Find research datasets worth reusing

opengpl-2.0-or-laterAug 2024View details →

Recordings of zebra finch group behaviors with manually annotated vocal segments

<div> <div>This dataset contains recording files and manually annotated vocal segments of freely behaving zebra finches in the BirdPark. The files contain animal-borne accelerometer, microphone and video channels. Birds are housed in groups of different sizes (2, 4, or 8 birds, (one 7-mins file per group). The dataset contains 112 mins of recordings from 4 different experiments.</div> </div> The dataset is published as an appendix of the following paper: <a href="https://doi.org/10.1101/2022.09.23.509166">Rüttimann, L., Wang, Y., Rychen, J., Tomka, T., Hörster, H., Rocha, M. D., & Hahnloser, R. H. (2024). Multimodal system for recording individual-level behaviors in songbird groups. bioRxiv.</a> See the README.pdf file for a detailed description.

opencc-by-4.0Jan 2024View details →

The North Pacific Eukaryotic Gene Catalog: metatranscriptome assemblies with taxonomy, function and abundance annotations

This data continues with the development of the unprocessed NPEGC Trinity de novo metatranscriptome assemblies, uploaded to this Zenodo repository for raw assemblies: <a href="../records/7332796">The North Pacific Eukaryotic Gene Catalog: Raw assemblies from Gradients 1, 2 and 3</a> A full description of this data is published in Scientific Data, available here: <a href="https://www.nature.com/articles/s41597-024-04005-5" target="_blank" rel="noopener">The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations</a>. Please cite this publication if your research uses this data: Groussman, R. D., Coesel, S. N., Durham, B. P., Schatz, M. J., & Armbrust, E. V. (2024). The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations. Scientific Data, 11(1), 1161. Excerpts of key processing steps are sampled below with links to the detailed code on the main github code repository: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog">https://github.com/armbrustlab/NPac_euk_gene_catalog</a> Processing and annotation of protein-level NPEGC metatranscripts is done in 6 primary steps: 1. Six-frame translation into protein sequences 2. Frame-selection of protein-coding translation frames 3. Clustering of protein sequences at 99% sequence identity 4. Taxonomic annotation against MarFERReT v1.1 + MARMICRODB v1.0 multi-kingdom marine reference protein sequence library with DIAMOND 5. Functional annotation against Pfam 35.0 protein family HMM profiles using HMMER3 6. Functional annotation against KOfam HMM profiles (KEGG release 104.0) using KofamScan v1.3.0 <code># Define local NPEGC base directory here:</code> <code>NPEGC_DIR="/mnt/nfs/projects/armbrust-metat"</code> <code># Raw assemblies are located in the /assemblies/raw/ directory</code> <code># for each of the metatranscriptome projects</code> <code>PROJECT_LIST="D1PA G1PA G2PA G3PA G3PA_diel"</code> <code># raw Trinity assemblies:</code> <code>RAW_ASSEMBLY_DIR="${NPEGC_DIR}/${PROJECT}/assemblies/raw"</code> Translation We began processing the raw metatranscriptome assemblies by six-frame translation from nucleotide transcripts into three forward and three reverse reading frame translations, using the transeq function in the EMBOSS package. We add a cruise and sample prefix to the sequence IDs to ensure unique identification downstream (ex, `>TRINITY_DN2064353_c0_g1_i1_1`  to `>G1PA_S09C1_3um_TRINITY_DN2064353_c0_g1_i1_1` for the S09C1_3um sample in the G1PA assemblies). See <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.6tr_frame_selection_clustering.sh">NPEGC.6tr_frame_selection_clustering.sh</a> for full code description. Example of six-frame translation using transeq <code>transeq -auto -sformat pearson -frame 6 -sequence 6tr/${PREFIX}.Trinity.fasta -outseq 6tr/${PREFIX}.Trinity.6tr.fasta</code> Frame selection We use a custom frame-selection python script <a href="https://github.com/armbrustlab/marferret/blob/main/scripts/python/keep_longest_frame.py">keep_longest_frame.py</a> to determine the longest coding length in each open reading frame and retain this sequence (or multiple sequences if there is a tie) for downstream analyses.  See <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.6tr_frame_selection_clustering.sh">NPEGC.6tr_frame_selection_clustering.sh</a> for full code description. Clustering by sequence identity To reduce sequence redundancy and near-identical sequences, we cluster protein sequences at the 99% sequence identity level and retain the sequence cluster representative in a reduced-size FASTA output file. See <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.6tr_frame_selection_clustering.sh">NPEGC.6tr_frame_selection_clustering.sh</a> for full code description of linclust/mmseqs clustering. Sample of linclust clustering script: core mmseqs function <code>function NPEGC_linclust {</code> <code># make an index of the fasta file:</code> <code>$MMSEQS_DIR/mmseqs createdb $FASTA_PATH/$FASTA_FILE NPac.$STUDY.bf100.db</code> <code># cluster sequences at $MIN_SEQ_ID</code> <code>$MMSEQS_DIR/mmseqs linclust NPac.${STUDY}.bf100.db NPac.${STUDY}.clusters.db NPac_tmp --min-seq-id ${MIN_SEQ_ID}</code> <code># retieve cluster representatives:</code> <code>$MMSEQS_DIR/mmseqs result2repseq NPac.${STUDY}.bf100.db NPac.${STUDY}.clusters.db NPac.${STUDY}.clusters.rep</code> <code># generate flat FASTA output with cluster reps</code> <code>$MMSEQS_DIR/mmseqs result2flat NPac.${STUDY}.bf100.db NPac.${STUDY}.bf100.db NPac.${STUDY}.clusters.rep NPac.${STUDY}.bf100.id99.fasta --use-fasta-header</code> <code>}</code> Corresponding files uploaded to this repository: Gzip-compressed FASTA files after translation, frame-selection, and clustering at 99% sequence identity (.bf100.id99.aa.fasta.gz)     NPac.G1PA.bf100.id99.aa.fasta.gz     NPac.G2PA.bf100.id99.aa.fasta.gz     NPac.G3PA.bf100.id99.aa.fasta.gz     NPac.G3PA_diel.bf100.id99.aa.fasta.gz     NPac.D1PA.bf100.id99.aa.fasta.gz MarFERReT + MARMICRODB taxonomic annotation with DIAMOND Taxonomy was inferred for the NPEGC metatranscripts with the DIAMOND fast read alignment software against the <a href="../records/10586950">MarFERReT v1.1 + MARMICRODB v1.0 multi-kingdom marine reference protein sequence library (v1.1)</a>, a combined database of the <a href="https://doi.org/10.1038/s41597-023-02842-4">MarFERReT v1.1 marine microbial eukaryote sequence library</a> and <a href="https://doi.org/10.5281/zenodo.3520509">MARMICRODB v1.0 </a>prokaryote-focused marine genome database. See <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.diamond_taxonomy.log.sh">NPEGC.diamond_taxonomy.log.sh</a> for full description of DIAMOND annotation. Excerpt of core DIAMOND function: <code>function NPEGC_diamond {</code> <code># FASTA filename for $STUDY</code> <code>FASTER_FASTA="NPac.${STUDY}.bf100.id99.aa.fasta"</code> <code># Output filename for LCA results in lca.tab file:</code> <code>LCA_TAB="NPac.${STUDY}.MarFERReT_v1.1_MMDB.lca.tab"</code> <code>echo "Beginning ${STUDY}"</code> <code>singularity exec --no-home --bind ${DATA_DIR} \</code> <code>        "${CONTAINER_DIR}/diamond.sif" diamond blastp \</code> <code>        -c 4 --threads $N_THREADS \</code> <code>        --db $MFT_MMDB_DMND_DB -e $EVALUE --top 10 -f 102 \</code> <code>        --memory-limit 110 \</code> <code>        --query ${FASTER_FASTA} -o ${LCA_TAB} >> "${STUDY}.MarFERReT_v1.1_MMDB.log" 2>&1</code> <code>}</code> Corresponding files uploaded to this repository: Gzip-compressed diamond lowest common ancestor predictions with NCBI Taxonomy against a combined MarFERReT + MARMICRODB taxonomic library (*.Pfam35.domtblout.tab.gz)     NPac.G1PA.MarFERReT_v1.1_MMDB.lca.tab.gz     NPac.G2PA.MarFERReT_v1.1_MMDB.lca.tab.gz     NPac.G3PA.MarFERReT_v1.1_MMDB.lca.tab.gz     NPac.G3PA_diel.MarFERReT_v1.1_MMDB.lca.tab.gz     NPac.D1PA.MarFERReT_v1.1_MMDB.lca.tab.gz Pfam 35.0 functional annotation using HMMER3 Clustered protein sequences were annotated against the Pfam 35.0 collection of 19,179 protein family Hidden Markov Models (HMMs) using <a href="http://hmmer.org/">HMMER 3.3 </a> with the <a href="https://academic.oup.com/nar/article/49/D1/D412/5943818">Pfam 35.0 protein family database</a>. Pfam annotation code is documented here: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.hmmer_function.sh">NPEGC.hmmer_function.sh</a> Excerpt of core hmmsearch function: <code>function NPEGC_hmmer {</code> <code># Define input FASTA</code> <code>INPUT_FASTA="NPac.${STUDY}.bf100.id99.aa.fasta"</code> <code># hmmsearch call:</code> <code>hmmsearch --cut_tc --cpu $NCORES --domtblout $ANNOTATION_DIR/${STUDY}.Pfam35.domtblout.tab $HMM_PROFILE ${INPUT_FASTA}</code> <code># compress output file:</code> <code>gzip $ANNOTATION_DIR/${STUDY}.Pfam35.domtblout.tab</code> <code>}</code> Corresponding files uploaded to this repository: Gzip-compressed hmmsearch domain table files for Pfam35 queries (*.Pfam35.domtblout.tab.gz)     G1PA.Pfam35.domtblout.tab.gz     G2PA.Pfam35.domtblout.tab.gz     G3PA.Pfam35.domtblout.tab.gz     G3PA_diel.Pfam35.domtblout.tab.gz     D1PA.Pfam35.domtblout.tab.gz KEGG functional annotation using KofamScan v1.3.0 Clustered protein sequences were annotated against the KEGG collection (release 104.0) of 20,819 protein family Hidden Markov Models (HMMs) using <a href="https://github.com/takaram/kofam_scan" target="_blank" rel="noopener">KofamScan </a>and KofamKOALA. Kofam annotation code is documented here: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.kofamscan_function.sh">NPEGC.kofamscan_function.sh</a> Excerpt of core NPEGC_kofam function: <code># Core function to perform KofamScan annotation</code> <code>function NPEGC_kofam {</code> <code>    # Define input FASTA</code> <code>    local INPUT_FASTA="NPac.${STUDY}.bf100.id99.aa.fasta"</code> <code>    # KofamScan call</code> <code>    ${KOFAM_DIR}/kofam_scan-1.3.0/exec_annotation -f detail-tsv -E ${EVALUE} -o ${ANNOTATION_DIR}/NPac.${STUDY}.bf100.id99.aa.tsv ${FASTA_DIR}/${INPUT_FASTA}</code> <code>    # Keep best hit (data is already sorted by KofamScan)</code> <code>    sort -uk1,1 ${ANNOTATION_DIR}/NPac.${STUDY}.bf100.id99.aa.tsv > ${ANNOTATION_DIR}/NPac.${STUDY}.bf100.id99.aa.best.kofam.tsv</code> <code>    # Compress output file</code> <code>    gzip ${ANNOTATION_DIR}/NPac.${STUDY}.bf100.id99.aa.tsv</code> <code>    # Compress best.kofam output file</code> <code>    gzip ${ANNOTATION_DIR}/NPac.${STUDY}.bf100.id99.aa.best.kofam.tsv</code> <code>}</code> <code># filter hits with a score > 30 in R</code> Corresponding files uploaded to this repository: Gzip-compressed KofamScan domain table files for Kofam queries (*.best.Kofam.incT30.csv.gz):     NPac.G1PA.bf100.id99.aa.best.Kofam.incT30.csv.gz     NPac.G2PA.bf100.id99.aa.best.Kofam.incT30.csv.gz     NPac.G3PA.UW.bf100.id99.aa.best.Kofam.incT30.csv.gz     NPac.G3PA.diel.bf100.id99.aa.best.kofam.incT30.csv.gz     NPac.D1PA.diel.bf100.id99.aa.best.kofam.incT30.csv.gz The full kofamscan tables with score >30 are deposited here: <a title="The North Pacific Eukaryotic Gene Catalog: KOfam protein function annotations" href="../records/13743267" target="_blank" rel="noopener">https://zenodo.org/records/13743267</a>

Genome assembly and annotation of an apple variety 'RubyMac'

In this dataset, we provided the contig-level genome assembly and annotation of an apple tree called 'RubyMac', which is growing in Michigan, USA (43°04'53.1"N 85°43'13.5"W). In this tree, the upper branches carried a sport mutation as compared with lower branches.

opencc-by-4.0May 2024View details →

MapReader_railspace_and_building_annotations_2024

This record contains post-processed railspace and building annotations relating to MapReader in GeoHumanities workshop (SIGSPATIAL 2022). The original data can be found at <a href="../records/7147906">https://zenodo.org/records/7147906</a>. See <code>README.md</code> for detailed description of this record.

ChessRender360: High-Fidelity Rendered Chess Dataset with Multi-Modal Annotations

ChessRender360 is a synthetically crafted dataset featuring 10,000 rendered chess positions. Designed for computer vision and machine learning research, this dataset provides a rich collection of RGB images, depth maps, instance masks, and semantic segmentation masks for each chess piece and board element. Each chess position is rendered in high resolution (2000x2000 pixels), capturing the intricate details of the board and pieces from various angles. The dataset includes: <ul> <li>RGB Images: High-quality rendered images of chess positions, showcasing a diverse range of board configurations.</li> <li>Depth Maps: Accurate depth representations of the scene, capturing depth in the range of 20 cm to 120 cm. In the depth maps, black corresponds to a depth of 20 cm, and white corresponds to 120 cm, providing spatial information for each position.</li> <li>Instance Masks: Unique instance masks for each chess piece, enabling precise identification and localization.</li> <li>Semantic Segmentation Masks: Segmentation masks that differentiate between piece types and board elements, with distinct hue values assigned to each type.</li> <li>Bounding Boxes: Each sample has an annotation <code>.json</code> file containing bounding boxes for each piece.</li> <li>Board Corners: Same annotation <code>.json</code> contains positions of corners of the board in order: white left, white right, black left, black right.</li> <li>FENs: A CSV file containing the FEN (Forsyth-Edwards Notation) for each chess position in the dataset, listed in order. This allows users to easily recognize and replicate the exact board position from any image.</li> </ul> Bounding Box Generation: <ul> <li>The dataset does not include predefined bounding boxes, but they can be easily generated from the provided semantic and instance masks. This allows for flexible bounding box creation tailored to specific research needs.</li> </ul> Rendering Details: <ul> <li>3D Models and Materials: The dataset uses a consistent set of 3D models for all chess pieces, with three different material/color schemes applied across the dataset, along with random perturbations in material brightness, contrast and saturation to introduce visual variety.</li> <li>Camera Angles: Camera angles are randomly selected, with yaw ranging from 0 to 360 degrees and pitch between 30 to 80 degrees, providing diverse perspectives of the chess positions.</li> <li>Background Variability: The chessboard is randomly placed on different types of tables, with the floor material randomly sampled to create a variety of backgrounds.</li> <li>Lighting: Lighting conditions are randomly generated, adding further diversity and realism to the rendered scenes.</li> </ul> Augmentation Potential: <ul> <li>The instance and semantic masks can be used to further augment the dataset. Researchers can selectively modify specific parts of the images—such as the board, background, or individual pieces—enabling the creation of new variations and enhancing the dataset's utility for model training and testing.</li> </ul> Color Mapping: <ul> <li>The semantic masks are color-coded using a hue-based system, where the board frame, squares, and each piece type are assigned specific hues. Instances of the same piece type are differentiated by varying the value component, with saturation consistently set to 1. A detailed <code>color_mapping.json</code> file is included, providing a comprehensive guide to interpreting the masks.</li> </ul> Applications: ChessRender360 is ideal for tasks such as object detection, instance segmentation, depth estimation, and scene understanding in synthetic environments. Researchers and developers can leverage this dataset for training and evaluating models in computer vision, robotics, and artificial intelligence. Dataset Highlights: <ul> <li>10,000 uniquely rendered chess positions</li> <li>High-resolution images (2000x2000 pixels) with diverse visual characteristics</li> <li>Comprehensive annotations with RGB, depth (20 cm to 120 cm), instance, and semantic maps</li> <li>Side identification map for distinguishing white and black sides of the board</li> <li>FEN notation CSV file for easy position recognition and replication</li> <li>Variety introduced through different material schemes and lighting setups</li> <li>Randomized camera angles for enhanced perspective diversity</li> <li>Potential for further augmentation by modifying specific image components</li> <li>Detailed color mapping for easy interpretation of segmentation masks</li> <li>Suitable for a wide range of computer vision and AI applications</li> </ul> ChessRender360 offers a rich and versatile dataset for advancing research and development in the field of computer vision, providing a synthetic yet highly realistic environment for model training and testing. For any questions, feedback, or collaboration opportunities, or if you are interested in custom artificial datasets, please contact me: <ul> <li>Name: Marko Kojić</li> <li>LinkedIn: https://www.linkedin.com/in/mmkoya</li> </ul> I welcome inquiries from researchers, developers, and organizations interested in utilizing or collaborating on artificial datasets.

FIG. 12 in Récolnat Annotate-On: a tool to improve your experience with virtual collections

FIG. 12. — Workflow of the Annotate-On tool. A project allows the study, annotation and measurement of objects within images from the Récolnat database, URLs or any hard-drive to constitute a library. It uses one or more of the data models defined previously. Key words are used to select subsets within the library and the results can be exported to various external data processing tools.

FIG. 15 in Récolnat Annotate-On: a tool to improve your experience with virtual collections

FIG. 15. — Scales on Paris herbarium sheets. The vertical line on the left measures 10 mm, on the right the label of the linear barcodes measures 50 mm in length. In addition, the photos of the nomenclatural types have a scale.

FIG. 13 in Récolnat Annotate-On: a tool to improve your experience with virtual collections

FIG. 13. — Definition of a new character ("apex angle") for the model "Asparagus", here a physical measurement (angle unity: "°") into the group named "Cladode".

FIG. 9 in Récolnat Annotate-On: a tool to improve your experience with virtual collections

FIG. 9. — Annotation of a categorical character "apex shape" with the character state or nominal value "accuminate" (red arrow).

FIG. 1 in Récolnat Annotate-On: a tool to improve your experience with virtual collections

FIG. 1. — Examples of mean (M) and standard deviation (SD). Two characters: blade length (FII L) and blade width (fll- larg), number of measures (#). First line: image measures; second line: batch measures (global value).

opencc-by-4.0Feb 2023View details →

Self-Annotated Wearable Activity Data

Our dataset contains 2 weeks of approx. 8-9 hours of acceleration data per day from 11 participants wearing a <a href="https://shop.espruino.com/banglejs">Bangle.js Version 1</a> smartwatch with our <a href="https://github.com/kristofvl/BangleApps/tree/master/apps/activate_test">firmware</a> installed. The dataset contains annotations from  4 different commonly used annotation methods utilized in user studies that focus on in-the-wild data. These methods can be grouped in user-driven, in situ annotations - which are performed before or during the activity is recorded - and recall methods - where participants annotate their data in hindsight at the end of the day. The participants had the task to label their activities using (1) a button located on the smartwatch, (2) the activity tracking app <a href="https://www.strava.com/">Strava</a>, (3) a (hand)written diary and (4) a tool to visually inspect and label activity data, called <a href="https://github.com/mad-lab-fau/mad-gui">MAD-GUI</a>. Methods (1)-(3) are used in both weeks, however method (4) is introduced in the beginning of the second study week. The accelerometer data is recorded with 25 Hz, a sensitivity of ±8g and is stored in a csv format. Labels and raw data are not yet combined. You can either write your own script to label the data or follow the instructions in our corresponding <a href="https://github.com/ahoelzemann/annotationMatters">Github repository.</a> The following unique classes are included in our dataset: laying, sitting, walking, running, cycling, bus_driving, car_driving, vacuum_cleaning, laundry, cooking, eating, shopping, showering, yoga, sport, playing_games, desk_work, guitar_playing, gardening, table_tennis, badminton, horse_riding. However, many activities are very participant specific and therefore only performed by one of the participants. The labels are also stored as a .csv file and have the following columns: week_day, start, stop, activity, layer Example: week2_day2,10:30:00,11:00:00,vacuum_cleaning,d The layer columns specifies which annotation method was used to set this label. The following identifiers can be found in the column: b: in situ button a: in situ app d: self-recall diary g: time-series recall labelled with a the MAD-GUI   The corresponding publication is currently under review.

opencc-by-4.0Sep 2024View details →

The North Pacific Eukaryotic Gene Catalog: KOfam protein function annotations

KEGG functional annotation using KofamScan v1.3.0 These tables are larger alternative versions to the KOfam tables included in the North Pacific Eukaryotic Gene Catalog protein data repository here: <a href="../records/12630398">https://zenodo.org/records/12630398</a> A full description of this data is published in Scientific Data, available here: <a href="https://www.nature.com/articles/s41597-024-04005-5" target="_blank" rel="noopener">The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations</a>. Please cite this publication if your research uses this data: Groussman, R. D., Coesel, S. N., Durham, B. P., Schatz, M. J., & Armbrust, E. V. (2024). The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations. Scientific Data, 11(1), 1161. Clustered protein sequences were annotated against the KEGG collection (release 104.0) of 20,819 protein family Hidden Markov Models (HMMs) using <a href="https://github.com/takaram/kofam_scan" target="_blank" rel="noopener">KofamScan </a>and KofamKOALA. Kofam annotation code is documented in the project github repository here: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/aa_data/NPEGC.kofamscan_function.sh">NPEGC.kofamscan_function.sh</a> Excerpt of core NPEGC_kofam function: <code># Define input FASTA</code> <code>local INPUT_FASTA="NPac.${STUDY}.bf100.id99.aa.fasta"</code> <code># KofamScan call</code> <code>${KOFAM_DIR}/kofam_scan-1.3.0/exec_annotation -f detail-tsv -E ${EVALUE} -o ${ANNOTATION_DIR}/NPac.${STUDY}.bf100.id99.aa.tsv ${FASTA_DIR}/${INPUT_FASTA}</code> Unprocessed annotation results were filtered with a minimum score of 30 to remove low-scoring matches: <code>zcat NPac.NPacID.kofam.tsv.gz | awk -F'\t' '{ gsub(/"/, "", $5); $5 = $5 + 0; if ($5 >= 30) print }' | gzip > NPac.NPacID.UW.bf100.id99.aa.incT30.tsv.gz</code>

opencc-by-4.0Jul 2023View details →

MTClass: Identification and annotation of multi-phenotype cis-eQTLs using machine learning

This dataset contains the aggregated results from three iterations of MTClass. Brief descriptions of the file names are below: <ul> <li>multi-tissue.zip: Multi-tissue study containing the 9-tissue, 13 brain tissue, and 48-tissue results from MTClass, MultiPhen, and MANOVA</li> <li>multi-exon.zip: Multi-exon study containing the multi-exon results from the 13 individual brain tissues (MTClass, MultiPhen, and MANOVA for each tissue)</li> <li>2D_exon_tissue.zip: Multi-tissue/multi-exon combined eQTL study, done in 9 tissues using multi-layer perceptron. MTClass was only run once on this dataset due to the relatively higher computational burden.</li> <li>PsychENCODE_isoQTL.zip: Multi-isoform eQTL study, done in human prefrontal cortex using PsychENCODE data.</li> <li>OneK1K_scRNAseq.zip: Multi-cell-type eQTL study, done using scRNA-seq data from the OneK1K cohort.</li> </ul>

openmit-licenseOct 2024View details →

ProtNote: a multimodal method for protein-function annotation

<div> <div>Understanding protein sequence-function relationships is essential for advancing protein biology and engineering. However, fewer than 1% of known protein sequences have human-verified functions, and scientists continually update the set of possible functions. While deep learning methods have demonstrated promise for protein function prediction, current models are limited to predicting only those functions on which they were trained. Here, we introduce ProtNote, a multimodal deep learning model that leverages free-form text to enable both supervised and zero-shot protein function prediction. ProtNote not only maintains near state-of-the-art performance for annotations in its train set, but also generalizes to unseen and novel functions in zero-shot test settings. We envision that ProtNote will enhance protein function discovery by enabling scientists to use free text inputs, without restriction to predefined labels – a necessary capability for navigating the dynamic landscape of protein biology.</div> </div>

Linked collectors and determiners for: An annotated distributional checklist of Vespidae (Hymenoptera: Vespoidea) of India.

Natural history specimen data linked to collectors and determiners held within, "An annotated distributional checklist of Vespidae (Hymenoptera: Vespoidea) of India". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/4d93f591-1785-4eea-9b96-49b782137498">https://bionomia.net/dataset/4d93f591-1785-4eea-9b96-49b782137498</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/4d93f591-1785-4eea-9b96-49b782137498">https://gbif.org/dataset/4d93f591-1785-4eea-9b96-49b782137498</a>. Formatted as a Frictionless Data package.

Linked collectors and determiners for: An annotated catalogue of Elachistinae of the World (Lepidoptera: Gelechioidea: Elachistidae).

Natural history specimen data linked to collectors and determiners held within, "An annotated catalogue of Elachistinae of the World (Lepidoptera: Gelechioidea: Elachistidae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/e0aabdb8-bda6-48e4-bdef-f7b64577d5cd">https://bionomia.net/dataset/e0aabdb8-bda6-48e4-bdef-f7b64577d5cd</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/e0aabdb8-bda6-48e4-bdef-f7b64577d5cd">https://gbif.org/dataset/e0aabdb8-bda6-48e4-bdef-f7b64577d5cd</a>. Formatted as a Frictionless Data package.

Linked collectors and determiners for: An annotated list of the Lepidoptera of Alberta, Canada.

Natural history specimen data linked to collectors and determiners held within, "An annotated list of the Lepidoptera of Alberta, Canada". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/aa97d560-1969-4b79-93bb-2614b7835c0e">https://bionomia.net/dataset/aa97d560-1969-4b79-93bb-2614b7835c0e</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/aa97d560-1969-4b79-93bb-2614b7835c0e">https://gbif.org/dataset/aa97d560-1969-4b79-93bb-2614b7835c0e</a>. Formatted as a Frictionless Data package.

Linked collectors and determiners for: An annotated checklist of the earthworm fauna of Turkey (Oligochaeta: Lumbricidae).

Natural history specimen data linked to collectors and determiners held within, "An annotated checklist of the earthworm fauna of Turkey (Oligochaeta: Lumbricidae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/6bf97383-b199-4676-96bd-49d292d4bde7">https://bionomia.net/dataset/6bf97383-b199-4676-96bd-49d292d4bde7</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/6bf97383-b199-4676-96bd-49d292d4bde7">https://gbif.org/dataset/6bf97383-b199-4676-96bd-49d292d4bde7</a>. Formatted as a Frictionless Data package.

Linked collectors and determiners for: Rediscovery of Obeliscus agassizi Pilsbry, 1906 (Gastropoda, Subulinidae, Obeliscinae), annotated checklist of species of Obeliscus Beck, 1837 and first description of the anatomy for the genus.

Natural history specimen data linked to collectors and determiners held within, "Rediscovery of Obeliscus agassizi Pilsbry, 1906 (Gastropoda, Subulinidae, Obeliscinae), annotated checklist of species of Obeliscus Beck, 1837 and first description of the anatomy for the genus". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/5920d575-69e3-45ef-bf81-10859e823214">https://bionomia.net/dataset/5920d575-69e3-45ef-bf81-10859e823214</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/5920d575-69e3-45ef-bf81-10859e823214">https://gbif.org/dataset/5920d575-69e3-45ef-bf81-10859e823214</a>. Formatted as a Frictionless Data package.