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opencc-zeroSep 2022View details →

LMAS Test Dataset - BMock12 Community Standards

The twelve bacterial replicons of the <a href="https://www.nature.com/articles/s41597-019-0287-z#Abs1">BMock12 Community Standards</a> were used as reference. It includes the following strains: <table> <thead> <tr> <th>Species</th> <th>Sample ID</th> <th>Depth of Coverage (x)</th> </tr> </thead> <tbody> <tr> <td>Muricauda sp. ES.050</td> <td>2615840527</td> <td>618.76</td> </tr> <tr> <td>Thioclava sp. ES.032</td> <td>2615840533</td> <td>78.32</td> </tr> <tr> <td>Cohaesibacter sp. ES.047</td> <td>2615840601</td> <td>170.59</td> </tr> <tr> <td>Propionibacteriaceae bacterium</td> <td>2615840646</td> <td>31.90</td> </tr> <tr> <td>Marinobacter sp. LV10R510-8</td> <td>2615840697</td> <td>447.83</td> </tr> <tr> <td>Marinobacter sp. LV10MA510-1</td> <td>2616644829</td> <td>135.05</td> </tr> <tr> <td>Psychrobacter sp. LV10R520-6</td> <td>2617270709</td> <td>425.47</td> </tr> <tr> <td>Micromonospora echinaurantiaca</td> <td>2623620557</td> <td>14.91</td> </tr> <tr> <td>Micromonospora echinofusca</td> <td>2623620567</td> <td>18.19</td> </tr> <tr> <td>Micromonospora coxensis</td> <td>2623620609</td> <td>0.02</td> </tr> <tr> <td>Halomonas sp. HL-4</td> <td>2623620617</td> <td>507.08</td> </tr> <tr> <td>Halomonas sp. HL-93</td> <td>2623620618</td> <td>579.87</td> </tr> </tbody> </table> The raw sequence data of the mock communities, with an even distribution of species, is available at: <ul> <li><a href="https://www.ebi.ac.uk/ena/browser/view/SRR8073716?show=reads">SRR8073716</a></li> </ul> For the sake of a less resource-intense evaluation we downsampled to have 20% of the reads available in the original sample and further processed only these. The main challenges of this data set are possibly to assemble the genomes of the three Micromonospora spp. and the two Halomonas spp. strains, which have an ANIb >0.84 and 0.98, respectively. The two Marinobacter spp. have an ANIb of 0.78.

opencc-zeroSep 2022View details →

LMAS Test Dataset - ZymoBIOMICS Microbial Community Standards

The <a href="https://zenodo.org/record/4588970#.YEeA83X7RhE">eight bacterial genomes and four plasmids of the ZymoBIOMICS Microbial Community Standards</a> were used as reference. It contains tripled complete sequences for the following species: <ul> <li>Bacillus subtilis</li> <li>Enterococcus faecalis</li> <li>Escherichia coli <ul> <li>Escherichia coli plasmid</li> </ul> </li> <li>Lactobacillus fermentum</li> <li>Listeria monocytogenes</li> <li>Pseudomonas aeruginosa</li> <li>Salmonella enterica</li> <li>Staphylococcus aureus <ul> <li>Staphylococcus aureus plasmid 1</li> <li>Staphylococcus aureus plasmid 2</li> <li>Staphylococcus aureus plasmid 3</li> </ul> </li> </ul> It also downloads the raw sequence data of the mock communities, with an even and logarithmic distribution of species: <ul> <li><a href="https://www.ebi.ac.uk/ena/browser/view/ERR2984773">ERR2984773</a> - Evenly distributed real sample</li> <li><a href="https://www.ebi.ac.uk/ena/browser/view/ERR2935805">ERR2935805</a> - Log distributed real sample</li> </ul> A set of simulated samples were generated from the genomes in the ZymoBIOMICS standard though the <a href="https://github.com/HadrienG/InSilicoSeq">InSilicoSeq sequence simulator</a> (version 1.5.2), including both even and logarithmic distribution, with and without Illumina error model. The number of read pairs generated matches the number of read pairs in the real data for each distribution. The following samples are available in Zenodo: <ul> <li>ENN - Evenly distributed sample with no error model</li> <li>EMS - Evenly distributed sample with Illumina MiSeq error model</li> <li>LNN - Log distributed sample with no error model</li> <li>LHS - Log distributed sample with Illumina HiSeq error model</li> </ul> <a href="https://doi.org/10.5281/zenodo.4588970">DOI Dataset</a>

opencc-by-4.0Apr 2022View details →

Dataset for use with "Technology standards vs. carbon taxes: comparing emissions reductions and system costs for US decarbonization"

This sqlite file contains the input data and results used in "Technology standards vs. carbon taxes: comparing emissions reductions and system costs for US decarbonization." The file is a version of the nine region Open Energy Outlook database for use with the Temoa model. The database is continually updated. Updated versions can be found in the Temoa GitHub repository, https://github.com/TemoaProject/oeo.

opencc-by-4.0Oct 2024View details →

Molecular surface coverage standards by reference-free GIXRF supporting SERS and SEIRA substrate benchmarking - Dataset

This is the dataset of "Molecular surface coverage standards by reference-free GIXRF supporting SERS and SEIRA substrate benchmarking".  <a href="https://doi.org/10.1515/nanoph-2024-0222" target="_blank" rel="noopener">https://doi.org/10.1515/nanoph-2024-0222</a> Part of this work was supported by the European project OpMetBat, code 21GRD01. The project has received funding from the European Partnership on Metrology, cofinanced from the the European Union's Horizon Europe Research and Innovation Programme, and by Participating States.

opencc-zeroJul 2021View details →

NLM-Gene, a richly annotated gold standard dataset for gene entities that addresses ambiguity and multi-species gene recognition

The automatic recognition of gene names and their corresponding database identifiers in biomedical text is an important first step for many downstream text-mining applications. The NLM-Gene corpus is a high-quality manually annotated corpus for genes, covering ambiguous gene names, with an average of 29 gene mentions (10 unique identifiers) per article, and a broader representation of different species (including Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Arabidopsis thaliana, Danio rerio, etc.) when compared to previous gene annotation corpora. NLM-Gene consists of 550 PubMed articles from 156 biomedical journals, doubly annotated by six experienced NLM indexers, randomly paired for each article to control for bias. The annotators worked in three annotation rounds until they reached a complete agreement. Using the new resource, we developed a new gene finding algorithm based on deep learning which improved both on precision and recall from existing tools. The NLM-Gene annotated corpus is freely available at Dryad and at <a href="https://www.ncbi.nlm.nih.gov/research/bionlp/">https://www.ncbi.nlm.nih.gov/research/bionlp/</a>. The gene finding results of applying this tool to the entire PubMed/PMC are freely accessible through our web-based tool PubTator.

opencc-byOct 2022View details →

Supplementary Dataset for "Synthetic Witherite for Standardization of Clumped Isotope (Delta 47) Analyses"

This supplementary dataset contains raw clumped isotopic data used to generate figures in the article "Synthetic Witherite for Standardization of Clumped Isotope (Delta 47) Analyses" by Kong et al.

publicApr 2025View details →

A multi-sensor gait dataset collected under non-standardized dual-task conditions

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publicMay 2025View details →

Standardized incidence ratio dataset of human West Nile Virus in Italy (2012-2024)

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publicJul 2021View details →

NLM-Gene, a richly annotated gold standard dataset for gene entities that addresses ambiguity and multi-species gene recognition

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opencc-by-4.0Jul 2020View details →

MESINESP: Post-workshop datasets. Silver Standard and annotator records

Please use the <a href="https://doi.org/10.5281/zenodo.4612274">MESINESP2 corpus (the second edition of the shared-task)</a> since it has a higher level of curation, quality and is organized by document type (scientific articles, patents and clinical trials). The MESINESP (Spanish BioASQ track, see https://temu.bsc.es/mesinesp) Challenge was held in May-June 2020, and as a result of a strong participation and the manual annotation of an evaluation dataset, two additional datasets are released now: 1) "all_annotations_withIDsv3.tsv" contains a tab-separated file with all manual annotations (both validated and non-validated) of the evaluation dataset prepared for the competition. It contains the following fields: <ul> <li>annotatorName: Human annotator id</li> <li>documentId: Document ID in the source database</li> <li> decsCode: A DeCS code added to it or validated</li> <li>timestamp: When it was added</li> <li>validated: if it was validated at that point by another annotator, or not yet</li> <li>SpanishTerm: The Spanish descriptor corresponding to the DeCS code</li> <li>mesinespId: The internal document id in the distributed  evaluation file</li> <li>dataset: if part of the evaluation or the test sets</li> <li>source: which database it was taken from</li> </ul> Example: annotatorName    documentId    decsCode    timestamp    validated    SpanishTerm    mesinespId    dataset    source A7    biblio-1001069    6893    2020-01-17T11:27:07.000Z    false    caballos    mesinesp-dev-671    dev    LILACS A7    biblio-1001069    4345    2020-01-17T11:27:12.000Z    false    perros    mesinesp-dev-671    dev    LILACS   2) A "Silver Standard" created from the 24 system runs submitted by 6 participating teams. It contains each of the submitted DeCS code for each document in the test set, as well as other information that can help ascertain reliability and source for anyone that wants to use this dataset to enrich their training data. It contains more that 5.8 million datapoints, and is structured as follows <ul> <li>SubmissionName:  Alias of the team that submitted the run</li> <li>REALdocumentId: The real id of the document</li> <li>mesinespId:    The mesinesp assigned id in the evaluation dataset</li> <li>docSource: The source database</li> <li>decsCode: the DeCS code assigned to it by the team's system</li> <li>SpanishTerm: The Spanish descriptor of the DeCS code</li> <li>MiF: The Micro-f1 scored by that system's run</li> <li>MiR: The Micro-Recall scored by that system's run</li> <li>MiP:  The Micro-Precision scored by that system's run   </li> <li>Acc: The Accuracy scored by that system's run</li> <li>consensus: The number of runs where that DeCS code was assigned to this document by the participating teams (max. is 24)</li> </ul> Example: SubmissionName    REALdocumentId    mesinespId    docSource    decsCode    SpanishTerm    MiF    MiR    MiP    Acc    consensus AN    ibc-177565    mesinesp-evaluation-00001    IBECS    28567    riesgo    0.2054    0.1930    0.2196    0.1198    4 AN    ibc-177565    mesinesp-evaluation-00001    IBECS    15335    trabajo    0.2054    0.1930    0.2196    0.1198    4 AN    ibc-177565    mesinesp-evaluation-00001    IBECS    33182    conocimiento    0.2054    0.1930    0.2196    0.1198    7   For citation and a detailed description of the Challenge, please cite: Anastasios, Nentidis and Anastasia, Krithara and Konstantinos, Bougiatiotis and Martin, Krallinger and Carlos, Rodriguez-Penagos and Marta, Villegas and Georgios, Paliouras. Overview of BioASQ 2020: The eighth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering (2020). Proceedings of the Eleventh International Conference of the CLEF Association (CLEF 2020). Thessaloniki, Greece, September 22--25 Citation @inproceedings{durusan2019overview,   title={Overview of BioASQ 2020: The eighth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering},   author={Anastasios, Nentidis and Anastasia, Krithara and Konstantinos, Bougiatiotis and Martin, Krallinger and Carlos, Rodriguez-Penagos and Marta, Villegas and Georgios, Paliouras},   booktitle={Experimental IR Meets Multilinguality, Multimodality, and Interaction Proceedings of the Eleventh International Conference of the CLEF Association (CLEF 2020), Thessaloniki, Greece, September 22--25, 2020, Proceedings},   volume={12260},   year={2020},   organization={Springer} }   Copyright (c) 2020 Secretaría de Estado de Digitalización e Inteligencia Artificial

opencc-by-nc-nd-1.0Nov 2024View details →

Gold Standard and Annotation Dataset for CO2 Emissions Annotation

This repository contains the results of a research project which provides a benchmark dataset for extracting greenhouse gas emissions from corporate annual and sustainability reports.  The zipped <code>datasets</code> file contains two datasets, <code>gold_standard</code> and <code>annotation_dataset</code>(inside the outer zip file there is a password-protected zip file containing the two datasets. To unpack, use the password is provided in the outer zip file). <h3>Data collection</h3> <ol> <li>A Large Language Model (LLM) based pipeline was used to extract the greenhouse gas emissions from the reports (see columns prefixed with <code>llm_</code> in <code>annotation_dataset</code>). The extracted emissions follow the categories Scope 1, 2 (market-based) and 2 (location-based) and 3, as defined in the GHGP protocol (see variables <code>scope</code>).</li> <li>Annotation of the pipeline output was done in 3 phases: first by non-experts (see columns prefixed with <code>non_expert_</code> in <code>annotation_dataset</code>), then by expert groups (columns prefixed with <code>exp_group_</code> in <code>annotation_dataset</code>) in case of disagreement of non-experts and finally in a discussion of all experts (columns prefixed with <code>exp__disc</code> in <code>annotation_dataset</code>) in case of disagreement between expert groups. The annotation guidelines for the <a href="https://zenodo.org/api/records/15124118/draft/files/Non-Expert%20Annotation%20Guidelines.pdf/content">non-experts</a> and <a href="https://zenodo.org/api/records/15124118/draft/files/Expert%20Annotation%20Guidelines.pdf/content">experts</a> are also included in this repository.</li> <li>The annotation results from all three phases are combined to form the final benchmark dataset: <code>gold_standard</code>. Codebooks detailing each variable of each of the two datasets are also provided. More details about the annotation template or the data wrangling scripts can be found in the <a href="https://github.com/soda-lmu/gist-data-descriptor/" target="_blank" rel="noopener">GitHub repository</a>. </li> </ol> <h3>Merging of datasets</h3> Users can match the two datasets (<code>gold_standard</code> and <code>annotation_dataset</code>) using the variable combination of <code>company_name</code>, <code>report_year</code> and <code>merge_id</code> (index column). The <code>merge_id</code> already includes the company name and report year implicitly, but to avoid column duplication in the join operation, it should be included as join variables. For example this is useful when comparing LLM extractions to gold standard data.

opencc-by-4.0Jan 2022View details →

LegacyPollen 1.0: A taxonomically harmonized global Late Quaternary pollen dataset of 2831 records with standardized chronologies

This repository consists of code for downloading pollen data from the Neotoma Paleoecology Database, the harmonization of pollen taxa, and the assignment of age-depth data so that datasets for customized harmonization levels can be easily established. The input data includes a harmonization table and example data, stored in machine-readable data format (.CSV).

opencc-by-nc-4.0Jun 2024View details →

"Play by Play": a dataset of handball and basketball game situations in a standardized space

Synthetic dataset of labeled game situations present in recordings of federated handball and basketball matches played in Galicia, Spain. The dataset consists of synthetic data generated from real video frames, including 308,805 labeled handball frames and 56,578 labeled basketball frames extracted from 2,105 handball and 383 basketball 5-second video clips. For each frame, the dataset includes player positions and directions (x, y, vx, vy), and ball position (referees are also considered as players, we do not distinguish teams or players from referees). For each tuple there is an associated game situation: right/left attack, right/left counterattack, right/left penalty and time out.

opencc-by-4.0Jan 2024View details →

IVD-SEG:Standardized Datasets for Industrial Vision Defect Segmentation

<h1> </h1> <h1>Code[<a href="https://github.com/KLIVIS/IVD-SEG">Github</a>]</h1> <h1>abstract</h1> We introduce IVD-SEG, a dataset encompassing defect images from 43 different industrial products, totaling 5686 images, spanning tasks that include binary and multiclass segmentation. Within IVD-SEG, we meticulously propose 12 sub-datasets, including two newly developed datasets by our team. Serving as a large-scale standardized industrial defect image dataset, all images are unified to a 256 × 256 size, accompanied by semantic segmentation annotations. This standardization facilitates users unfamiliar with industrial product defects to utilize the dataset, allowing them to focus on exploring algorithmic performance on the IVD-SEG dataset. To our knowledge, the dataset we propose is currently the most comprehensive and voluminous compilation of industrial defect images, thereby contributing to the advancement of relevant research in industrial image analysis. Simultaneously, our dataset supports research and education in various fields, including computer vision and machine learning. We conducted benchmark tests on IVD-SEG using several baseline methods, including representative CNN and ViT networks.

opencc-by-4.0Sep 2024View details →

Dataset: Gold standard dataset for explainability need detection in app reviews.

We crawled 90,000 app reviews from both Google Play Store and Apple App Store, including reviews from both free and paid apps. These reviews were filtered for explainability needs, and after this process, 4,495 reviews remained. Among them, 2,185 reviews indicated an explanation need, while 2,310 did not. This resulting gold standard dataset was used to train and evaluate several machine learning models and rule-based approaches for detecting explanation needs in app reviews. The dataset includes both balanced and unbalanced evaluation sets, as well as the original crawled data from October 2023. In addition to machine learning approaches, rule-based methods optimized for F1 score, precision, and recall are also included. We provide several pre-trained machine learning models (including BERT, SetFit, AdaBoost, K-Nearest Neighbor, Logistic Regression, Naive Bayes, Random Forest, and SVM) along with training scripts and evaluation notebooks. These models can be applied directly or retrained using the included datasets. For further details on the structure and usage of the dataset, please refer to the README.md file within the provided ZIP archive.