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PopDel identifies medium-size deletions jointly in tens of thousands of genomes - Variant call sets
<p>This data set contains the variant calls sets generated by different tools for the benchmarks in the paper <a href="https://www.nature.com/articles/s41467-020-20850-5">PopDel identifies medium-size deletions simultaneously in tens of thousands of genomes</a>. It includes the VCFs/BCFs for the following test cases:</p> <ul> <li>Random deletion simulation on up to 1000 chromosome 21 samples</li> <li>1000 Genomes Project deletions inserted into simulated chromosomes 17 to 22 of up to 500 samples</li> <li>HG001 (NA12878)</li> <li>Trio of <a href="https://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG002_NA24385_son/NIST_HiSeq_HG002_Homogeneity-10953946/">HG002</a> + <a href="https://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG003_NA24149_father/NIST_HiSeq_HG003_Homogeneity-12389378/">HG003</a> + <a href="https://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG004_NA24143_mother/NIST_HiSeq_HG004_Homogeneity-14572558/">HG004</a></li> <li><a href="https://github.com/Illumina/Polaris/wiki/HiSeqX-Diversity-Cohort">Polaris Diversity cohort</a></li> <li><a href="https://github.com/Illumina/Polaris/wiki/HiSeqX-Kids-Cohort">Polaris Kids cohort</a></li> </ul> <p>Further, the long and short read reference call sets for HG001 are provided. For HG002 the reference call set and the high confidence regions by the Genome in a Bottle consortium are provided.</p> <p>For details on how the files have been created, please refer to the paper and the script repository on <a href="https://github.com/kehrlab/PopDel-scripts">GitHub</a>.</p>
BirdVox-full-night: a dataset for avian flight call detection in continuous recordings
<p>BirdVox-full-night: a dataset for avian flight call detection in continuous recordings<br> ======================================================================================<br> Version 3.0, March 2018.</p> <p><br> Created By<br> ----------</p> <p>Vincent Lostanlen (1, 2, 3), Justin Salamon (2, 3), Andrew Farnsworth (1), Steve Kelling (1), and Juan Pablo Bello (2, 3).</p> <p>(1): Cornell Lab of Ornithology (CLO)<br> (2): Center for Urban Science and Progress, New York University<br> (3): Music and Audio Research Lab, New York University</p> <p>https://wp.nyu.edu/birdvox</p> <p> </p> <p>Description<br> -----------</p> <p>The BirdVox-full-night dataset contains 6 audio recordings, each about ten hours in duration. These recordings come from ROBIN autonomous recording units, placed near Ithaca, NY, USA during the fall 2015. They were captured on the night of September 23rd, 2015, by six different sensors, originally numbered 1, 2, 3, 5, 7, and 10.</p> <p>Andrew Farnsworth used the Raven software to pinpoint every avian flight call in time and frequency. He found 35402 flight calls in total. He estimates that about 25 different species of passerines (thrushes, warblers, and sparrows) are present in this recording. Species are not labeled in BirdVox-full-night, but it is possible to tell apart thrushes from warblers and sparrrows by looking at the center frequencies of their calls. The annotation process took 102 hours.</p> <p>The dataset can be used, among other things, for the research,<br> development and testing of bioacoustic classification models, including the reproduction of the results reported in [1].</p> <p>For details on the hardware of ROBIN recording units, we refer the reader to [2].</p> <p>[1] V. Lostanlen, J. Salamon, A. Farnsworth, S. Kelling, J. Bello. BirdVox-full-night: a dataset and benchmark for avian flight call detection. Proc. IEEE ICASSP, 2018.</p> <p>[2] J. Salamon, J. P. Bello, A. Farnsworth, M. Robbins, S. Keen, H. Klinck, and S. Kelling. Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring. PLoS One, 2016.</p> <p>@inproceedings{lostanlen2018icassp,<br> title = {BirdVox-full-night: a dataset and benchmark for avian flight call detection},<br> author = {Lostanlen, Vincent and Salamon, Justin and Farnsworth, Andrew and Kelling, Steve and Bello, Juan Pablo},<br> booktitle = {Proc. IEEE ICASSP},<br> year = {2018},<br> published = {IEEE},<br> venue = {Calgary, Canada},<br> month = {April},<br> }</p> <p> </p> <p>Data Files<br> ------------</p> <p>The BirdVox-full-night_flac-audio folder contains the recordings as FLAC files, sampled at 24 kHz, with a single channel (mono).</p> <p> </p> <p>Metadata Files<br> --------------</p> <p>The BirdVox-full-night_csv-annotations folder contains JAMS files, where each row correspond to a different location in the time frequency domain (columns "Time (s)" and "Freq (Hz)").</p> <p>The approximate GPS coordinates of the sensors (latitudes and longitudes rounded to 2 decimal points) and UTC timestamps corresponding to the start of the recording for each sensor are included as CSV files in the main directory.</p> <p> </p> <p>Please acknowledge BirdVox-full-night in academic research<br> ----------------------------------------------------------</p> <p>When BirdVox-full-night is used for academic research, we would highly appreciate it if scientific publications of works partly based on this dataset cite the following publication:</p> <p>V. Lostanlen, J. Salamon, A. Farnsworth, S. Kelling, J. Bello. BirdVox-full-night: a dataset and benchmark for avian flight call detection, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2018.</p> <p>The creation of this dataset was supported by NSF grants 1125098 (BIRDCAST) and 1633259 (BIRDVOX), a Google Faculty Award, the Leon Levy Foundation, and two anonymous donors.</p> <p> </p> <p>Conditions of Use<br> -----------------</p> <p>Dataset created by Vincent Lostanlen, Justin Salamon, Andrew Farnsworth, Steve Kelling, and Juan Pablo Bello.</p> <p>The BirdVox-full-night dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license:<br> https://creativecommons.org/licenses/by/4.0/</p> <p>The dataset and its contents are made available on an "as is" basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, Cornell Lab of Ornithology is not liable for, and expressly excludes all liability for, loss or damage however and whenever caused to anyone by any use of the BirdVox-full-night dataset or any part of it.</p> <p> </p> <p>Feedback<br> -----------</p> <p>Please help us improve BirdVox-full-night by sending your feedback to:<br> vincent.lostanlen@gmail.com and af27@cornell.edu</p> <p>In case of a problem, please include as many details as possible.</p> <p> </p> <p>Acknowledgements<br> ----------------</p> <p>Jessie Barry, Ian Davies, Tom Fredericks, Jeff Gerbracht, Sara Keen, Holger Klinck, Anne Klingensmith, Ray Mack, Peter Marchetto, Ed Moore, Matt Robbins, Ken Rosenberg, and Chris Tessaglia-Hymes.</p> <p>We acknowledge that the land on which the data was collected is the unceded territory of the Cayuga nation, which is part of the Haudenosaunee (Iroquois) confederacy.</p>
FLAME Project, Open Call 3, FC5 Live Trial
<p>This dataset contains the RAW data recorded during the FC5 Live trial, Open Call 3, FLAME project.<br> The dataset contains the following data:<br> • Server-side metrics: log data of all involved nodes from CLMC (CPU, mem used, bandwidth used)<br> • Client-side metrics: Bitrate, latency, and buffering<br> • Users’ feedback: questionnaires (CSV)</p>
Präzi: From Package-based to Call-based Dependency Networks
<p>The data is originally derived from commit 6c550c8 of <a href="https://web.archive.org/web/20210129124622/https://github.com/rust-lang/crates.io-index">https://github.com/rust-lang/crates.io-index</a>. The dataset includes the following files:</p> <ul> <li><a href="/api/files/0386399e-bc22-4e16-a2e3-2d331696e1de/releases.csv?versionId=0fbac7f6-820c-4411-80ee-26967eda0652">releases.csv</a>: extracted package releases.</li> <li><a href="/api/files/0386399e-bc22-4e16-a2e3-2d331696e1de/docsrs.csv?versionId=b61f6277-0f19-4c21-bb54-04dcb19f8d43">docsrs.csv</a>: build status and compile toolchain of package releases scrapped from <a href="https://web.archive.org/web/20210118074327/https://docs.rs/">Docs.rs</a>.</li> <li><a href="/api/files/0386399e-bc22-4e16-a2e3-2d331696e1de/rustcg-corpus.tar.xz?versionId=914e7e03-0509-443f-8841-a76408f329d3">rustcg-corpus.tar.xz</a>: call graphs and type hierarchies corpus of <a href="https://web.archive.org/web/20210125175834if_/https://crates.io/">crates.io</a> in JSON format. Constructed using <a href="https://web.archive.org/web/20210129130825/https://github.com/ktrianta/rust-callgraphs">rust-callgraphs</a>.</li> <li><a href="/api/files/0386399e-bc22-4e16-a2e3-2d331696e1de/CDN.tar.xz?versionId=9e0d7baa-32c8-4594-b8b9-fb486eee7d11">CDN.tar.xz</a>: static call-based dependency network (CDN) and package-based dependency network (PDN) in JSON format. </li> </ul>
A trace of microservice time, call rate, and number of replicas
<p>This is part of the runtime traces of the Alibaba Cloud clusters, which record rows of different microservice times, call rates, and number replicas over a 30-second interval.<br><br>In this dataset, starting from zero, the fifth and sixth columns show the microservice time and call rates (with the microservice ID in column 2 and the container ID in column 3).<br>Moreover, column 4 contains the number of replicas from the microservice, denoted in column 2.</p> <p>Alibaba's original microservice trace has 24 parts, from which we extracted ten and filtered based on message queue and similarity in microservice and container IDs.<br><br>Please consider that the first two columns are timestamps.</p>
Hepatocystis alignments and variant calls
<p>This contains BAM files of <em>Hepatocystis</em> reads identified in <em>Papio</em> and <em>Chlorocebus</em> samples mapped to the <em>Hepatocystis</em> reference genome as well as major and minor allele calls for cHEP and pHEP called with ANGSD, both individually and jointly. Nucleotide alignments have been added in the latest version.</p>
BirdVox-296h: a large-scale dataset for detection and classification of flight calls
<p>BirdVox 296 hours dataset (BirdVox-296h)<br> ====================================</p> <p>Version 2.1, May 2022.</p> <p><br> Created By<br> ----------</p> <p>Andrew Farnsworth (1), Benjamin Mark Van Doren (1), Steve Kelling (1), Vincent Lostanlen (2), Justin Salamon (3), Aurora Cramer (4), Juan Pablo Bello (4)</p> <p>(1): Cornell Lab of Ornithology (CLO)<br> (2): Laboratoire des Sciences du Numérique de Nantes (LS2N), CNRS<br> (3): Adobe Research<br> (4): New York University</p> <p>https://wp.nyu.edu/birdvox<br> <br> </p> <p>Description<br> ---------------</p> <p>The BirdVox-296h dataset contains 148 audio recordings, each two hours in duration. These recordings come from ROBIN autonomous recording units, placed near Ithaca, NY, USA during the fall 2015. They were captured by nine different sensors, originally numbered 1, 2, 3, 4, 5, 6, 7, 8, and 10.<br> <br> Ornithologist Andrew Farnsworth used the Raven software to pinpoint and label every avian flight call in time and frequency. He found 26138 sound events, of which 21546 are flight calls from Passeriformes. Of those, 13385 are identifiable in terms of family, and 8669 are identifiable in terms of both family and species. The annotation process took over 600 hours.</p> <p>The dataset can be used, among other things, for the research, development and testing of machine listening models for bird migration monitoring.</p> <p> </p> <p>Data Files<br> ------------</p> <p>The BirdVox-296h_wav folder contains 148 recordings as WAV files, sampled at 24 kHz, with a single channel (mono). Each recording lasts exactly two hours and is named according to the following format:</p> <p>YYYY-MM-DD_hh-mm-ss_unitUU.wav</p> <p>Where Y means Year, M means Month, D means Day, h means hour, m means minute, and s means second. This date format corresponds to the start time of the recording file, expressed in Coordinated Universal Time (UTC).</p> <p>The field UU contains two digits corresponding to the identifier of the autonomous recording unit (i.e., bioacoustic sensor). UU is either equal to 01, 02, 03, 04, 05, 06, 07, 08, or 10. Note that 09 is absent from the list because sensor 09 failed during the acquisition campaign.</p> <p> </p> <p>Metadata Files<br> -------------------</p> <p>The BirdVox-296h_csv-annotations folder contains CSV files, one for each audio file. The columns of each CSV file are:</p> <p>ID,Time (s),Frequency (Hz),Taxonomy Code,Fine Label,Medium Label,Coarse Label</p> <p><br> "Taxonomy Code" is compliant with the BirdVoxClassify software: github.com/BirdVox/BirdVoxClassify</p> <p>"Fine Label", "Medium Label", and "Coarse Label" most often correspond to species, family and order respectively.</p> <p> </p> <p>The BirdVox-296h_gps-coordinates.csv file contains the approximate GPS coordinates of the sensors (latitudes and longitudes rounded to 2 decimal points) of all nine sensors.</p> <p> </p> <p> </p> <p>Conditions of Use<br> -----------------</p> <p>Dataset created by Andrew Farnsworth, Steve Kelling, Vincent Lostanlen, Justin Salamon, Aurora Cramer, and Juan Pablo Bello.</p> <p>The BirdVox-full-night dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license:<br> https://creativecommons.org/licenses/by/4.0/</p> <p>The dataset and its contents are made available on an "as is" basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, Cornell Lab of Ornithology is not liable for, and expressly excludes all liability for, loss or damage however and whenever caused to anyone by any use of the BirdVox-full-night dataset or any part of it.</p> <p> </p> <p>Feedback<br> -------------</p> <p>Please help us improve BirdVox-296h by sending your feedback to:<br> vincent.lostanlen@ls2n.fr and af27@cornell.edu</p> <p>In case of a problem, please include as many details as possible.</p> <p> </p> <p>Acknowledgements<br> --------------------------</p> <p>Jessie Barry, Ian Davies, Tom Fredericks, Jeff Gerbracht, Sara Keen, Holger Klinck, Anne Klingensmith, Ray Mack, Peter Marchetto, Ed Moore, Matt Robbins, Ken Rosenberg, and Chris Tessaglia-Hymes.</p> <p>We acknowledge that the land on which the data was collected is the unceded territory of the Cayuga nation, which is part of the Haudenosaunee (Iroquois) confederacy.</p>
BirdVox-25SD: a dataset of flight calls with species annotations
<pre>BirdVox 25 Species Dataset (BirdVox-25SD) ============= Version 1.0, Jan 2021. Created By ---------- Andrew Farnsworth (1), Benjamin Mark Van Doren (1), Steve Kelling (1), Vincent Lostanlen (2), Justin Salamon (3), Aurora Cramer (4), Juan Pablo Bello (4) (1): Cornell Lab of Ornithology (CLO) (2): Laboratoire des Sciences du Numérique de Nantes (LS2N), CNRS (3): Adobe Research (4): New York University https://wp.nyu.edu/birdvox Description ----------- The BirdVox 25 Species Dataset (BirdVox-25SD) contains 26,124 audio clips of avian flight calls, each ranging from about 150 ms to 500 ms in duration. The clips are extracted from the <a href="http://https://doi.org/10.5281/zenodo.4603643">BirdVox-296h</a> dataset using the corresponding annotations. The recordings come from ROBIN autonomous recording units, placed near Ithaca, NY, USA during the 2015 migration season (August - November). The dataset can be used, among other things, for the research, development and testing of bioacoustic classification models. For details on the hardware of ROBIN recording units, we refer the reader to [1]. [1] J. Salamon, J. P. Bello, A. Farnsworth, M. Robbins, S. Keen, H. Klinck, and S. Kelling. Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring. PLoS One, 2016. Changes from BirdVox 14-SD ---------------------------- This dataset builds upon the <a href="http://https://doi.org/10.5281/zenodo.3667094">BirdVox 14 Species Dataset (BirdVox-14SD)</a>, adding ~12,000 audio clips and annotations. The annotation taxonomy has been expanded to add a new order, a new family, and 11 new species. Additionally, the audio clips are more accurately aligned to the annotation times. For backwards compatibility with the BirdVox-14SD taxonomy, we include the file `birdvox25sd-to-birdvox14sd-taxonomy-code-map.csv` which maps BirdVox-25SD taxonomy codes to BirdVox-14SD taxonomy codes. Taxonomic Annotations ----------------------- Classification annotations for each flight call are given at three taxonomic levels: order, family, and species. These annotations are condensed into a three-number-code which largely follow "..". The specific numeric codes are: * Order * 1.\*.\* - Passeriformes * 2.\*.\* - Pelecaniformes * Family * 1.1.\* - American Sparrow * 1.2.\* - Cardinals * 1.3.\* - Thrushes * 1.4.\* - New World warblers * 2.1.\* - Herons * Species * 1.1.1 - American tree sparrow (ATSP) * 1.1.2 - Chipping sparrow (CHSP) * 1.1.3 - Savannah sparrow (SAVS) * 1.1.4 - White-throated sparrow (WTSP) * 1.1.5 - Song sparrow (SOSP) * 1.2.1 - Rose-breasted grosbeak (RBGR) * 1.3.1 - Gray-cheeked thrush (GCTH) * 1.3.2 - Swainson's thrush (SWTH) * 1.3.3 - Hermit thrush (HETH) * 1.3.4 - Veery (VEER) * 1.3.5 - Wood thrush (WOTH) * 1.4.1 - American redstart (AMRE) * 1.4.2 - Bay-breasted warbler (BBWA) * 1.4.3 - Black-throated blue warbler (BTBW) * 1.4.4 - Canada warbler (CAWA) * 1.4.5 - Common yellowthroat (COYE) * 1.4.6 - Mourning warbler (MOWA) * 1.4.7 - Ovenbird (OVEN) * 1.4.8 - Black-and-white warbler (BAWW) * 1.4.9 - Cape May warbler (CMWA) * 1.4.10 - Chestnut-sided warbler (CSWA) * 1.4.11 - Northern Parula (NOPA) * 1.4.12 - Wilson's warbler (WIWA) * 1.4.13 - Yellow-rumped warbler (YRWA) * 2.1.1 - Green heron (GRHE) Additionally, at any level of the taxonomy, the numeric code "0" is reserved for "other" and the code "X" refers to unknown. For example, 1.1.0 corresponds to an American Sparrow with a species outside of our scope of interest, and 1.1.X corresponds to an American Sparrow of unknown species. At the top level (family), the "other" codes (0.\*.\*) deviate from the family-order-species in order to capture a variety of other out-of-scope sounds, including anthropophony, non-avian biophony, and biophony of avians outside of the scope of interest. Please refer to `<a href="https://zenodo.org/record/5856260/files/BirdVox-296h_taxonomy.yaml">BirdVox-296h_taxonomy.yaml</a>` in <a href="http://https://doi.org/10.5281/zenodo.5856260">BirdVox-296h</a> for the details of this taxonomy structure. Data Files ------------ BirdVox-25SD contains the recordings as HDF5 files, sampled at 22,050 Hz, with a single channel (mono). Each HDF5 file contains flight call vocalizations of a particular species. The name of each HDF5 file follows the format: `BirdVox-25SD-v1pt0_{taxonomy_code}_original.h5`. The name of the HDF5 dataset in each file is "waveforms", with the corresponding key for each audio recording following the format: `unit-{unit_num}`. Conditions of Use ---------------------- Dataset created by Andrew Farnsworth, Steve Kelling, Vincent Lostanlen, Justin Salamon, Aurora Cramer, and Juan Pablo Bello. The BirdVox-25SD dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International License. The dataset and its contents are made available on an "as is" basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, CLO is not liable for, and expressly excludes all liability for, loss or damage however and whenever caused to anyone by any use of the BirdVox-25SD dataset or any part of it. Feedback ----------- Please help us improve BirdVox-25SD by sending your feedback to: vincent.lostanlen@gmail.com and auroracramer@nyu.edu In case of a problem, please include as many details as possible. Acknowledgements ------------------------ Jessie Barry, Ian Davies, Tom Fredericks, Jeff Gerbracht, Sara Keen, Holger Klinck, Anne Klingensmith, Ray Mack, Peter Marchetto, Ed Moore, Matt Robbins, Ken Rosenberg, and Chris Tessaglia-Hymes. We acknowledge that the land on which the data was collected is the unceded territory of the Cayuga nation, which is part of the Haudenosaunee (Iroquois) confederacy. The creation of this dataset was supported by NSF grants 1633259 (BIRDVOX).</pre>
BirdVox-ANAFCC: A dataset for American Northeast Avian Flight Call Classification
<p>BirdVox-ANAFCC: A dataset for American Northeast Avian Flight Call Classification<br> ===============================================================<br> Version 2.0, February 2022.</p> <p>https://wp.nyu.edu/birdvox</p> <p><br> Description<br> ---------------</p> <p>BirdVox-ANAFCC is a dataset of short audio waveforms, each of them containing a flight call from one of 14 birds of North America: four American sparrows, one cardinal, two thrushes, and seven New World warblers.<br> * American Tree Sparrow (ATSP)<br> * Chipping Sparrow (CHSP)<br> * Savannah Sparrow (SAVS)<br> * White-throated Sparrow (WTSP)<br> * Red-breasted Grosbeak (RBGR)<br> * Gray-cheeked Thrush (GCTH)<br> * Swainson's Thrush (SWTH)<br> * American Redstart (AMRE)<br> * Bay-breasted Warbler (BBWA)<br> * Black-throated Blue Warbler (BTBW)<br> * Canada Warbler (CAWA)<br> * Common Yellowthroat (COYE)<br> * Mourning Warbler (MOWA)<br> * Ovenbird (OVEN)</p> <p>It also contains other sounds which are often confused for one of the species above. These "confounding factors" encompass flight calls from other species of birds, vocalizations from non-avian animals, as well as some machine beeps.</p> <p>BirdVox-ANAFCC results from an aggregation of various smaller datasets, integrated under a common taxonomy. For more details on this taxonomy, we refer the reader to [1]:</p> <p>[1] Cramer, Lostanlen, Salamon, Farnsworth, Bello. Chirping up the right tree: Incorporating biological taxonomies into deep bioacoustic classifiers. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020.</p> <p>The second version of the BirdVox-ANAFCC dataset (v2.0) contains flight calls from the BirdVox-full-night dataset. These flight calls were present in the ICASSP 2020 benchmark but did not appear in the initial release of BirdVox-ANAFCC.</p> <p><br> Data Files<br> ------------<br> BirdVox-ANAFCC contains the recordings as HDF5 files, sampled at 22,050 Hz, with a single channel (mono). Each HDF5 file contains flight call vocalizations of a particular species. The name of each HDF5 file follows the format: `<data-source>_<taxonomy-code>_original.h5`. The name of the HDF5 dataset in each file is "waveforms", with the corresponding key for each audio recording varying in format depending on the data source.</p> <p> </p> <p>Metadata Files<br> ---------------<br> `taxonomy.yaml` details the three-level taxonomy structure used in this dataset, reflected in three-number-codes which largely follow "<family>.<order>.<species>". Additionally, at any level of the taxonomy, the numeric code "0" is reserved for "other" and the code "X" refers to unknown. For example, 1.1.0 corresponds to an American Sparrow with a species outside of our scope of interest, and 1.1.X corresponds to an American Sparrow of unknown species. At the top level (family), the "other" codes (0.\*.\*) deviate from the family-order-species in order to capture a variety of other out-of-scope sounds, including anthropophony, non-avian biophony, and biophony of avians outside of the scope of interest.</p> <p><br> Please acknowledge BirdVox-ANAFCC in academic research<br> --------------------------------------------------------------------------</p> <p>When BirdVox-ANAFCC is used for academic research, we would highly appreciate it if scientific publications of works partly based on this dataset cite the following publication:</p> <p>Cramer, Lostanlen, Salamon, Farnsworth, Bello. Chirping up the right tree: Incorporating biological taxonomies into deep bioacoustic classifiers. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020.</p> <p>The creation of this dataset was supported by NSF grants 1125098 (BIRDCAST) and 1633259 (BIRDVOX), a Google Faculty Award, the Leon Levy Foundation, and two anonymous donors.</p> <p> </p> <p>Conditions of Use<br> ----------------------</p> <p>Dataset created by Aurora Cramer, Vincent Lostanlen, Bill Evans, Andrew Farnsworth, Justin Salamon, and Juan Pablo Bello.<br> <br> The BirdVox-ANAFCC dataset is offered free of charge under the terms of the Creative Commons Attribution International License:<br> https://creativecommons.org/licenses/by/4.0/<br> <br> The dataset and its contents are made available on an "as is" basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, the authors are not liable for, and expressly exclude all liability for, loss or damage however and whenever caused to anyone by any use of the BirdVox-ANAFCC dataset or any part of it.</p> <p><br> Feedback<br> -------------</p> <p>Please help us improve BirdVox-full-night by sending your feedback to:<br> vincent.lostanlen@gmail.com and auroracramer@nyu.edu</p> <p>In case of a problem, please include as many details as possible.<br> <br> <br> Versions<br> ------------<br> 1.0, May 2020: initial version, paired with ICASSP 2020 publication.<br> 2.0, February 2022: added a missing dataset file (BirdVox-70k), updated name of first author (Aurora Cramer).<br> </p> <p><br> Acknowledgement<br> --------------------------<br> Jessie Barry, Ian Davies, Tom Fredericks, Jeff Gerbracht, Sara Keen, Holger Klinck, Anne Klingensmith, Ray Mack, Peter Marchetto, Ed Moore, Matt Robbins, Ken Rosenberg, and Chris Tessaglia-Hymes.</p> <p>We thank contributors and maintainers of the Macaulay Library and the Xeno-Canto website.</p> <p>We acknowledge that the land on which the data was collected is the unceded territory of the Cayuga nation, which is part of the Haudenosaunee (Iroquois) confederacy.</p>
Experimental Results of the REWIRE FED4FIRE+ Open Call (OC) 9 Project
<p>This repository contains the detailed experimental results of the <strong>REWIRE <em>"Experimenting with SDN-based Adaptable Non-IP Protocol Stacks in Smart-City Environments"</em></strong> project.</p> <p>This work has received funding from the EU's Horizon 2020 research and innovation programme through the 9th open call scheme of the FED4FIRE+ (grant agr. no 732638)</p>
Phase I trial of CX-5461, a first-in-class G-quadruplex stabilizer in patients with advanced solid tumors enriched for DNA-repair deficiencies (CCTG IND.231) - Variant Calls
<p>Variant Calls from Phase I trial of CX-5461, a first-in-class G-quadruplex stabilizer in patients with advanced solid tumors enriched for DNA-repair deficiencies (CCTG IND.231)</p> <p>See publication for methodology.</p>
Chick Call Dataset
<p>Chick Call Dataset comprises recordings for chick vocalization analysis. It contains recordings and call annotations of domestic chicks from the Ross 308 strain of the species Gallus gallus. The audio was recorded by placing one chick at a time in an arena within 12 hours after their hatching. All data was recorded at a sampling rate of 44.1kHz/16bits. More details can be found in the paper:</p> <p>C. Wang, E. Benetos, S. Wang, and E. Versace, "<a href="https://changhongw.github.io/publications/EUSIPCO22_chick_call_recognition.pdf">Joint Scattering for Automatic Chick Call Recognition</a>", <em>European Signal Processing Conference (EUSIPCO)</em>, 2022.</p>
Call 0816-986-118, Magang Guru Produktif di Malang
<p>Magang Guru SMK Produktif di Malang, Hubungi 0816-986-118, Griya Marketer membuka Program Info Magang Guru, Magang Guru Jurusan Pemasaran, Lowongan Magang Guru Produktif Pemasaran, Info Magang Guru OTKP, Lowongan Magang Guru Produktif OTKP.Magang Guru SMK Produktif di Malang, Hubungi 0816-986-118, Griya Marketer membuka Program Info Magang Guru, Magang Guru Jurusan Pemasaran, Lowongan Magang Guru Produktif Pemasaran, Info Magang Guru OTKP, Lowongan Magang Guru Produktif OTKP.</p>
Recordings Q&A and matchmaking sessions for call for proposals 'Open Science Infrastructure'
<p>On Thursday, July 11, and Tuesday, July 16, 2024, Open Science NL organised two online Q&A sessions combined with a matchmaking opportunity for the Open Science NL call 'Open Science Infrastructure'.</p> <p>These are the two recordings of the two Q&A sessions. The Open Science NL team has drafted a Frequently Asked Questions document addressing all the questions that came up during the meetings. This is added as a seperate text-file (PDF). The slides presented during both meetings are shared as well as a PDF.</p> <p>For more information about the call and how to apply, please go to: <a href="https://www.openscience.nl/en/calls/open-science-infrastructure" target="_blank" rel="noopener">https://www.openscience.nl/en/calls/open-science-infrastructure</a></p>
Graphic for Twitter - Call for Open Science Success Stories
<p>This graphic was created to share on Twitter to encourage people to submit compelling stories about open science practices in research to the TOPS team. This graphic was tweeted out on Dr. Chelle Gentemann's Twitter account on August 23, 2022. <a href="https://twitter.com/ChelleGentemann/status/1562049765566709764?s=20&t=kkxyz4dx1vA6obyveL5WNA">Link to tweet</a>.</p>
Forests damaged by a windthrow event called Žofia in Slovakia
<p>Forests damaged by a windthrow event, called Žofia, in Slovakia was surveyed and investigated by Mokros et al. (2017). The available data were acquired by SenseFly eBee Plus fixed-wing UAS (SenseFly, Cheseaux-sur-Lausanne, Switzerland) with GNSS RTK/PPK technology. This repository contains the data collected and described in the publication by Müllerová et al. (2023). </p> <p>Reference</p> <p>Mokroš, M., Výbošťok, J., Merganič, J., Hollaus, M., Barton, I., Koreň, M., et al., 2017. Early stage forest windthrow estimation based on unmanned aircraft system imagery. Forests 8, 306.</p> <p>Müllerová, L, Mokroš, M, Mücher, S., Paulus, G., Bartaloš, T., Gago, X., Kent, R., Michez, A., Vegetation mapping and monitoring by unmanned aerial systems (UAS)—current state and perspectives, in Unmanned Aerial Systems for Monitoring Soil, Vegetation, and Riverine Environments, ed. S. Manfreda and E. Ben Dor, Elsevier, 2023.</p>
ASSIST-IOT Open Call Project RAZOR DATASETS (INSIGHIO)
<p>Example datasets for road anomaly detection produced in the context of RAZOR Open Call ASSIST-IoT Project, carried out by INSIGHIO.</p>
Variant calls for 'Genome-wide identification of lineage and locus specific variation associated with pneumococcal carriage duration'
<p>A VCF of SNP calls used for input to GWAS in https://elifesciences.org/articles/26255</p>
Network traffic datasets with novel extended IP flow called NetTiSA flow
<p><strong>Network traffic datasets with novel extended IP flow called NetTiSA flow</strong></p> <p>Datasets were created for the paper: NetTiSA: Extended IP Flow with Time-series Features for Universal Bandwidth-constrained High-speed Network Traffic Classification -- Josef Koumar, Karel Hynek, Jaroslav Pešek, Tomáš Čejka -- which is published in The International Journal of Computer and Telecommunications Networking <a href="https://doi.org/10.1016/j.comnet.2023.110147" rel="nofollow">https://doi.org/10.1016/j.comnet.2023.110147</a><br><br>Please cite the usage of our datasets as:</p> <blockquote> <p>Josef Koumar, Karel Hynek, Jaroslav Pešek, Tomáš Čejka, "NetTiSA: Extended IP flow with time-series features for universal bandwidth-constrained high-speed network traffic classification", Computer Networks, Volume 240, 2024, 110147, ISSN 1389-1286<br><br></p> <pre><code>@article{KOUMAR2024110147, title = {NetTiSA: Extended IP flow with time-series features for universal bandwidth-constrained high-speed network traffic classification}, journal = {Computer Networks}, volume = {240}, pages = {110147}, year = {2024}, issn = {1389-1286}, doi = {https://doi.org/10.1016/j.comnet.2023.110147}, url = {https://www.sciencedirect.com/science/article/pii/S1389128623005923}, author = {Josef Koumar and Karel Hynek and Jaroslav Pešek and Tomáš Čejka} } </code></pre> </blockquote> <p>This Zenodo repository contains 23 datasets created from 15 well-known published datasets, which are cited in the table below. Each dataset contains the NetTiSA flow feature vector.<br><br> </p> <p><strong>NetTiSA flow feature vector</strong></p> <p><br>The novel extended IP flow called NetTiSA (Network Time Series Analysed) flow contains a universal bandwidth-constrained feature vector consisting of 20 features. We divide the NetTiSA flow classification features into three groups by computation. The first group of features is based on classical bidirectional flow information---a number of transferred bytes, and packets. The second group contains statistical and time-based features calculated using the time-series analysis of the packet sequences. The third type of features can be computed from the previous groups (i.e., on the flow collector) and improve the classification performance without any impact on the telemetry bandwidth.</p> <p> </p> <p><strong>Flow features</strong></p> <p>The flow features are:</p> <ul> <li><strong><em>Packets</em></strong> is the number of packets in the direction from the source to the destination IP address.</li> <li><em><strong>Packets in reverse order</strong></em> is the number of packets in the direction from the destination to the source IP address.</li> <li><strong><em>Bytes</em> </strong>is the size of the payload in bytes transferred in the direction from the source to the destination IP address.</li> <li><strong><em>Bytes in reverse order</em></strong> is the size of the payload in bytes transferred in the direction from the destination to the source IP address.</li> </ul> <p> </p> <p><strong>Statistical and Time-based features</strong></p> <p>The features that are exported in the extended part of the flow. All of them can be computed (exactly or in approximative) by stream-wise computation, which is necessary for keeping memory requirements low. The second type of feature set contains the following features:</p> <ul> <li><strong><em>Mean</em></strong> represents mean of the payload lengths of packets</li> <li><strong><em>Min</em></strong> is the minimal value from payload lengths of all packets in a flow</li> <li><strong><em>Max</em></strong> is the maximum value from payload lengths of all packets in a flow</li> <li><strong><em>Standard deviation</em></strong> is a measure of the variation of payload lengths from the mean payload length</li> <li><strong><em>Root mean square</em></strong> is the measure of the magnitude of payload lengths of packets</li> <li><strong><em>Average dispersion</em></strong> is the average absolute difference between each payload length of the packet and the mean value</li> <li><strong><em>Kurtosis</em></strong> is the measure describing the extent to which the tails of a distribution differ from the tails of a normal distribution</li> <li><em><strong>Mean of relative times</strong></em> is the mean of the relative times which is a sequence defined as <span>\(st = \{t_1 - t_1, t_2 - t_1, ..., t_n - t_1\} \)</span></li> <li><em><strong>Mean of time differences</strong></em> is the mean of the time differences which is a sequence defined as <span>\(dt = \{ t_j - t_i | j = i + 1, i \in \{1, 2, \dots, n - 1\} \}.\)</span></li> <li><em><strong>Min from time differences</strong></em> is the minimal value from all time differences, i.e., min space between packets.</li> <li><em><strong>Max from time differences</strong></em> is the maximum value from all time differences, i.e., max space between packets.</li> <li><em><strong>Time distribution</strong></em> describes the deviation of time differences between individual packets within the time series. The feature is computed by the following equation:<br><span>\(tdist = \frac{ \frac{1}{n-1} \sum_{i=1}^{n-1} \left| \mu_{\{dt_{n-1}\}} - dt_i \right| }{ \frac{1}{2} \left(max\left(\{dt_{n-1}\}\right) - min\left(\{dt_{n-1}\}\right) \right) }\)</span></li> <li><em><strong>Switching ratio</strong></em> represents a value change ratio (switching) between payload lengths. The switching ratio is computed by equation:<br><span>\(sr = \frac{s_n}{\frac{1}{2} (n - 1)}\)</span></li> </ul> <p> where <span>\(s_n\)</span> is number of switches.</p> <p> </p> <p><strong>Features computed at the collector</strong><br>The third set contains features that are computed from the previous two groups prior to classification. Therefore, they do not influence the network telemetry size and their computation does not put additional load to resource-constrained flow monitoring probes. The NetTiSA flow combined with this feature set is called the Enhanced NetTiSA flow and contains the following features:</p> <ul> <li><em><strong>Max minus min</strong></em> is the difference between minimum and maximum payload lengths</li> <li><em><strong>Percent deviation</strong></em> is the dispersion of the average absolute difference to the mean value</li> <li><em><strong>Variance</strong></em> is the spread measure of the data from its mean</li> <li><em><strong>Burstiness</strong></em> is the degree of peakedness in the central part of the distribution</li> <li><em><strong>Coefficient of variation</strong></em> is a dimensionless quantity that compares the dispersion of a time series to its mean value and is often used to compare the variability of different time series that have different units of measurement</li> <li><em><strong>Directions</strong></em> describe a percentage ratio of packet direction computed as <span>\(\frac{d_1}{ d_1 + d_0}\)</span>, where <span>\(d_1\)</span> is a number of packets in a direction from source to destination IP address and <span>\(d_0\)</span> the opposite direction. Both <span>\(d_1\)</span> and <span>\(d_0\)</span> are inside the classical bidirectional flow.</li> <li><em><strong>Duration</strong></em> is the duration of the flow</li> </ul> <p> </p> <p>The NetTiSA flow is implemented into IP flow exporter <a href="https://github.com/CESNET/ipfixprobe">ipfixprobe</a>.</p> <p> </p> <p><strong>Description of dataset files</strong></p> <p>In the following table is a description of each dataset file:</p> <table> <tbody> <tr> <td> <p><strong>File name</strong></p> </td> <td> <p><strong>Detection problem</strong></p> </td> <td> <p><strong>Citation of the original raw dataset</strong></p> </td> </tr> <tr> <td>botnet_binary.csv </td> <td>Binary detection of botnet </td> <td>S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. </td> </tr> <tr> <td>botnet_multiclass.csv </td> <td>Multi-class classification of botnet </td> <td>S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. </td> </tr> <tr> <td>cryptomining_design.csv </td> <td>Binary detection of cryptomining; the design part </td> <td>Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 </td> </tr> <tr> <td>cryptomining_evaluation.csv </td> <td>Binary detection of cryptomining; the evaluation part </td> <td>Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 </td> </tr> <tr> <td>dns_malware.csv </td> <td>Binary detection of malware DNS </td> <td>Samaneh Mahdavifar et al. Classifying Malicious Domains using DNS Traffic Analysis. In DASC/PiCom/CBDCom/CyberSciTech 2021, pages 60–67. IEEE, 2021. </td> </tr> <tr> <td>doh_cic.csv </td> <td>Binary detection of DoH </td> <td>Mohammadreza MontazeriShatoori et al. Detection of doh tunnels using time-series classification of encrypted traffic. In DASC/PiCom/CBDCom/CyberSciTech 2020, pages 63–70. IEEE, 2020 </td> </tr> <tr> <td>doh_real_world.csv </td> <td>Binary detection of DoH </td> <td>Kamil Jeřábek et al. Collection of datasets with DNS over HTTPS traffic. Data in Brief, 42:108310, 2022 </td> </tr> <tr> <td>dos.csv </td> <td>Binary detection of DoS </td> <td>Nickolaos Koroniotis et al. Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Gener. Comput. Syst., 100:779–796, 2019. </td> </tr> <tr> <td>edge_iiot_binary.csv </td> <td>Binary detection of IoT malware </td> <td>Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022. </td> </tr> <tr> <td>edge_iiot_multiclass.csv </td> <td>Multi-class classification of IoT malware </td> <td>Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022. </td> </tr> <tr> <td>https_brute_force.csv </td> <td>Binary detection of HTTPS Brute Force </td> <td>Jan Luxemburk et al. HTTPS Brute-force dataset with extended network flows, November 2020 </td> </tr> <tr> <td>ids_cic_binary.csv </td> <td>Binary detection of intrusion in IDS </td> <td>Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018. </td> </tr> <tr> <td>ids_cic_multiclass.csv </td> <td>Multi-class classification of intrusion in IDS </td> <td>Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018. </td> </tr> <tr> <td>unsw_binary.csv </td> <td>Binary detection of intrusion in IDS </td> <td>Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015. </td> </tr> <tr> <td>unsw_multiclass.csv </td> <td>Multi-class classification of intrusion in IDS </td> <td>Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015. </td> </tr> <tr> <td>iot_23.csv </td> <td>Binary detection of IoT malware </td> <td>Sebastian Garcia et al. IoT-23: A labeled dataset with malicious and benign IoT network traffic, January 2020. More details here https://www.stratosphereips.org /datasets-iot23 </td> </tr> <tr> <td>ton_iot_binary.csv </td> <td>Binary detection of IoT malware </td> <td>Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021 </td> </tr> <tr> <td>ton_iot_multiclass.csv </td> <td>Multi-class classification of IoT malware </td> <td>Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021 </td> </tr> <tr> <td>tor_binary.csv </td> <td>Binary detection of TOR </td> <td>Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. </td> </tr> <tr> <td>tor_multiclass.csv </td> <td>Multi-class classification of TOR </td> <td>Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. </td> </tr> <tr> <td>vpn_iscx_binary.csv </td> <td>Binary detection of VPN </td> <td>Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. </td> </tr> <tr> <td>vpn_iscx_multiclass.csv </td> <td>Multi-class classification of VPN </td> <td>Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. </td> </tr> <tr> <td>vpn_vnat_binary.csv </td> <td>Binary detection of VPN </td> <td>Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022 </td> </tr> <tr> <td>vpn_vnat_multiclass.csv </td> <td>Multi-class classification of VPN </td> <td>Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022 </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p>
MediaFutures Open Calls Data-Set
<p>The Data-Set includes the data collected through the cascade funding open calls carried out during the H2020 MediaFutures project with the title “MediaFutures, Data-driven innovation hub for the media value chain”.</p> <p>The responsible and innovative use of data is instrumental in today's digitalised media industry. The EU-funded MediaFutures project has addressed this challenge by reshaping the media value chain. It has set up a virtual European data innovation hub to support entrepreneurial and innovative projects. It has also established a participatory inclusive innovation program encouraging synergies between businesses and creators and organised a competition to identify innovative digital entrepreneurs, creatives and data-empowered solutions. By delivering data and experimentation facilities to the winners, the project has showcased and improved their ideas. It has also facilitated the technical, legal, business and sustainability mentoring of businesses and artists and helped them achieve further access to funding. The virtual European data innovation hub has been supported by an international network of European organisations.</p> <p>The project has received funding from the European Union’s Horizon2020 research and innovation programme under grant agreement 951962.</p> <p>The file contains data from the open calls, webinars, matchmaking events and help-desk activities carried out during the MediaFutures project. It includes data such as the number of applications received, data regarding eligibility and in-eligibility of applications, from which country the applications came, how many projects were evaluated and funded, data on the gender and ethnicity of applicants, etc.</p> <p>For more information and context related to the data, see also the following deliverables published on the MediaFutures website (https://mediafutures.eu/resources/): D1.4: Summary of Calls v1, D1.5: Summary of Open Calls v2, D1.6: Summary of Calls v3.</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.