Find research datasets worth reusing

opencc-by-4.0May 2023View details →

Picea mariana isolate 40-10-1 genome annotation

Genome annotation of Picea mariana isolate 40-10-1. Gene models were identified using BRAKER v2.1.6 and functionally annotated with EnTAP v0.10.8.  pmariana-v1.gff - Genome annotation in GFF3 format pmariana-v1_proteins.faa - FASTA file of translated protein-coding sequences pmariana-v1_transcripts.fa - FASTA file of transcripts (CDS)

opencc-by-4.0Dec 2022View details →

AI-derived annotations for the NLST and NSCLC-Radiomics computed tomography imaging collections

Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many of the available datasets do not provide annotations of tumors or  organs-at-risk, crucial for the assessment of these tools. This is due to the fact that annotation of medical images is time consuming and requires domain expertise. It has been demonstrated that artificial intelligence (AI) based annotation tools can achieve acceptable performance and thus can be used to automate the annotation of large datasets. As part of the effort to enrich the public data available within NCI Imaging Data Commons (IDC) (<a href="https://imaging.datacommons.cancer.gov/">https://imaging.datacommons.cancer.gov/</a>) [1], we introduce this dataset that consists of such AI-generated annotations for two publicly available medical imaging collections of Computed Tomography (CT) images of the chest. For detailed information concerning this dataset, please refer to our publication <a href="https://www.nature.com/articles/s41597-023-02864-y">here</a> [2].  We use publicly available pre-trained AI tools to enhance CT lung cancer collections that are unlabeled or partially labeled. The first tool is the nnU-Net deep learning framework [3] for volumetric segmentation of organs, where we use a pretrained model (Task D18 using the SegTHOR dataset) for labeling volumetric regions in the image corresponding to the heart, trachea, aorta and esophagus. These are the major organs-at-risk for radiation therapy for lung cancer. We further enhance these annotations by computing 3D shape radiomics features using the pyradiomics package [4]. The second tool is a pretrained model for per-slice automatic labeling of anatomic landmarks and imaged body part regions in axial CT volumes [5]. We focus on enhancing two publicly available collections, the Non-small Cell Lung Cancer Radiomics (NSCLC-Radiomics collection) [6,7], and the National Lung Screening Trial (NLST collection) [8,9]. The CT data for these collections are available both in The Cancer Imaging Archive (TCIA) [10] and in NCI Imaging Data Commons (IDC). Further, the NSLSC-Radiomics collection includes expert-generated manual annotations of several chest organs, allowing us to quantify performance of the AI tools in that subset of data. IDC is relying on the DICOM standard to achieve FAIR [10] sharing of data and interoperability. Generated annotations are saved as DICOM Segmentation objects (volumetric segmentations of regions of interest) created using the dcmqi [12], and DICOM Structured Report (SR) objects (per-slice annotations of the body part imaged, anatomical landmarks and radiomics features) created using dcmqi and highdicom [13]. 3D shape radiomics features and corresponding DICOM SR objects are also provided for the manual segmentations available in the NSCLC-Radiomics collection.  The dataset is available in IDC, and is accompanied by our publication <a href="https://www.nature.com/articles/s41597-023-02864-y">here</a> [2]. This pre-print details how the data were generated, and how the resulting DICOM objects can be interpreted and used in tools. Additionally, for further information about how to interact with and explore the dataset, please refer to our <a href="https://github.com/ImagingDataCommons/nnU-Net-BPR-annotations/">repository</a> and accompanying <a href="https://github.com/ImagingDataCommons/nnU-Net-BPR-annotations/blob/main/usage_notebooks/scientific_data_paper_usage_notes.ipynb">Google Colaboratory notebook</a>.  The annotations are organized as follows. For NSCLC-Radiomics, three nnU-Net models were evaluated ('2d-tta', '3d_lowres-tta' and '3d_fullres-tta'). Within each folder, the PatientID and the StudyInstanceUID are subdirectories, and within this the DICOM Segmentation object and the DICOM SR for the 3D shape features are stored. A separate directory for the DICOM SR body part regression regions ('sr_regions') and landmarks ('sr_landmarks') are also provided with the same folder structure as above. Lastly, the DICOM SR for the existing manual annotations are provided in the 'sr_gt' directory. For NSCLC-Radiomics, each patient has a single StudyInstanceUID. The DICOM Segmentation and SR objects are named according to the SeriesInstanceUID of the original CT files.  <ul> <li>nsclc <ul> <li>2d-tta <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_SEG.dcm</li> <li>ReferencedSeriesInstanceUID_features_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>3d_lowres-tta <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_SEG.dcm</li> <li>ReferencedSeriesInstanceUID_features_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>3d_fullres-tta  <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_SEG.dcm</li> <li>ReferencedSeriesInstanceUID_features_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>sr_regions <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_regions_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>sr_landmarks <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_landmarks_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>sr_gt <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_features_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul> For NLST, the '3d_fullres-tta' model was evaluated. The data is organized the same as above, where within each folder the PatientID and the StudyInstanceUID are subdirectories. For the NLST collection, it is possible that some patients have more than one StudyInstanceUID subdirectory. A separate directory for the DICOM SR body par regions ('sr_regions') and landmarks ('sr_landmarks') are also provided. The DICOM Segmentation and SR objects are named according to the SeriesInstanceUID of the original CT files.  <ul> <li>nlst <ul> <li>3d_fullres-tta  <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_SEG.dcm</li> <li>ReferencedSeriesInstanceUID_features_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>sr_regions <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_regions_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>sr_landmarks <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_landmarks_SR.dcm </li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul> The query used for NSCLC-Radiomics is <a href="https://github.com/ImagingDataCommons/ai_medima_misc/blob/main/common/queries/NSCLC_Radiomics_query.txt">here</a>, and a list of corresponding SeriesInstanceUIDs (along with PatientIDs and StudyInstanceUIDs) is <a href="https://github.com/ImagingDataCommons/ai_medima_misc/blob/main/common/queries/zenodo_nsclc_radiomics_series_analyzed.csv">here</a>. The query used for NLST is <a href="https://github.com/ImagingDataCommons/ai_medima_misc/blob/main/common/queries/NLST_query.txt">here</a>, and a list of corresponding SeriesInstanceUIDs (along with PatientIDs and StudyInstanceUIDs) is <a href="https://github.com/ImagingDataCommons/ai_medima_misc/blob/main/common/queries/zenodo_nlst_series_analyzed.csv">here</a>. The two csv files that describe the series analyzed, nsclc_series_analyzed.csv and nlst_series_analyzed.csv, are also available as uploads to this repository.  Version updates:  Version 2: For the regions SR and landmarks SR, changed to use a distinct TrackingUniqueIdentifier for each MeasurementGroup. Also instead of using TargetRegion, changed to use FindingSite. Additionally for the landmarks SR, the TopographicalModifier was made a child of FindingSite instead of a sibling. Version 3: Added the two csv files that describe which series were analyzed  Version 4: Modified the landmarks SR as the TopographicalModifier for the Kidney landmark (bottom) does not describe the landmark correctly. The Kidney landmark is the "first slice where both kidneys can be seen well." Instead, removed the use of the TopographicalModifier for that landmark. For the features SR, modified the units code for the Flatness and Elongation, as we incorrectly used mm units instead of no units.

opencc-by-4.0Nov 2022View details →

Translation Alignment: Ancient Greek to Latin. Annotation Style Guide and Gold Standard

This dataset contains guidelines and a gold standard for the alignment of Ancient Greek texts with Latin scholarly translations.  The gold standard consists of 100 fragments randomly selected from the Digital Fragmenta Historicorum Graecorum (DFHG) (https://www.dfhg-project.org/), which were aligned manually by Chiara Palladino and David J. Wright using Ugarit (https://ugarit.ialigner.com/). The Annotation Style Guide was developed for this project. The resulting Inter-Annotator-Agreement (IAA) is 90.5%.   The materials available in this repository can be used to perform and evaluate alignments of various texts in Ancient Greek, to create gold standards, and to train automated translation alignment models.  The Guidelines can be further adapted to address similar language pairs including inflected languages, or can provide a structure for the alignment of other historical texts against modern translations. However, the guidelines are not project-specific: they were specifically intended for the scenario of machine translation. Different research questions, such as translation history or pedagogy, may need further tweaking of these guidelines.  For further information on Ugarit and translation alignment of historical languages, see http://ugarit.aligner.com/bib.php and follow us on Twitter (@ugarit_ty).

opencc-by-4.0Jun 2023View details →

Annotated dataset mentions corpus in IR/ML/NLP domain

This corpus is a re-annotated version of the dataset available at <a href="https://github.com/xjaeh/ner_dataset_recognition">https://github.com/xjaeh/ner_dataset_recognition</a> and described in the following publication: Heddes, J.; Meerdink, P.; Pieters, M.; Marx, M. The Automatic Detection of Dataset Names in Scientific Articles. Data 2021, 6, 84. <a href="https://doi.org/10.3390/data6080084">https://doi.org/10.3390/data6080084</a> The corpus contains 6000 sentences in the IR/ML/NLP domains, with dataset annotations. The original corpus in CSV covered only explicitly named and reused datasets. In addition, "conjunctions" of datasets were annotated in a single span. We review entirely the annotation to include new datasets too (as developed in the described research work of the source articles) and to annotate separately every individual datasets. In addition, we re-packaged the corpus into a more standard JSON with annotation offsets. Python scripts for conversion are available at <a href="https://github.com/kermitt2/dataset_recognition_resources">https://github.com/kermitt2/dataset_recognition_resources</a> We thank the original authors of the corpus for their very valuable resource !

opencc-by-4.0Dec 2022View details →

Genome annotations of darkbarbel catfish (Pelteobagrus vachelli)

Genome annotations (Pva.annotation.v1.gff.gz), along with predicted coding sequences (Pva.annotation.v1.cds.fa.gz) and protein sequences (Pva.annotation.v1.pep.fa.gz) of darkbarbel catfish genome assembly.

opencc-by-4.0Jul 2023View details →

Extensions to Mining Framework Annotation Rules

Framework usage is challenging because the requirements for the correctness are often implicit. We focus on making such requirements more explicit by association rule mining on the data from client projects that use a framework. We present an extension to an existing baseline method that does this. In particular, we examine alternative rule quality measures used in the ranking of association rules mined, and alternatives in the selection of client projects. Such alternatives are novel and have not been explored in the context of the baseline method. We evaluate the alternatives by comparing their results to those produced by the baseline method. More concretely, we base the comparison on their ranking of incorrect rules, and on their measurements for the Area Under Curve metric. We conclude that some of the evaluated quality measures outperform the baseline for the ranking and selection of rules. We also show that the selection of secondary client projects, adding some clients that do not directly use the framework of interest, matters.

opencc-by-4.0Aug 2023View details →

Gene annotations of Amphibolurus muricatus (jacky dragon), Intellagama lesueurii (Australian water dragon), Phrynocephalus przewalskii (Przewalski's toadhead agama), and Phrynocephalus vlangalii (Ching Hai toadhead agama)

Annotation file and associated FASTA files for A. muricatus assembly AmpMurF_3.0 • AmpMurF3.gff3.tar.gz: EVidenceModeler (EVM) gene model annotation file. • AmpMurF3.cds.tar.gz: EVM gene models coding sequences. • AmpMurF3.pep.tar.gz: EVM gene models coding sequences translated into amino acid sequences. Annotation file and associated FASTA files for A. muricatus assembly AmpMurM_3.0 • AmpMurM3.gff3.tar.gz: EVidenceModeler (EVM) gene model annotation file. • AmpMurM3.cds.tar.gz: EVM gene models coding sequences. • AmpMurM3.pep.tar.gz: EVM gene models coding sequences translated into amino acid sequences. Annotation file and associated FASTA files for I. lesueurii (Australian water dragon; assembly EWD_hifiasm_HiC generated as part of the AusARG consortium) • Intellagama_lesueurii.evm.final.add_replace_buscoV5_homolog.final.gff3.tar.gz: EVidenceModeler (EVM) gene model annotation file. • Intellagama_lesueurii.evm.final.add_replace_buscoV5_homolog.final.cds.fa.tar.gz: EVM gene models coding sequences. • Intellagama_lesueurii.evm.final.add_replace_buscoV5_homolog.final.pep.fa.tar.gz: EVM gene models coding sequences translated into amino acid sequences. Annotation file and associated FASTA files for P. przewalskii (Przewalski’s toadhead agama; see PMID ID 30808754 and CNGBdb accession no. CNP0000203)  • Phrynocephalus_przewalskii.evm.final.add_replace_buscoV5_homolog.gff3.tar.gz: EVidenceModeler (EVM) gene model annotation file. • Phrynocephalus_przewalskii.evm.final.add_replace_buscoV5_homolog.cds.fa.tar.gz: EVM gene models coding sequences. • Phrynocephalus_przewalskii.evm.final.add_replace_buscoV5_homolog.pep.fa.tar.gz: EVM gene models coding sequences translated into amino acid sequences. Annotation file and associated FASTA files for P. vlangalii (Ching Hai toadhead agama; see PMID ID 30808754 and CNGBdb accession no. CNP0000203) • Phrynocephalus_vlangalii.evm.final.add_replace_busco_homolog.gff3.tar.gz: EVidenceModeler (EVM) gene model annotation file. • Phrynocephalus_vlangalii.evm.final.add_replace_busco_homolog.cds.fa.tar.gz: EVM gene models coding sequences. • Phrynocephalus_vlangalii.evm.final.add_replace_busco_homolog.pep.fa.tar.gz: EVM gene models coding sequences translated into amino acid sequences.

opencc-byAug 2023View details →

Audiovisual Moments in Time: A Large-Scale Annotated Dataset of Audiovisual Actions

We present Audiovisual Moments in Time (AVMIT), a large-scale dataset of audiovisual action events. In an extensive annotation task 11 participants labelled a subset of 3-second audiovisual videos from the Moments in Time dataset (MIT). For each trial, participants assessed whether the labelled audiovisual action event was present and whether it was the most prominent feature of the video. The dataset includes the annotation of 57,177 audiovisual videos, each independently evaluated by 3 of 11 trained participants. From this initial collection, we created a curated test set of 16 distinct action classes, with 60 videos each (960 videos). We also offer 2 sets of pre-computed audiovisual feature embeddings, using VGGish/YamNet for audio data and VGG16/EfficientNetB0 for visual data, thereby lowering the barrier to entry for audiovisual DNN research. We further carried out an experiment to explore the utility of the AVMIT annotations and feature embeddings. A series of 6 Recurrent Neural Networks (RNNs) were trained on either AVMIT-filtered audiovisual events or modality-agnostic events from MIT, and then tested on our audiovisual test set. In all RNNs, top 1 accuracy was increased by 2.71-5.94\% by training exclusively on audiovisual events, even outweighing a three-fold increase in training data. We anticipate that the newly annotated AVMIT dataset will serve as a valuable resource for research and comparative experiments involving computational models and human participants, specifically when addressing research questions where audiovisual correspondence is of critical importance.

opencc-by-4.0Aug 2023View details →

Annotation of inverted repeats displaying features of pble STIR or IR in the hg38 genome model

Annotation of inverted repeats displaying features of pble STIR or IR in the hg38 genome model. The annotation of pble-like inner inverted repeats was done using Palindrome (EMBOSS package). The output file was then filtered using pal2gff (https://github.com/Leelouh/pal2gff/blob/main/pal2gff.py), using as parameters a repeat size between 5 and 15 nucleotides, a spacer between pairs of inverted repeats (IRs) of 2 to 10 nucleotides, and a number of mismatches within repeats ranging from 0 to 1. These parameters were chosen taking into account those of the inner IRs found at ends of invertebrate pbles.

opencc-by-4.0Jan 2023View details →

POLIcy design ANNotAtions (POLIANNA): Towards understanding policy design through text-as-data approaches

The POLIANNA dataset is a collection of legislative texts from the European Union (EU) that have been annotated based on theoretical concepts of policy design. The dataset consists of 20,577 annotated spans in 412 articles, drawn from 18 EU climate change mitigation and renewable energy laws, and can be used to develop supervised machine learning approaches for scaling policy analysis. The dataset includes a novel coding scheme for annotating text spans, and you find a description of the annotated corpus, an analysis of inter-annotator agreement, and a discussion of potential applications in the paper accompanying this dataset. The objective of this dataset to build tools that assist with manual coding of policy texts by automatically identifying relevant paragraphs. Detailed instructions and further guidance about the dataset as well as all the code used for this project can be found in the accompanying <a href="https://doi.org/10.1038/s41597-023-02801-z">paper</a> and on the <a href="https://github.com/kueddelmaier/POLIANNA">GitHub project page</a>. The repository also contains useful code to calculate various inter-annotator agreement measures and can be used to process text annotations generated by <a href="https://inception-project.github.io/">INCEpTION</a>.   Dataset Description We provide the dataset in 3 different formats: JSON: Each article corresponds to a folder, where the Tokens and Spans are stored in a separate JSON file. Each article-folder further contains the raw policy-text as in a text file and the metadata about the policy. This is the most human-readable format. JSONL: Same folder structure as the JSON format, but the Spans and Tokens are stored in a <a href="https://jsonlines.org/">JSONL</a> file, where each line is a valid JSON document. Pickle: We provide the dataset as a Python object. This is the recommended method when using our own Python framework that is provided on GitHub. For more information, check out the GitHub project page. License The POLIANNA dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. If you use the POLIANNA dataset in your research in any form, please cite the dataset.   Citation Sewerin, S., Kaack, L.H., Küttel, J. et al. Towards understanding policy design through text-as-data approaches: The policy design annotations (POLIANNA) dataset. Sci Data10, 896 (2023). https://doi.org/10.1038/s41597-023-02801-z

openmit-licenseSep 2023View details →

Semantic annotation of a part of the Italian Copyright Legislation

The dataset is a structured JSONL file focusing on copyright law. Each entry contains key fields that annotate legal texts, mainly in Italian. These fields include: 1. ID: A unique numerical identifier. 2. Text: Contains the actual legal provisions. 3. Chapter ID & Heading: Identifiers and titles for chapters, categorizing the legal text. 4. Article and Paragraph ID: Further break down of the text into articles and paragraphs. 5. Insertions: Highlights inserted text fragments in the legal text. 6. References: Cites external references with URLs and descriptions. 7. Entities: Labels sections of the text, identifying their beginning and ending offsets. 8. Relations: Intended to describe relationships between entities, although this field is empty in the sample. 9. Comments: A field for comments, also empty in the sample.

opencc-by-4.0Sep 2023View details →

Gold standard annotation of epilepsy clinic letters for the development of information extraction tools

This folder contains 200 synthetic letters, based on hospital outpatient epilepsy clinic consultations, written by neurology consultants, specialist trainees, and epilepsy specialist nurses. The letters were double annotated by trained researchers, according to annotation guidelines, uploaded separately (What and How of annotating with Markup). The 200 .ann annotation files are also included. We used Markup (https://getmarkup.com) for annotation (the configuration file is within the uploaded set of annotations) with an epilepsy concept list based on the Unified Medical Language System (UMLS) ontology. All annotations were compared, reviewed, and corrected to form a gold standard annotation set.

openmit-licenseOct 2023View details →

Subset of 'MLSUM: The Multilingual Summarization Corpus' for constraints annotation experiment

[EN] Subset of 'MLSUM: The Multilingual Summarization Corpus' for constraints annotation experiment. <ul> <li>Description: MLSUM is a dataset of newspappers articles aimed at training summaring model. We use it for a constraints annotation experiment on newspapper titles according to their topic classification.</li> <li>Content: For constraints annotation experiment based on data similarity, this dataset have been subsetted (randomly pick 75 articles in the following 14 most used topics: 'economie', 'politique', 'sport', 'planete' (renamed in 'ecologie'), 'sciences', 'police-justice', 'disparitions', 'emploi', 'sante', 'musiques', 'arts', 'educations', 'climat' (renamed in 'meteo'), 'immobilier') and filtered (keep articles that have an obvious topics regarding their titles, without their bodies). Two reviewers have working on this task in order to limit the subjectivity of the filtering. This subsetted dataset is used (1) to estimate needed time to annotate titles similarity with constraints (MUST-LINK, CANNOT-LINK) and (2) to test interactive clustering methodology (constraints annotation and constrained clustering).</li> <li>Origin: The dataset is bassed on the original 'MLSUM: The Multilingual Summarization Corpus' dataset (https://doi.org/10.48550/arXiv.2004.14900).</li> </ul> [FR] Echantillon de 'MLSUM: The Multilingual Summarization Corpus' pour une expérience d'annotation de contraintes. <ul> <li>Description : MLSUM est un ensemble de données d'articles de journaux destinés à l'entraînement d'un modèle de résumé automatique. Nous l'utilisons pour une expérience d'annotation de contraintes sur des titres de journaux en fonction de leur classification thématique.</li> <li>Contenu : Pour une expérience d'annotation de contraintes basée sur la similarité des données, cet ensemble de données a été échantillonné (sélectionner au hasard de 75 articles dans les 14 sujets les plus utilisés : 'économie', 'politique', 'sport', 'planète' (renommé en « écologie »). ), 'sciences', 'police-justice', 'disparitions', 'emploi', 'sante', 'musiques', 'arts', 'éducations', 'climat' (renommé en 'meteo'), 'immobilier' ) et filtré (conserver les articles qui ont un sujet évident par rapport à leur titre, sans leur corps). Deux relecteurs ont travaillé sur cette tâche afin de limiter la subjectivité du filtrage. Ce sous-ensemble de données est utilisé (1) pour estimer le temps nécessaire pour annoter la similarité des titres avec des contraintes (MUST-LINK, CANNOT-LINK) et (2) pour tester la méthodologie de clustering interactif (annotation de contraintes et clustering contraint).</li> <li>Origine : L'ensemble de données est basé sur l'ensemble de données original 'MLSUM : The Multilingual Summarization Corpus' (https://doi.org/10.48550/arXiv.2004.1490).</li> </ul>

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Annotated Web Tables

Data sets used for experimental evaluation in the related publication: Matching Web Tables with Knowledge Base Entities: From Entity Lookups to Entity Embeddings International Semantic Web Conference (1) 2017: 260-277 Vasilis Efthymiou Oktie Hassanzadeh Mariano Rodríguez-Muro Vassilis Christophides The gold standard data sets are collections of web tables: T2D (v1) consists of a schema-level gold standard of 1,748 Web tables, manually annotated with class- and property-mappings, as well as an entity-level gold standard of 233 Web tables. Limaye consists of 400 manually annotated Web tables with entity-, class-, and property-level correspondences, where single cells (not rows) are mapped to entities. The corrected version of this gold standard is adapted to annotate rows with entities, from the annotations of the label column cells. WikipediaGS is an instance-level gold standard developed from 485K Wikipedia tables, in which links in the label column are used to infer the annotation of a row to a DBpedia entity.   Note on license: please refer to the README.txt. Data is derived from Wikipedia and other sources may have different licenses. Wikipedia contents can be shared under the terms of Creative Commons Attribution-ShareAlike License as outlined on Wikipedia: <a href="https://en.wikipedia.org/wiki/Wikipedia:Reusing_Wikipedia_content">https://en.wikipedia.org/wiki/Wikipedia:Reusing_Wikipedia_content</a> The correspondences of the T2D Gold standard is provided under the terms of the Apache license. The Web tables are provided according the same terms of use, disclaimer of warranties and limitation of liabilities that apply to the Common Crawl corpus. The DBpedia subset is licensed under the terms of the Creative Commons Attribution-ShareAlike License and the GNU Free Documentation License that applies to DBpedia. Limaye gold standard is downloaded from: <a href="http://websail-fe.cs.northwestern.edu/TabEL/">http://websail-fe.cs.northwestern.edu/TabEL/</a> (download date: August 25, 2016). Please refer to the original website and the following paper for more details and citation information: G. Limaye, S. Sarawagi, and S. Chakrabarti. Annotating and Searching Web Tables Using Entities, Types and Relationships. PVLDB, 3(1):1338â€“1347, 2010. THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

opencc-by-4.0May 2019View details →

edi44/100

Invertebrates of the Andrews Experimental Forest: An annotated list of insects and other arthropods, 1971 to 2002

This publication is not a pro forma species list; rather, it has been generated as the result of diverse ecological studies centered on and around the Andrews Forest beginning in 1971. No attempt has been made to exhaustively collect the area with methodologies appropriate to each invertebrate group. This list provides some insight into the enormous invertebrate diversity present in the coniferous forests of the Pacific Northwest. It provides reference material for investigators who might be engaged in ecological investigations. We hope that these data, set in an ecological context, will stimulate collaboration and facilitate the design of future research.

openCustomDec 2013View details →

edi44/100

MCR LTER: Coral Reef: Computer Vision: Multi-annotator Comparison of Coral Photo Quadrat Analysis

This repository contains the Moorea portion of a larger data package published in conjuncture with: "Towards automated annotation of benthic survey images: variability of human experts and operational modes of automation", Beijbom et al. PLOS One, 2015. The rest of the data package is hosted at the Dryad data repository (doi:10.5061/dryad.m5pr3). The larger data package is an aggregate dataset from four Pacific coral reef monitoring projects in: Moorea (French Polynesia), the northern Line Islands, Nanwan Bay (Taiwan) and Heron Reef (Australia). It contains 5090 coral reef survey images, and 251,988 random-point annotations by coral ecology experts. The point-annotations indicate the dominant benthic substrate at 10 to 200 random point locations per image, using a label-set of 20 categories. In addition, 200 images from each location have been cross-annotated by 6 experts, for a total of 7 sets of annotations for each image. This set of cross-annotations can be used to contextualize the performance of automated annotation methods for coral reef ecology. The full data package can also be used by computer-vision and machine learning researchers to develop object classification, image segmentation, and domain transfer learning methods. These data contain a subset of the raw data from which dataset knb-lter-mcr.4 is derived.

openCustomApr 2015View details →

Fig. 26. a. Farlowella hahni, NUP 374, 130.0 in Peixes da planície de inundação do alto rio Paraná e áreas adjacentes: revised, annotated and updated

Fig. 26. a. Farlowella hahni, NUP 374, 130.0 mm SL, ribeirão São Pedro, São Pedro do Paraná, State of Paraná. b. Loricaria prolixa, 230.5 mm SL, uncat. c. Loricaria sp., NUP 2567, 230.5 mm SL, rio Paraná, Porto Rico, State of Paraná. d. Loricariichthys platymetopon, 210.0 mm SL, uncat. e. Loricariichthys rostratus, 250.0 mm SL, uncat. f. Rineloricaria sp., NUP 2565, 86.0 mm SL, riacho na Estrada (D. Armando), Santa Helena, State of Paraná.

Fig. 21. All from Brazil. a. Cetopsorhamdia iheringi, NUP 14875, 51.1 in Peixes da planície de inundação do alto rio Paraná e áreas adjacentes: revised, annotated and updated

Fig. 21. All from Brazil. a. Cetopsorhamdia iheringi, NUP 14875, 51.1 mm SL, rio Curimba, Umuarama, State of Paraná. b. Heptapterus mustelinus, NUP 2500, 118.8 mm SL, Itaipu Reservoir, Santa Helena, State of Paraná. c. Imparfinis borodini, NUP 3028, 128.3 mm SL, rio Mourão, tributary of the rio Ivaí, Campo Mourão, State of Paraná. d. Imparfinis mirini, NUP 87, 64.4 mm SL, rio Alambari, Botucatu, State of São Paulo. e. Imparfinis schubarti, NUP 11369, 99.5 mm SL, rio Pitangui (cachoeira do Leajenski), Ponta Grossa, State of Paraná. f. Phenacorhamdia tenebrosa, NUP 4949, 73.4 mm SL, ribeirão Maringá, Maringá, State of Paraná.

Fig. 35. a. Geophagus brasiliensis, 148.0 in Peixes da planície de inundação do alto rio Paraná e áreas adjacentes: revised, annotated and updated

Fig. 35. a. Geophagus brasiliensis, 148.0 mm SL, fresh specimen, uncat. b. Geophagus sveni, NUP 18698, 115.0 mm SL, rio Ivinheima, Naviraí, State of Mato Grosso do Sul. c. Gymnogeophagus setequedas, 81.3 mm SL, fresh specimen, uncat. d. Laetacara araguaiae, NUP 11315, 43.5 mm SL, upper rio Sucuriú, Paraíso, State of Mato Grosso do Sul. e. Oreochromis niloticus, NUP 1132, 185.0 mm SL, Corumbá Reservoir, Caldas Novas, State of Goiás. d. Satanoperca sp., 150.0 mm SL, fresh specimen, uncat.

Fig. 17. a. Brachyhypopomus gauderio, NUP 2510, 112.2 in Peixes da planície de inundação do alto rio Paraná e áreas adjacentes: revised, annotated and updated

Fig. 17. a. Brachyhypopomus gauderio, NUP 2510, 112.2, lagoa do Aurélio, Taquarussu, State of Mato Grosso do Sul. b. Gymnorhamphichthys britskii, NUP 3337, 115.0 mm TL, rio Baía, Taquarussu, State of Mato Grosso do Sul. c. Rhamphichthys hahni, 567.3 mm TL, uncat. d. Eigenmannia guairaca, NUP 16151, 112.3 mm TL, córrego Água Boa, tributary of the rio Iguatemi, Mundo Novo, Mato Grosso do Sul State. e. Eigenmannia trilineata, 109.2 mm TL, fresh specimen, uncat. f. Eigenmannia virescens, 205.0 mm TL, uncat. g. Sternopygus macrurus, NUP 2096, 280.0 mm TL, rio Paraná, Porto Rico, State of Paraná.