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COCO, LVIS, Open Images V4 classes mapping
<p>This repository contains a mapping between the classes of COCO, LVIS, and Open Images V4 datasets into a unique set of 1460 classes.</p> <p>COCO [Lin et al 2014] contains 80 classes, LVIS [gupta2019lvis] contains 1460 classes, Open Images V4 [Kuznetsova et al. 2020] contains 601 classes.</p> <p>We built a mapping of these classes using a semi-automatic procedure in order to have a unique final list of 1460 classes. We also generated a hierarchy for each class, using <a href="https://wordnet.princeton.edu/">wordnet</a></p> <p>This repository contains the following files:</p> <ul> <li><em>coco_classes_map.txt</em>, contains the mapping for the 80 coco classes</li> <li><em>lvis_classes_map.txt</em>, contains the mapping for the 1460 coco classes</li> <li><em>openimages_classes_map.txt</em>, contains the mapping for the 601 coco classes</li> <li><em>classname_hyperset_definition.csv</em>, contains the final set of 1460 classes, their definition and hierarchy</li> <li><em>all-classnames.xlsx</em>, contains a side-by-side view of all classes considered</li> </ul> <p>This mapping was used in VISIONE [Amato et al. 2021, Amato et al. 2022] that is a content-based retrieval system that supports various search functionalities (text search, object/color-based search, semantic and visual similarity search, temporal search). For the object detection VISIONE uses three pre-trained models: VfNet [Zhang et al. 2021] (trained on COCO dataset), Mask R-CNN [He et al. 2017] (trained on LVIS), and a <a href="http://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1">Faster R-CNN+Inception ResNet</a> (trained on the Open Images V4).</p> <p>This is repository is released under a Creative Commons Attribution license, please cite the following paper if you use it in your work in any form:</p> <blockquote> <pre>@inproceedings{amato2021visione, title={The visione video search system: exploiting off-the-shelf text search engines for large-scale video retrieval}, author={Amato, Giuseppe and Bolettieri, Paolo and Carrara, Fabio and Debole, Franca and Falchi, Fabrizio and Gennaro, Claudio and Vadicamo, Lucia and Vairo, Claudio}, journal={Journal of Imaging}, volume={7}, number={5}, pages={76}, year={2021}, publisher={Multidisciplinary Digital Publishing Institute} } </pre> </blockquote> <p> </p> <p> </p> <p><em><strong>References:</strong></em></p> <p>[Amato et al. 2022] Amato, G. et al. (2022). VISIONE at Video Browser Showdown 2022. In: , et al. MultiMedia Modeling. <em>MMM 2022. Lecture Notes in Computer Science</em>, vol 13142. Springer, Cham. <a href="https://doi.org/10.1007/978-3-030-98355-0_52">https://doi.org/10.1007/978-3-030-98355-0_52</a></p> <p>[Amato et al. 2021] Amato, G., Bolettieri, P., Carrara, F., Debole, F., Falchi, F., Gennaro, C., Vadicamo, L. and Vairo, C., 2021. The visione video search system: exploiting off-the-shelf text search engines for large-scale video retrieval. <em>Journal of Imaging</em>, <em>7</em>(5), p.76.</p> <p>[Gupta et al.2019] Gupta, A., Dollar, P. and Girshick, R., 2019. Lvis: A dataset for large vocabulary instance segmentation. In <em>Proceedings of the IEEE/CVF conference on computer vision and pattern recognition</em> (pp. 5356-5364).</p> <p>[He et al. 2017] He, K., Gkioxari, G., Dollár, P. and Girshick, R., 2017. Mask r-cnn. In <em>Proceedings of the IEEE international conference on computer vision</em> (pp. 2961-2969).</p> <p>[Kuznetsova et al. 2020] Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A. and Duerig, T., 2020. The open images dataset v4. <em>International Journal of Computer Vision</em>, <em>128</em>(7), pp.1956-1981.</p> <p>[Lin et al. 2014] Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P. and Zitnick, C.L., 2014, September. Microsoft coco: Common objects in context. In <em>European conference on computer vision</em> (pp. 740-755). Springer, Cham.</p> <p>[Zhang et al. 2021] Zhang, H., Wang, Y., Dayoub, F. and Sunderhauf, N., 2021. Varifocalnet: An iou-aware dense object detector. In <em>Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition</em> (pp. 8514-8523).</p> <p> </p>
18S V4 rDNA sequences organized at the OTU level for the SOMLIT-Astan time-series (2009-2016)
<p>The present file includes metadata for each 18S V4<strong> rDNA OTU</strong> from the SOMLIT-Astan time series (2009-2016) including the following fields: <strong>amplicon</strong> = identifier of the representative (most abundant) sequence; <strong>total</strong> = total number of reads; <strong>spread </strong>= number of samples in which the OTU has been found; <strong>cloud </strong>= number of unique sequences constituting the OTU; <strong>sequence</strong> = nucleic acid sequence of the representative sequence; <strong>length</strong> = length of the representative sequence; <strong>quality </strong>= minimum expected error observed for the representative sequence, divided by sequence length; <strong> taxonomy</strong> = taxonomic path assigned to the representative sequence; <strong>identity</strong> = percentage of identity of the representative sequence to the closest reference sequence from PR2; <strong>references</strong> = best hit reference sequence(s) ; <strong>RA090107_02:RA161222_3 </strong>= 375 samples from January 2009 to December 2016, the first two number are the year followed by the month and the day (sampling twice a month during 8 years). Values after “_” indicate the size of the filter used for the filtration: 02 for 0.2 µm and 3 for 3 µm.</p> <p>Generation of 18S V4 rDNA Operational Taxonomic Units (OTUs) from the raw sequencing reads and their assembly into a OTUtable was obtained according to the following pipeline (https://doi.org/10.5281/zenodo.5791089). The V4 region was extracted from the 18S rDNA reference sequences from PR2 v4.12 (Guillou et al., 2013) with Cutadapt. The representative sequences of each OTU were compared to these V4 reference sequences by pairwise global alignment (usearch_global VSEARCH’s command). Each OTU inherits the taxonomy of the best hit or the last common ancestor in case of ties. OTUs with a score below 80% similarity were considered as unassigned (Mahé et al., 2017; Stoeck et al., 2010).</p> <p>The final dataset (filtered OTU table) contains 375 samples (sampled twice per month from 2009 to 2016) with a total of ~30 million sequence reads and 21,418 OTUs.</p>
Polymerase Structure Alignments - v4 Update (July 2022)
<p>v4 update (July 2022) of superposed viral polymerase structures using the methodology described in <br> <em>A Comprehensive Superposition of Viral Polymerase Structures</em> by Olve Peersen, <a href="https://www.mdpi.com/1999-4915/11/8/745">Viruses 2019 11(8) 745</a>.</p> <p> • <strong>_All_PDBs_v4.zip</strong> contains a single directory with coordinate files for 917 structures taken from 628 PDB entries. Uncompressed size is 1.6 GB.</p> <p> • <strong>Polymerases_v4.zip</strong> contains the complete set of files from the superpositions, including the _All_PDBs/ directory. Uncompressed size is 5.7 GB.</p> <p>---------------------------------------------------------------------------------------------------------------<br> POLYMERASE STRUCTURES community page at Zenodo.org </p> <p> v4 Release July 19, 2022</p> <p> 917 structures from 628 PDB entries</p> <p> http://www.zenodo.org/communities/pols/ <br> =========================================================================================</p> <p>This directory contains a large collection viral polymerase structure coordinates that have been superposed using the methods described in Peersen, Viruses 2019, 11(8), 745. </p> <p>They are released into the public domain under the Creative Commons Attribution (CC BY) license. </p> <p>Please cite the following two references for any derivative work:</p> <p> 1) Reference to the publication describing the methodology:<br> Peersen, OB<br> A Comprehensive Superposition of Viral Polymerase Structures.<br> Viruses 2019, 11(8), 745 <br> https://doi.org/10.3390/v11080745</p> <p> 2) DOI reference that always resolves to the latest release of the dataset:<br> Peersen, OB<br> Polymerase Structure Alignments<br> https://doi.org/10.5281/zenodo.3361874</p> <p><br> ---------------------------------------------------------------------------------------------<br> Olve Peersen Olve.Peersen@ColoState.edu<br> Professor<br> Biochemistry & Molecular Biology<br> 1870 Campus Delivery<br> Colorado State University<br> Ft. Collins, CO 80523-1870<br> -------------------------------------------------------------------------</p> <p>=============================================================================</p> <p>v4 220719 - Many updates and several news sets, as outlined below. <br> The total count is now 917 polymerase structures from 628 PDB entries</p> <p> 2-rddp: Foamyvirus FOAM added, but rest of set left unchanged</p> <p> 3-dsdn: Simplified as hsv1 was pulled into its own set (see below)</p> <p><br> NEW alph: Alphaviruses - RRV: 7f0s,<br> SINV: 7vb4, 7vw5</p> <p> aren: Lassa: 7ckl, 7ela, 7och, 7oe3, 7oe7, 7oea, 7oeb,<br> 7ojj, 7ojk, 70jl, 7ojn<br> Machupo: 7ckm, 7el9, 7elb, 7elc, 7vgq, 7vh1, 7vh2, 7vh3</p> <p> buny: RVF: 7eei<br> LaCrosse: 7ori, 7orj, 7ork, 7orl, 7orm, 7orn, 7oro</p> <p> coro: 7dfg, 7dfh, 7doi, 7dok, 7dte, 7ed5, 7egq, 7eiz, 7krn, 7kro, 7krp, <br> 7oyg, 7ozu, 7ozv, 7rdx, 7rdy, 7rdz, 7re0, 7re1, 7re2, 7re3, 7thm </p> <p> fluv: 7nha, 7nhc, 7nhx, 7ni0, 7nik, 7nil, 7nir, 7nis, 7nj3, 7nj4, 7nj5, <br> 7nj7, 7nk1, 7nk2, 7nk4, 7nk6, 7nk8, 7nka, 7nkc, 7nki, 7nkr, 7z42, <br> 7z43, 7z43, 7z4o, 7z4o</p> <p> NEW foam: Foamyviruses - 7o0g, 7o0h, 7024<br> Created FOAM.pdb from core of 7o0g.pdb, which was <br> superposed on rddp set, but the orientations of other <br> members of that set (HIV,MMLV,TERT) were not updated.</p> <p> NEW hsv1: Herpes Simplex Virus - 2gv9, 7luf</p> <p> pest: Classic Swine Fever Virus: 7ekj</p> <p> poxv: 7amv, 7aof, 7aoh, 7aoz, 7ap8, 7ap9</p> <p> rb69: 2atq, 7f4y</p> <p> tavp: 7om2, 7om6, 7om7, 7om9, 7oma<br> </p>
The Encyclopedia of Domains (TED) structural domains assignments for AlphaFold Database v4
<h3>Dataset description:</h3> <p>The Encyclopedia of Domains (TED) is a joint effort by CATH (Orengo group) and the Jones group at University College London to identify and classify protein domains in AlphaFold2 models from AlphaFold Database version 4, covering over 188 million unique sequences and 365 million domain assignments. </p> <p>In this data release, we will be making available to the community a table of domain boundaries and additional metadata on quality (pLDDT, globularity, number of secondary structures), taxonomy, and putative CATH SuperFamily or Fold assignments, for all 365 million domains (~324 million domains in TED100 and ~40 million domains in TED-redundant).</p> <p>For all chains in the chain-level TED-redundant files, the file contains boundary predictions, consensus level and information on the TED100 representative.</p> <p>For both TED100 and TED-redundant we provide domain boundary predictions outputted by each of the three methods employed in the project (Chainsaw, Merizo, UniDoc). </p> <p>We are making available 7,427 PDB files for potentially novel folds identified during the TED classification process, with an annotation table sorted by novelty, as well as 6,433 highly symmetrical folds representatives.</p> <p>Please use the gunzip command to extract files with a '.gz' extension and "tar -xzvf file.tar.gz" to open .tar.gz files .</p> <p>CATH annotations have been assigned using the Foldseek algorithm applied in various modes, and the Foldclass algorithm, both of which are used to report significant structural similarity to a known CATH domain. </p> <p><br><strong>Note: The TED protocol differs from that of the standard CATH Assignment protocol for superfamily assignment, which also involves HMM-based protocols and manual curation for classification into superfamilies.</strong></p> <h3> </h3> <h3><strong>Changelog Version 5:</strong></h3> <ul> <li><strong>Add</strong>: ted_365m.domain_summary.cath.globularity.taxid.tsv.tar.gz - This table, in the same format as the previous ted_100_324m.domain_summary.cath.globularity.taxid.tsv.tar.gz, contains per-domain annotations for the whole of TED, including metadata on domain quality metrics such as secondary structure elements counts, globularity scores, average pLDDT and taxonomical assignments.</li> <li><strong>Add:</strong> high_symmetry_folds_set.domain_summary.tsv.gz - subset of ted_365m.domain_summary.cath.globularity.taxid.tsv containing information on 6,433 high symmetry folds in TED. The entries are sorted in descending order by Z-score obtained from SymD.</li> <li><strong>Add:</strong> high_symmetry_folds_set_models.tar.gz - TED domain models in PDB format for 6,433 high symmetry folds in TED.</li> <li><strong>Add:</strong> ISP_data.tar.gz - Raw data for Interacting SuperFamily Pairs calculations used in the manuscript. A more detailed description of the ISP data is available below as well as within the tar.gz file. </li> <li><strong>Add:</strong> ted_redundant_40m_domain_id.list.gz - list of TED_domain_ID in TED redundant</li> <li><strong>Add:</strong> ted_100_324m_domain_id.list.gz - list of TED_domain_ID in TED100</li> <li><strong>Fix/Replace</strong>: A domain-level summary of TED, now consolidated into <strong>ted_365m.domain_summary.cath.globularity.taxid.tsv</strong>, is consistent with the protocol used in the manuscript. As Foldclass and Foldseek T-level hits provide all 4 CATH digits, we removed the H portion of the CATH code from each prediction at the T-level. <br>Previously, the following columns <br> 14. cath_label - CATH superfamily code if predicted, either a C.A.T.H. homologous superfamily or C.A.T. fold assignment. i.e. 3.40.50.300<br> 15. cath_assignment_level - H for homologous superfamily assignment, T for fold level assignment.<br> 16. cath_assignment_method - Method used to assign a CATH label, either Foldseek or Foldclass<br>sometimes showed an additional label with a T-level prediction by Foldclass in the case of T-level assignments obtained by Foldseek, e.g.<br>3.40.30,3.40.30 T foldseek,foldclass<br>This has now been corrected to reflect the TED protocol, with Foldclass T-level assignments applied only to domains where a T-level assignment could not be applied using Foldseek, e.g. <br>domain-x 3.40.30 T foldseek<br>domain-y 3.20.20 T foldclass<br><br>Thus, in the current version of the data, CATH assignments label can only be <br>H-level assignment by Foldseek (i.e. 3.40.50.300 H foldseek)<br>T-level assignment by Foldseek (i.e. 3.40.30 T foldseek)<br>T-level assignment by Foldclass (i.e. 3.40.30 T foldclass)<br>or no assignment (- - - )</li> </ul> <h3><br>This dataset contains:</h3> <ul> <li><strong>ted_214m_per_chain_segmentation.tsv</strong><br>The file contains all 214M protein chains in TED with consensus domain boundaries and proteome information in the following columns.<br>1. AFDB_model_ID: chain identifier from AFDB in the format AF-<UniProtID>-F1-model_v4 i.e. AF-A0A1V6M2Y0-F1-model_v4<br>2. md5 hash for chain sequence<br>3. nres - number of residues in chain<br>4. n_high - number of high consensus domains predicted in chain<br>5. n_med - number of medium consensus domains predicted in chain<br>6. n_low - number of low consensus domains predicted in chain<br>7. high_consesnsus - boundaries of high consensus domains predicted in chain. If none, 'na'<br>8. med_consensus - boundaries of medium consensus domains predicted in chain. If none, 'na'<br>9. low_consensus - boundaries of low consensus domains predicted in chain. If none, 'na'<br>10. proteome_id - proteome identifier in the format proteome-tax_id-<taxonID>-<shard>_v4 i.e. proteome-tax_id-67581-0_v4<br><br></li> <li><strong>ted_365m_domain_boundaries_consensus_level.tsv.gz</strong><br>The file contains all domain assignments in TED100 and TED-redundant (365M) in the format:<br>1. TED_ID: TED domain identifier in the format AF-<UniProtID>-F1-model_v4_TED<domain_number_in_chain> i.e. AF-A0A1V6M2Y0-F1-model_v4_TED03<br>2. Boundaries: domain boundaries in the format <start>-<stop> or <start>-<stop>_<start>-<stop> for discontinuous domains.<br>3. Consensus: either high or medium.<br><br></li> <li><strong>ted_100_324m_domain_id.list.gz</strong> - list of ~324 million domain identifiers in TED100, one per line in the format AF-<UniProtID>-F1-model_v4_TED<domain_number_in_chain> i.e. AF-A0A1V6M2Y0-F1-model_v4_TED0<br><br></li> <li><strong>ted_redundant_40m_domain_id.list.gz</strong> - list of ~40 million domain identifiers in TED redundant, one per line in the format AF-<UniProtID>-F1-model_v4_TED<domain_number_in_chain> i.e. AF-A0A1V6M2Y0-F1-model_v4_TED0<br><br></li> <li><strong>ted_365m.domain_summary.cath.globularity.taxid.tsv, novel_folds_set.domain_summary.tsv</strong> and <strong>high_symmetry_folds_set.domain_summary.tsv</strong> are header-less with the following columns separated by tabs (.tsv). novel_folds_set.domain_summary.tsv is sorted by novelty<br><strong>Note: The TED protocol differs from that of the standard CATH Assignment protocol for superfamily assignment, which also involves HMM-based protocols and manual curation for classification into superfamilies.</strong><br><br> 1. ted_id - TED domain identifier in the format AF-<UniProtID>-F1-model_v4_TED<domain_number_in_chain> i.e. AF-A0A1V6M2Y0-F1-model_v4_TED03<br> 2. md5_domain - md5 hash of domain sequence<br> 3. consensus_level - medium (2 methods agreement) or high (3 methods agreement)<br> 4. chopping - domain boundaries in the format <start>-<stop> or <start>-<stop>_<start>-<stop> for discontinuous domains<br> 5. nres_domain - number of residues in domain<br> 6. num_segments - number of individual segments in domain. <br> 7. plddt - average pLDDT for domain (range from 0 to 100)<br> 8. num_helix_strand_turn - number of helix strand turns predicted by STRIDE<br> 9. num_helix - number of helices predicted by STRIDE<br> 10. num_strand - number of strands predicted by STRIDE<br> 11. num_helix_strand - number of helices and strands predicted by STRIDE<br> 12. num_turn - number of turns predicted by STRIDE<br> 13. proteome_id - proteome identifier in the format proteome-tax_id-<taxonID>-<shard>_v4 i.e. proteome-tax_id-67581-0_v4<br> 14. cath_label - CATH superfamily code if predicted, either a C.A.T.H. homologous superfamily or C.A.T. fold assignment. i.e. 3.40.50.300. Otherwise '-'<br> 15. cath_assignment_level - H for homologous superfamily assignment, T for fold level assignment. Otherwise '-'<br> 16. cath_assignment_method - Method used to assign a CATH label, either foldseek or foldclass. Otherwise '-'<br> 17. packing_density - metric used to determine globularity. A domain with packing_density >=10.333 and norm_rg below 0.356 is considered globular<br> 18. norm_rg - normalised radius of gyration. A domain with packing_density >=10.333 AND norm_rg below 0.356 is considered globular. <br> 19. tax_common_name - Common name for organism<br> 20. tax_scientific_name - Scientific name for organism<br> 21. tax_lineage - Full taxonomic lineage.<br><br></li> <li><strong>ted_324m_seq_clustering.cathlabels.tsv.gz</strong> <br>The file contains the results of the domain sequences clustering with MMseqs2. <br>Columns:<br>1. Cluster_representative<br>2. Cluster_member<br>3. CATH code assignment if available i.e. 3.40.50.300 for a domain with a homologous match or 3.20.20 for a domain matching at the fold level in the CATH classification<br>4. CATH assignment type - either Foldseek-T, Foldseek-H or Foldclass<br><br></li> <li><strong>Domain assignments for TED redundant using single-chain and multi-chain consensus in</strong> <strong>ted_redundant_39m.multichain.consensus_domain_summary.taxid.tsv.gz and ted_redundant_39m.singlechain.consensus_domain_summary.taxid.tsv.gz</strong> </li> <li> The file <strong>ted_redundant_39m.multichain.consensus_domain_summary.taxid.tsv.gz</strong> contains a header with the following fields. Each column is tab-separated (.tsv).<br> 1. TED_redundant_id - TED chain identifier in the format AF-<UniProtID>-F1-model_v4 i.e. AF-A0A1V6M2Y0-F1-model_v4<br> 2. md5 - md5 hash for chain sequence<br> 3. nres - number of residues in chain<br> 4. n_high - number of high consensus domains predicted in chain<br> 5. n_med - number of medium consensus domains predicted in chain<br> 6. high_consensus - boundaries of high consensus domains predicted in chain<br> 7. med_consensus - boundaries of medium consensus domains predicted in chain<br> 8. ndom_consensus - number of consensus domains predicted in chain<br> 9. n_targets - number of chains considered for consensus calculation<br> 10. proteome_id - proteome identifier in the format proteome-tax_id-<taxonID>-<shard>_v4 i.e. proteome-tax_id-67581-0_v4<br> 11. TED_redundant_species - Scientific name for organism the chain originally comes from.<br> 12. TED100_chain_rep - TED100 representative for chain <br> 13. TED100_chain_rep_species - Species of TED100 representative for chain.</li> <li>The file <strong>ted_redundant_39m.singlechain.consensus_domain_summary.taxid.tsv</strong> contains a header with the following fields. Each column is tab-separated (.tsv).<br> 1. TED_redundant_id - TED chain identifier in the format AF-<UniProtID>-F1-model_v4 i.e. AF-A0A1V6M2Y0-F1-model_v4<br> 2. md5 - md5 hash for chain sequence<br> 3. nres - number of residues in chain<br> 4. n_high - number of high consensus domains predicted in chain<br> 5. n_med - number of medium consensus domains predicted in chain<br> 6. high_consensus - boundaries of high consensus domains predicted in chain<br> 7. med_consensus - boundaries of medium consensus domains predicted in chain<br> 8. proteome_id - proteome identifier in the format proteome-tax_id-<taxonID>-<shard>_v4 i.e. proteome-tax_id-67581-0_v4 <br> 9. TED_redundant_species - Scientific name for organism the chain originally comes from<br> 10. TED100_chain_rep - TED100 representative for chain <br> 11. TED100_chain_rep_species - Species of TED100 representative for chain.</li> </ul> <p> </p> <ul> <li><strong>novel_folds_set_models.tar.gz</strong> contains PDB files of all novel folds representatives identified in TED100.<br><br></li> <li><strong>high_symmetry_folds_set_models.tar.gz</strong> contains PDB files of all highly symmetrical folds representatives identified in TED100.<br> </li> <li><strong>Per-tool domain boundaries_predictions</strong> - All per-tool domain boundaries predictions for TED100 and TED-redundant are in the same format with the following columns.<br> 1. TED_chainID - TED chain identifier in the format AF-<UniProtID>-F1-model_v4 i.e. AF-A0A1V6M2Y0-F1-model_v4<br> 2. TED_chain_md5 - md5 hash for chain sequence<br> 3. TED_chain_length - number of residues in chain<br> 4. ndoms - number of domains predicted in chains<br> 5. Domain boundaries - domain boundaries in the format <start>-<stop> or <start>-<stop>_<start>-<stop> for discontinuous domains<br> 6. Prediction probability - probability of each per-chain prediction<br>Domain boundaries predictions share the same format, with each segment separated by '_' and segment boundaries (start,stop) separated by '-'<br> <br> i.e.domain prediction by Merizo for AF-A0A000-F1-model_v4<br> AF-A0A000-F1-model_v4 e8872c7a0261b9e88e6ff47eb34e4162 394 2 10-52_289-394,53-288 0.90077<br> <br> Merizo predicts one continuous domain and a discontinuous domain,<br> Domain1 (discontinuous): 10-52_289-394<br> segment1: 10-52<br> segment2: 289-394<br> Domain 2 (continuous):<br> segment 1: 53-288<br><br></li> <li><strong>ISP_data.tar.gz</strong> contains raw data for the Interacting Superfamily Pairs (ISP) calculations featured in the manuscript. The archive contains a README as well as :<br>all_ISP_data_cath.pkl: <br>ISP data for CATH 4.3 in Python pickle format. <br>A Python dictionary with the following contents: <br>Each key is an ISP, e.g. '3.40.640.10-3.90.1150.10' <br>Each value is a dictionary with the following contents:<br>key 'aligned_domain_pairs': value is a Python list of length N, each element is a string specifying the two TED domain IDs in contact, separated by a colon, e.g. "AF-A0A000-F1-model_v4_TED02:AF-A0A000-F1-model_v4_TED01" <br>key 'vectors': value is a numpy.ndarray of shape (N, 3). Each row is a raw unnormalized interaction vector for the corresponding domain pair, after aligning to the reference structure. <br>Any given index in each list or ndarray has the data for a single domain pair; the order is constant in each list/array. <br>---------------------------------- <br>all_ISP_data_afdb.pkl: <br>ISP data for TED100 in Python pickle format. <br>A Python dictionary with the following contents: <br>Each key is an ISP, e.g. '3.40.640.10-3.90.1150.10' <br>Each value is a dictionary with the following contents:<br>key 'aligned_domain_pairs': value is a Python list of length N, each element is a string specifying the two TED domain IDs in contact, separated by a colon, e.g. "AF-A0A000-F1-model_v4_TED02:AF-A0A000-F1-model_v4_TED01"<br>key 'vectors': value is a numpy.ndarray of shape (N, 3). Each row is a raw unnormalized interaction vector for the corresponding domain pair, after aligning to the reference structure. <br>key 'choppings': value is a Python list of length N, each element is a colon-separated string containing the TED chopping strings for the domains in contact, e.g. "54-288:11-41_290-389". The format for each 'chopping' follows that used in the main TED TSV files. <br>key 'pae_score': value is a numpy.ndarray of floats of shape (N,). Each element is the median PAE score between the domains in contact, computed across both relevant parts of the PAE matrix as described in the paper. <br>Any given index in each list or ndarray has the data for a single domain pair; the order is constant in each list/array. <br>NB: each list of domains has not been filtered by PAE score, so the pae_score values include values greater than 4.0, which was the threshold used to filter confident predictions in the manuscript.<br>------------------------------------- <br>isp_data_afdbonly_nopaefilter.csv:<br>A subset of the data in the TED100 .pkl file above, in CSV format. <br>Each row contains the following fields: AFDB ID, e.g. AF-A0A000-F1-model_v4 ISP, e.g. 3.40.640.10-3.90.1150.10<br>Colon-separated domain ID pair, e.g. AF-A0A000-F1-model_v4_TED02:AF-A0A000-F1-model_v4_TED01 PAE score, e.g. '4.0' <br>As the aforementioned files, this data has not been filtered by PAE score values.<br><br></li> <li><strong>ted-tools-main.zip</strong> - copy of the https://github.com/psipred/ted-tools repository, containing tools and software used to generate TED.<br><br></li> <li><strong>cath-alphaflow-main.zip</strong> - copy of CATH-AlphaFlow, used to generate globularity scores for TED domains.<br><br></li> <li><strong>ted-web-master.zip</strong> - copy of TED-web, containing code to generate the web interface of TED (https://ted.cathdb.info)<br><br></li> <li><strong>gofocus_data.tar.bz2</strong> - GOFocus model weights</li> </ul>
ADS v4 results for MY 34 & 35
<p>Results from the Aerosol Determination Strategy v4:</p> <p>*Extinction values comes from RCP retrievals</p> <p>*Covers MY 34 and first half of MY 35</p> <p>Columns description:</p> <p>Reff_dust : Effective radius dust (unit in μm)<br> Reff_dust_err : Effective radius error dust (unit in μm)<br> Reff_H2Oice : Effective radius water ice (unit in μm)<br> Reff_H2Oice_err : Effective radius error water ice (unit in μm)<br> Gamma : Ratio of the number densities of water ice over dust (no unit)<br> Gamma_err : Ratio of the number densities of water ice over dust (no unit)<br> Veff_dust : Effective variance of dust (no unit)<br> Veff_dust_err : Effective variance error of dust (no unit)<br> Veff_H2Oice : Effective variance of water ice (no unit)<br> Veff_H2Oice_err : Effective variance error of water ice (no unit)<br> M_Dust : Mass loading of dust (unit in g/cm^-3)<br> M_Dust_err : Mass loading error of dust (unit in g/cm^-3)<br> M_Dust_0Veff_err : Mass loading error considering ν_eff exact of dust (unit in g/cm^-3)<br> M_Dust_0VeffGamma_err : Mass loading error considering ν_eff and γ exact of dust (unit in g/cm^-3)<br> M_H2Oice : Mass loading of water ice (unit in g/cm^-3)<br> M_H2Oice_err : Mass loading error of water ice (unit in g/cm^-3)<br> M_H2Oice_0Veff_err : Mass loading error considering ν_eff exact of water ice (unit in g/cm^-3)<br> M_H2Oice_0VeffGamma_err: Mass loading error considering ν_eff and γ exact of water ice (unit in g/cm^-3)<br> N_Dust : Number density of dust (unit in #/cm^-3)<br> N_Dust_err : Number density error of dust (unit in #/cm^-3)<br> N_Dust_0Veff_err : Number density error considering ν_eff exact of dust (unit in #/cm^-3)<br> N_Dust_0VeffGamma_err : Number density error considering ν_eff and γ exact of dust (unit in #/cm^-3)<br> N_H2Oice : Number density of water ice (unit in #/cm^-3)<br> N_H2Oice_err : Number density error of water ice (unit in #/cm^-3)<br> N_H2Oice_0Veff_err : Number density error considering ν_eff exact of water ice (unit in #/cm^-3)<br> N_H2Oice_0VeffGamma_err: Number density error considering ν_eff and γ exact of water ice (unit in #/cm^-3)<br> MeritF : Value of the Merit Function<br> aerosol_type : Type of the aerosol, H2Oice (poure water ice), Dust (pure dust), Mixture_H2Oice (mixture with water ice as MSC), Mixture_Dust (mixture with dust as MSC), No_Aerosols<br> Vertical_res : Vertical resolution (unit in km)<br> extinction_121 : Extinction for order 121 (unit in km^-1)<br> extinction_134 : Extinction for order 134 (unit in km^-1)<br> extinction_149 : Extinction for order 149 (unit in km^-1)<br> extinction_168 : Extinction for order 168 (unit in km^-1)<br> extinction_190 : Extinction for order 190 (unit in km^-1)<br> altitude : Altitude above Mars' surface (unit in km)</p>
16S V4-V5 metabarcoding reference databases and weighted naive-bayes classifiers, dereplicated
<p>16S metabarcoding databases and naive-bayes classifiers specific to the V4-V5 region. Built from the <a href="https://www.arb-silva.de/documentation/release-138/">Silva 138.1 SSU Ref NR 99</a> database using Qiime2 (version 2023.2) and the <a href="https://github.com/BenKaehler/q2-clawback">q2-clawback plugin.</a> Includes weighted classifiers for two Earth Microbiome Project Ontology (EMPO) 3 habitat types: "sediment (saline)" and "water (saline)" , with data downloaded from <a href="https://qiita.ucsd.edu/">Qiita</a>. Sequences were dereplicated with Rescript --p-mode 'uniq' , retaining identical sequence records that have differing taxonomies.</p> <p>Primers used:</p> <p>EMP 16S 515f: GTGYCAGCMGCCGCGGTAA</p> <p>EMP 16S 926r: CCGYCAATTYMTTTRAGTTT</p> <p><strong>Stats</strong></p> <p>286,948 unique sequences</p> <p>309,567 total sequences</p> <p>46,254 unique taxa (Level 7)</p> <table> <caption>File description</caption> <thead> <tr> <th scope="col"> <table> <thead> <tr> <th>File</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>make new 16S silva V4-V5 database.md</td> <td>Markdown with code used to generate databases</td> </tr> <tr> <td>silva-138-99-seqs.qza</td> <td>Full length Silva 138.1 SSU 99 sequences</td> </tr> <tr> <td>silva-138-99-tax.qza</td> <td>Taxa for full length Silva 138.1 SSU 99 database</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-seqs.qza</td> <td>Sequences for 16S V4-V5 (primers 515f, 926r), extracted from Silva 138.1 SSU 99, generated by qiime2-2023.2 (forward compatible), dereplicated</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-taxa.qza</td> <td>Taxa for silva-138_1-99-515f_926r-seqs.qza database, dereplicated</td> </tr> <tr> <td>uniform-silva-138_1-99-515f_926r-uniq-classifier.qza</td> <td>Unweighted (uniform) naive-bayes classifier for 16S V4-V5 (primers 515f, 926r) extracted from Silva 138.1 SSU 99, generated by qiime2-2023.2 (forward compatible)</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-sediment-saline-classifier.qza</td> <td>Weighted naive-bayes classifier for 16S V4-V5 (primers 515f, 926r) extracted from Silva 138.1 SSU 99, weighted for sediment-saline, generated by qiime2-2023.2 (forward compatible)</td> </tr> <tr> <td>silva-138_1-99-515f_926r-q2_2023_2-uniq-sediment-saline-weights.qza</td> <td>Weights used to generate silva-138_1-99-515f_926r-q2_2023_2-sediment-saline-classifier.qza</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-water-saline-classifier.qza</td> <td>Weighted naive-bayes classifier for 16S V4-V5 (primers 515f, 926r) extracted from Silva 138.1 SSU 99, weighted for water-saline, generated by qiime2-2023.2 (forward compatible)</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-water-saline-weights.qza</td> <td>Weights used to generate silva-138_1-99-515f_926r-water-saline-classifier.qza</td> </tr> </tbody> </table> </th> <th scope="col"> </th> </tr> </thead> <tbody> </tbody> </table> <p> </p>
The processed clean data of 16S rRNA V4 amplicon sequecnces for the six stage of phenolic microbiome domestication
Open the record for dataset details and reuse information.
Adaptive processing and perceptual learning in visual cortical areas V1 and V4
<p>Neurons in visual cortical areas primary visual cortex (V1) and V4 are adaptive processors, influenced by perceptual task. This is reflected in their ability to segment the visual scene into task-relevant and task-irrelevant stimulus components and by changing their tuning to task-relevant stimulus properties according to the current top-down instruction. Differences between the information represented in each area were seen. While V1 represented detailed stimulus characteristics, V4 filtered the input from V1 to carry the binary information required for the two-alternative judgement task. Neurons in V1 were activated at locations where the behaviorally relevant stimulus was placed well outside the grating-mapped receptive field. By systematically following the development of the task-dependent signals over the course of perceptual learning, we found that neuronal selectivity for task-relevant information was initially seen in V4 and, over a period of weeks, subsequently in V1. Once the learned information was represented in V1, on any given trial, task-relevant information appeared initially in V1 responses, followed by a 12-ms delay in V4. We propose that the shifting representation of learned information constitutes a mechanism for systems consolidation of memory.</p>
Tara Oceans (2009-2013) rDNA 18S V4 ASV table (DADA2) with nf-core/ampliseq
<p>This repository contains datasets describing the DADA2 ASVs generated from <em>Tara</em> Oceans 18S V4 rDNA data. The ASVs were generated using the nf-core workflow <a href="https://nf-co.re/ampliseq" target="_blank" rel="noopener">ampliseq</a>. Please refer to the readme file (README.html) for more details.</p>
Data for: ToadFishFinder classifier model v4: A catalog of oyster toadfish (Opsanus tau) calls for machine learning
<p>This data repository contains labeled passive underwater acoustic data used to train and test the machine-learning model of Bohnenstiehl (in prep – 2023), <span>Automated cataloging of oyster toadfish (<em>Opsanus</em> <em>tau</em>) calls using template matching and machine learning</span>. The software accompanying this paper is known as ToadFishFinder, and the classifier model presented in the paper is v4. It consists of more than 10000 labeled toadfish and 10000 labeled other signals. Labeled spectrogram images are provided, along with pressure-corrected waveforms (micro-Pascals) sampled at 24 kHz. Each waveform sample is 1350 ms long. The center 850 ms of these waveform segments represent the portion of the signal used in training and testing the classifier model. Waveform data are provided in multiple formats: 1) MATLAB (.mat) files containing the 'boatwhistle' and 'other' waveforms stored in column format, and 2) individual .wav files, each containing a labeled waveform example. Codes are provided to demonstrate how these .wav files can be read into MATLAB and PYTHON. These labeled data can be used to re-train the ToadFishFinder model or develop alternative classifiers. </p>
Adaptive processing and perceptual learning in visual cortical areas V1 and V4
Open the record for dataset details and reuse information.
Data for: ToadFishFinder classifier model v4: A catalog of oyster toadfish (Opsanus tau) calls for machine learning
Open the record for dataset details and reuse information.
EukBank 18S V4 dataset
<p>This repository contains the files constituting the first release of the EukBank 18S V4 dataset gathering 12,672 metabarcoding samples globally distributed across biomes for which 335,662 ASVs have been identified. The dataset is composed of seven files; their structure and associated methods are detailed in the readme file README.pdf.</p>
The IASI/Metop-B HCOOH ANNI v4 product
<p>This dataset comprises the version 4 of the HCOOH product retrieved from IASI/Metop-B between July 2022 and June 2023, using the Artificial Neural Network for IASI (ANNI) version 4 retrieval scheme. Each day is represented by a .nc file. For detailed information on the HCOOH product, please refer to Franco et al. (2018) <em>"A General Framework for Global Retrievals of Trace Gases From IASI: Application to Methanol, Formic Acid, and PAN"</em> (JGR, https://doi.org/10.1029/2018JD029633) and for a description of the ANNI v4 retrievals, please consult Clarisse et al. (2023) <em>"The IASI NH3 version 4 product: averaging kernels and improved consistency"</em> (AMT, <a href="https://doi.org/10.5194/amt-16-5009-2023">https://doi.org/10.5194/amt-16-5009-2023</a>). Prior to using this dataset, we recommend reaching out to the main developers Bruno Franco (<a href="mailto:bruno.franco@ulb.be">bruno.franco@ulb.be</a>) and Lieven Clarisse (<a href="mailto:lieven.clarisse@ulb.be">lieven.clarisse@ulb.be</a>) for guidance and information on more recent product versions.</p>
16S rRNA V4 gut microbiome of Leptonycteris yerbabuenae in Mexico
<p>Migratory animals live in a world of constant change. Animals undergo many physiological changes preparing themselves for the migration. Although this field has been extensively studied over the last decades, we know relatively little about the seasonal changes that occur in the microbial communities that these animals carry in their guts. Here we assessed the V4 region of the 16S rRNA high-throughput sequencing data as a proxy to estimate microbiome diversity of Tequila Bats from fecal pellets and evaluate how the natural process of migration shapes the microbiome composition, and diversity. We collected samples from individual bats at two localities in the Dry Forest biome (Chamela and Coquimatlán) and one site at the end point of the migration in the Sonoran Desert (Pinacate). We found that the gut microbiome of the Tequila bats is largely dominated by Firmicutes and Proteobacteria. Our data also provide insights on how microbiome diversity shifts at the same site in consecutive years. <em>Our study has demonstrated that both locality and year-to-year variation contribute to shaping the composition, overall diversity, and the 'uniqueness' of the gut microbiome of migratory nectar-feeding female bats with localities from the dry forest biome looking more like each other when compared to the desert biome.</em> In terms of beta diversity, our data show a stratified effect in which the samples locality was the strongest factor influencing the gut microbiome, but with significant variation between consecutive years at the same locality.</p>
ECCO v4 standard analysis sample (v4r3)
<p>Generated from the ECCO output (version 4, release 3) available to download from <a href="https://ecco-group.org/products.htm">ecco-group.org</a> using a Julia workflow available from <a href="https://github.com/gaelforget/OceanStateEstimation.jl">OceanStateEstimation.jl</a> (see <a href="https://gaelforget.github.io/OceanStateEstimation.jl/dev/">the docs</a>).</p>
ECCO v4 standard analysis sample
<p>Generated from the ECCO output (version 4, release 2) available to download from <a href="https://ecco-group.org/products.htm">ecco-group.org</a> using a Julia workflow available from <a href="https://github.com/gaelforget/OceanStateEstimation.jl">OceanStateEstimation.jl</a> (see <a href="https://gaelforget.github.io/OceanStateEstimation.jl/dev/">the docs</a>).</p> <p>The ECCO output (version 4, release 2) used here is archived @ <a href="https://doi.org/10.7910/DVN/NXYKDW">https://doi.org/10.7910/DVN/NXYKDW</a></p>
ECCO v4 standard analysis sample (v4r4)
<p>Generated from the ECCO output (version 4, release 4) available to download from <a href="https://ecco-group.org/products.htm">ecco-group.org</a> using a Julia workflow available from <a href="https://github.com/gaelforget/OceanStateEstimation.jl">OceanStateEstimation.jl</a> (see <a href="https://gaelforget.github.io/OceanStateEstimation.jl/dev/">the docs</a>).</p> <p> </p>
Genome and annotation files (v4_23) for Blumeria graminis f. sp. tritici isolate CHE_96224 (genome assembly: Bgt_genome_v3_16)
<p>Genome and annotation (v4_23) files for Blumeria graminis f. sp. tritici isolate CHE_96224 (genome assembly: Bgt_genome_v3_16)</p>
rDNA 18S V4 metabarcoding tables (Swarm) for Tara Oceans Expedition (2009-2013), including Tara Polar Circle Expedition (2013)
<p>Reads were grouped into OTUs using the following swarm-based pipeline: paired-end reads were merged with vsearch’s --fastq_mergepairs command (version 2.15.1, allowing for staggered reads; Rognes et al., 2016), and trimmed with cutadapt (version 3.0; Martin, 2011), keeping only reads containing both forward and reverse primers. After trimming, the expected error per read was estimated with vsearch’s command --fastq_filter and the option --eeout. Each sample was then de-replicated, i.e. strictly identical reads were merged, using vsearch’s command --derep_fulllength, and converted into fasta format. Clustering was performed at the sample level with swarm 3.0 using default parameters (Mahé et al., 2015). Prior to global clustering, individual fasta files (one per sample) were pooled and further dereplicated with vsearch. Files containing per-read expected error values were also dereplicated to retain only the lowest expected error for each unique sequence. Global clustering was performed with swarm (using the fastidious option). Cluster representative sequences were then searched for chimeras with vsearch’s command --uchime_denovo using default parameters (Edgar et al., 2011).<br> Clustering results, expected error values, taxonomic assignments, and chimera detection results were used to build a “raw” occurrence table. Reads without primers, reads shorter than 32 nucleotides and reads with uncalled bases (“N”) were discarded. For a “filtered” occurrence table, non-chimeric sequences, sequences with an expected error per nucleotide below 0.0002, and clusters containing at least 2 reads were retained. Since primer trimming is not perfect, some sequences can still contain primer fragments or be excessively trimmed. These sub- or super-sequences were identified using vsearch and merged with their closest, most abundant perfectly trimmed sequence. Finally, occurrence patterns throughout our sample collection were used to further refine the occurrence table. Clusters that contain sub-clusters with only a single-nucleotide difference but with different ecological patterns (defined here as uncorrelated abundance values in at least 5% of the samples) were turned into distinct clusters (https://github.com/frederic-mahe/fred-metabarcoding-pipeline). On the other hand, clusters with similar sequences that had correlated abundance values in at least 95% of the samples, were merged using a re-implementation of lulu's method (Frøslev et al. 2017; https://github.com/frederic-mahe/mumu).</p>
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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.