 and 5 are the bins of the two paper, respectively. The bin is a number between 0 and 100. Papers are sorted by publication date. There are about 2M papers per bin, with the oldest papers in bin 0, and the newest papers in bin 99.</p> <p>Bin 100 is a catch-all for papers with unknown publication dates.</p> <p>head valid.txt</p> <p>1 248518397 1041744 97 51</p> <p>2 248518397 23848439 97 21</p> <p>3 248518397 4235810 97 12</p> <p>4 248518397 82079949 97 11</p> <p>1 3374228 140728989 79 0</p> <p>1 68334187 36144275 58 34</p> <p>2 68334187 7008060 58 4</p> <p>1 205881482 94036919 77 72</p> <p>2 205881482 95069173 77 53</p> <p>3 205881482 53480264 77 52</p> <p>Each row is assigned to a bin, B, where B = max(col4, col5).</p> <p> </p> <p><strong>Task</strong>: the task is to distinguish pairs of papers with distance == 1 from pairs of papers with distance > 1.</p> <p><strong>Test/Train splits</strong>: For all thresholds, 0 <= T_{train} <= 99, train a model on rows in bins between 0 and T_{train} (inclusively). Test these models on rows in all bins 0 <= T_{test} <= 99. Report average accuracy for all combinations of T_{train} and T_{test}.</p> <p> </p> <p>Average Accuracy is defined as: mean(Predict(row) == 1, Gold(row) == 1)</p> <p>The means are computed over rows in a test bin.</p> <p> </p>
Replication Package for: Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud
<h2>Replication Package for: Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud</h2><p>This is our replication package for our study on <i>Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud</i>.</p><p>All scalability experiments are performed with the scalability benchmarking framework <a href="https://www.theodolite.rocks/">Theodolite</a> at <a href="https://www.se.informatik.uni-kiel.de/en/research/software-performance-engineering-lab-spel">Kiel University's Software Performance Engineering Lab (SPEL)</a> or Google Cloud.</p><p>With this replication package, we provide:</p><ul><li><a href="https://www.theodolite.rocks/concepts/benchmarks-and-executions.html">Benchmark execution files</a> in <i>executions</i>,</li><li>our benchmark (raw) results in <i>results</i>, and</li><li>analysis script for our results in <i>analysis</i>.</li></ul><h3>Repeating Benchmark Executions</h3><p>All our Theodolite executions are tailored to either the SPEL cluster or the Google Cloud.</p><h4>Kiel University's Software Performance Engineering Lab (SPEL)</h4><p>The SPEL cluster consists of 5 nodes, named <i>kube1-1</i> to <i>kube1-5</i> and labeled with <i>env=dev</i>. To run them in your local cluster, make sure to provide the same infrastructure or rename node selectors in the execution files accordingly.</p><p>To install Theodolite, run:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f se-cluster-dev.yaml</p></blockquote><p>or for the vertical scalability experiment:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f se-cluster-dev.yaml -f se-cluster-dev-vertical.yaml</p></blockquote><p>See <a href="https://www.theodolite.rocks">Theodolite's documentation</a> for further usage instructions.</p><h4>Google Cloud</h4><p>In the public cloud baseline experiments, the cluster consists of 5 e2-standard-32 nodes.</p><p>To install Theodolite, run:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f gcp-cluster-dev.yaml</p></blockquote><p>For the experiments testing higher load intensities, the cluster consists of 4 e2-standard-16 nodes labeled with <i>type=infra</i> and 4 or 8 e2-standard-16 nodes with label <i>type=sut</i>. To install Theodolite in this cluster, run:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f gcp-cluster-stress.yaml</p></blockquote><p>In both cases, change the maximum load generated per load generator instance:</p><blockquote><p># Generate max. 100000 rec/sec per load generator instance export MAX_RECORDS_PER_INSTANCE=100000 kubectl patch benchmarks uc1-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]"</p></blockquote><p>See <a href="https://www.theodolite.rocks">Theodolite's documentation</a> for further usage instructions.</p><h3>Repeating Results Analysis</h3><p>To inspect, repeat, or extend our results analysis, see <i>results</i> or run the corresponding notebooks in <i>analysis</i>.</p><p>For analyzing and visualizing benchmark results, either Docker or a Jupyter installation with Python 3.7 or 3.8 is required (e.g., in a virtual environment). Moreover, we require some Python libraries, which can be installed by:</p><blockquote><p>python3.8 -m venv .venv # source .venv/bin/activate pip install -r analysis/requirements.txt</p></blockquote><p> </p>
Results and log of LLM-KG-Bench runs described in article "Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering", Meyer et al. 2023
<p>Results and logs of <a href="https://github.com/AKSW/LLM-KG-Bench">LLM-KG-Bench</a> runs described in article "Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering", Meyer et al., to appear in <a href="https://2023-eu.semantics.cc/page/accepted_posters">SEMANTICS 2023 poster track</a> proceedings.</p>
Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"
<p>Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"<br> URL: https://github.com/qianghuangwhu/benchtemp</p> <p>Openreview: https://openreview.net/forum?id=rnZm2vQq31</p> <p><br> There are 19 (15+4) benchmark temporal graph datasets:<br> reddit,<br> wikipedia,<br> mooc,<br> lastfm,<br> enron,<br> SocialEvo,<br> uci,<br> CollegeMsg,<br> TaobaoSmall,<br> CanParl,<br> Contacts,<br> Flights,<br> UNtrade,<br> USLegis,<br> UNvote,</p> <p>DGraphFin,</p> <p>TaobaoLarge,</p> <p>YoutubeReddit,</p> <p>YoutubeRedditLarge</p> <p> </p> <p><br> Each dataset has three files:<br> 1. ml_{data_name}.csv - the csv file of the Temporal Graph.</p> <p>This file have five columns with properties:</p> <p>'u': the id of the user.<br> 'i': the id of the item.<br> 'ts': the timestamp of the interaction (edge) between the user and the item.<br> 'label': the label of the interaction (edge).<br> 'idx': the index of the interaction (edge).<br> For example:</p> <p>,u,i,ts,label,idx<br> 0,1,2,0.0,0.0,1<br> 1,1,3,0.0,0.0,2<br> 2,1,4,0.0,0.0,3<br> 2. ml_{data_name}.npy - the edge features corresponding to the interactions (edges) in the the Temporal Graph..</p> <p>3. ml_{data_name}_node.npy - the initialization node features of the Temporal Graph.</p>
Optimal design of virtual screening benchmarks from in vitro screening data
<p>Input Datasets and output data for the creation of a new Benchmark dataset using PubChem Bioassay data. </p>
VIO-GNSS Dataset: Benchmarking Dataset for Sensor Fusion of Visual Inertial Odometry and GNSS Positioning
<p>This upload contains datasets for benchmarking and improving different Sensor Fusion implementations/algorithms. The documentation for these datasets can be found on <a href="https://github.com/AaltoVision/vio-gnss-dataset">GitHub</a>.</p> <p>The upload contains two datasets (version 1.0.0):</p> <ul> <li>urban_with_gnss_dead_zones (7.0 GB, ~16 minutes) <ul> <li>City streets</li> <li>A building is passed through on two occasions which makes the GNSS location signal unavailable at times.</li> <li>RTK Fix is acquired at times</li> </ul> </li> <li>suburban_nature (10.6 GB, ~19 minutes) <ul> <li>The route begins on a suburban street but quickly turns into a nature trail. Lots of vegetation</li> <li>The RTK solution is only Float or None most of the route.</li> </ul> </li> </ul> <p>Details on collecting the data:</p> <ul> <li>Software <ul> <li>The data was collected using <a href="https://github.com/AaltoVision/vio-gnss-recorder">this</a> open-source recorder. <ul> <li>Can be easily replayed using <a href="https://github.com/SpectacularAI/sdk-examples">SpectacularAI's SDK</a> (sdk-examples/python/oak/vio_replay.py)</li> </ul> </li> <li>Each dataset contains a map of the travelled route in Otaniemi, Espoo, Finland.</li> <li><strong>Necessary files to implement SLAM are included</strong> in the dataset.</li> <li>Use of NTRIP and the high precision GNSS antenna enables global positioning accuracy of only few centimeters.</li> </ul> </li> <li>Hardware <ul> <li>OAK-D stereo depth + color camera (Luxonis)</li> <li>C099-F9P GNSS module (u-blox)</li> <li>ANN-MB-00 high precision GNSS antenna (u-blox)</li> </ul> </li> </ul>
Simulated reads for benchmarking SARS-CoV-2 lineage abundance estimation
<p>To evaluate the accuracy of lineage abundance estimates from amplicon-based and whole genome-based sequencing, we simulated paired-end reads from amplicons determined by AmpliDiff, and reads spanning full genomes. Abundances of lineages are based on the relative abundance of a lineage within the dataset. The data consists of the following 8 independent datasets:</p> <ul> <li>200 bp reads from the Netherlands based on AmpliDiff amplicons (1, 2, 5 or 10 amplicons) at 1000x coverage,</li> <li>400 bp reads from the Netherlands based on AmpliDiff amplicons (1, 2, 5 or 10 amplicons) at 1000x coverage,</li> <li>200 bp reads from the Netherlands based on whole genome sequencing at 100x coverage,</li> <li>400 bp reads from the Netherlands based on whole genome sequencing at 100x coverage,</li> <li>200 bp reads from Texas based on AmpliDiff amplicons (1, 2, 5 or 10 amplicons) at 1000x coverage,</li> <li>400 bp reads from Texas based on AmpliDiff amplicons (1, 2, 5 or 10 amplicons) at 1000x coverage,</li> <li>200 bp reads from Texas based on whole genome sequencing at 100x coverage,</li> <li>400 bp reads from Texas based on whole genome sequencing at 100x coverage.</li> </ul> <p>Every independent dataset contains 20 sets of reads (generated with different random seeds). The genomes used for the Netherlands-based simulations can be obtained via GISAID through accession id <a href="https://doi.org/10.55876/gis8.230825fe">EPI_SET_230825fe</a>, and the genomes used for the Texas-based simulations can be obtained via GISAID through accession id <a href="https://doi.org/10.55876/gis8.230825pe">EPI_SET_230825pe</a>.</p>
Benchmarking of PROTAC docking and virtual screening tools - dataset
<p>This repository includes all the input files for the PROTAC docking and virtual screening benchmark. The raw data files are available on request (all raw data files combined is ~70GB and compressed ~47GB). Researchers can access and download the data to reproduce the results. Details about files/folders is included in the README.txt.</p>
FarmConners Wind Farm Flow Control Benchmark: Blind Test with CL-WINDCON Wind Tunnel Data
<p>This is the dataset used for running the fourth Blind Test of the FarmConners Wind Farm Flow Control Benchmark. The Blind Test was performed with an extensive dataset gathered while testing a cluster of three scaled wind turbines within a large boundary layer wind tunnel. The experimental dataset has been compared against the predictions provided by 5 different control-oriented wind farm flow models. The resulting comparison is described in the paper "FarmConners Wind Farm Flow Control Benchmark: Blind Test Results, Part 2", by Campagnolo et al, (2023). The dataset consists of:</p> <ol> <li>measurements of the flow within the wake shed by one or two machines, as well as measurements of the power, loads (on the rotating shaft and at tower base), and pitch/yaw/torque actuators states of the three scaled machines. The measurements have been performed under a wide range of inflow and machines operating conditions. The time series of the measured data are provided in the format of Matlab structures saved in .mat files.</li> <li>Predictions provided by the models used by the Blind Test participants</li> <li>Matlab scripts used for comparing the experimental dataset and the numerical predictions provided by the Blind Test participants</li> <li>Additional data provided to the Blind Test participants. This includes a FAST model of the scaled wind turbine and the mapping of the inflow of the empty wind tunnel. </li> </ol>
FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees
<p>The challenge of accurately segmenting individual trees from laser scanning data hinders the assessment of crucial tree parameters necessary for effective forest management, impacting many downstream applications. While dense laser scanning offers detailed 3D representations, automating the segmentation of trees and their structures from point clouds remains difficult. The lack of suitable benchmark datasets and reliance on small datasets have limited method development. The emergence of deep learning models exacerbates the need for standardized benchmarks. Addressing these gaps, the FOR-instance data represent a novel benchmarking dataset to enhance forest measurement using dense airborne laser scanning data, aiding researchers in advancing segmentation methods for forested 3D scenes.</p> <p>In this repository, users will find forest laser scanning point clouds from unamnned aerial vehicle (using Riegl sensors) that are manually segmented according to the individual trees (1130 trees) and semantic classes. The point clouds are subdivided into five data collections representing different forests in Norway, the Czech Republic, Austria, New Zealand, and Australia. </p> <p>These data are meant to be used either for developement of new methods (using the dev data) or for testing of exisitng methods (test data). The data splits are provided in the data_split_metadata.csv file.</p> <p>A full description of the FOR-instance data can be found at <a href="http://arxiv.org/abs/2309.01279">http://arxiv.org/abs/2309.01279</a> </p>
Small version of other JSON benchmarking datasets
<p>10% size version of bestbuy, google, twitter, and walmart datasets used for benchmarking JSON engines.<br> <br> Full versions:<br> <br> https://zenodo.org/record/7607865</p> <p>https://zenodo.org/record/7607889</p> <p>https://zenodo.org/record/7607891</p> <p>https://zenodo.org/record/7607882</p>
SDS_Benchmark: a testbed for shoreline mapping algorithms using satellite imagery
<p>This is an archived copy of the following Github repository: https://github.com/SatelliteShorelines/SDS_Benchmark</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.