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Figure 6. - ATruncatoflabellummortenseni, USNM 97522, paratype, Philippines B Truncatoflabellumaustraliensis, paratype (including anthocaulus), USNM 96652, Western Australia C Truncatoflabellumcandeanum, neotype, including anthocaulus, USNM 81963, Albatross 5369, Philippines D Truncatoflabellumcompressum, upper figure, illustration of type from Lesson (1827); other views from Challenger 190, BM 1880.11.25.78. Scale bars: all 10 mm.
Figure 6. - ATruncatoflabellummortenseni, USNM 97522, paratype, Philippines B Truncatoflabellumaustraliensis, paratype (including anthocaulus), USNM 96652, Western Australia C Truncatoflabellumcandeanum, neotype, including anthocaulus, USNM 81963, Albatross 5369, Philippines D Truncatoflabellumcompressum, upper figure, illustration of type from Lesson (1827); other views from Challenger 190, BM 1880.11.25.78. Scale bars: all 10 mm.
Tractography Challenge ISMRM 2015 Data
<p>The ground truth of this tractography validation data set was defined based on the fiber bundle geometry of a high-quality Human Connectome Project (HCP) dataset, constructed from multiple whole-brain global tractography maps. An expert radiologist extracted 25 major tracts (i.e., bundles of streamlines) from the tractogram. These association, projection and commissural fibers covered more than 70% of the white matter across the whole brain. The dataset features a brain-like macro-structure of long-range connections, mimicking <em>in vivo</em> DWI clinical-like acquisitions based on a simulated diffusion signal. An additional anatomical image with T1-like contrast was simulated as a reference. The dataset contains all files needed to perform the simulation.</p>
Assessment of endothelial cell function and physiological microcirculatory reserve by video microscopy using a topical acetylcholine and nitroglycerin challenge - Individual subject data
<p>This dataset contains individual subject data for microcirculatory parameters assessed in the present study. Macrocirculatory parameters are not included at this time due to the present study being part of a larger, partially unpublished project.</p>
Tractography Challenge ISMRM 2015 Videos
<p>These videos supplement the Tractography Challenge ISMRM 2015 </p>
Pairwise Learning using Unsupervised Bottleneck Features for Zero-Resource Speech Challenge 2017 (System 1)
<p>The system is for track1 alone. We trained an antoencoder using unsupervised bottleneck features with word-pair information from Switchboard. The unsupervised bottleneck features was extracted from an extractor of multi-task learning deep neural networks (MTL-DNN). The word-pair information was the ground truth from Switchboard. The final features are obtained from the third layer in our pairwise trained autoencoder.</p>
Pairwise Learning using Unsupervised Bottleneck Features for Zero-Resource Speech Challenge 2017 (System 3)
<p>The system is for track1 alone. We trained an antoencoder using unsupervised bottleneck features with word-pair information from unsupervised term detection (UTD) on all corpora of five languages. The unsupervised bottleneck features was extracted from an extractor of multi-task learning deep neural networks (MTL-DNN). The word-pair was found by UTD. The UTD process was built on ZRTools. The final features are obtained from the third layer in our pairwise trained autoencoder.</p>
Pairwise Learning using Unsupervised Bottleneck Features for Zero-Resource Speech Challenge 2017 (System 2)
<p>The system is for track1 alone. We trained an antoencoder using unsupervised bottleneck features with word-pair information from unsupervised term detection (UTD) only on the give ENGLISH corpus. The unsupervised bottleneck features was extracted from an extractor of multi-task learning deep neural networks (MTL-DNN). The word-pair was found by UTD. The UTD process was built on ZRTools. The final features are obtained from the third layer in our pairwise trained autoencoder.</p>
Tractography Challenge ISMRM 2015 Submissions
<p>Submissions to the open international ISMRM 2015 tractography challenge. 20 research groups with extensive expertise in diffusion imaging from 12 countries participated in the competition and submitted a total of 96 tractograms generated using a large variety of tractography pipelines with different pre-processing, local reconstruction, tractography and post-processing algorithms.</p> <p>Each file represents an individual submission. The mapping from file name to team can be inferred from the listing in the paper, as seen in the references.</p>
The ULS23 Challenge Public Training Dataset
<p>This dataset contains part of the imaging data for the <a href="https://uls23.grand-challenge.org/">Universal Lesion Segmentation Challenge (ULS23).</a> It contains the lesion volumes-of-interest (VOI's) for the novel annotated data of this challenge. It consists of 750 lesions from the DeepLesion dataset, 744 bone and 124 pancreas lesions from the Radboudumc. Pancreas data was collected by Natália Alves, Megan Schuurmans and Henkjan Huisman. The annotations are made available through the <a href="https://github.com/MJJdG/ULS23">Challenge repository on GitHub</a>.<br> </p><p>The full training dataset:</p><ul><li><a href="https://zenodo.org/records/10050960">Part 2</a>: kits21, LIDC-IDRI, LiTS</li><li><a href="https://zenodo.org/records/10054306">Part 3</a>: MDSC task 6/7/10, NIH-LN, CCC18</li><li><a href="https://zenodo.org/records/10054702">Part 4</a>: DeepLesion</li><li><a href="https://zenodo.org/records/10057471">Part 5</a>: DeepLesion</li><li><a href="https://zenodo.org/records/10056235">Part 6</a>: DeepLesion</li></ul><p><br>The Universal Lesion Segmentation 2023 (ULS23) data is licensed under CC BY-NC-SA 4.0 <br> </p>
PheKnowLator Builds -- CERLIB Challenge
<p><strong>PHENOTYPE KNOWLEDGE TRANSLATOR (PHEKNOWLATOR)</strong></p><p><i><strong>2021 Continuous Evaluation of Relational Learning in Biomedicine (CERLIB)</strong></i></p><ul><li>Website: <a href="https://github.com/callahantiff/PheKnowLator/wiki">https://github.com/callahantiff/PheKnowLator/wiki</a></li><li>Preprint: <a href="https://doi.org/10.48550/arXiv.2307.05727">https://doi.org/10.48550/arXiv.2307.05727</a></li></ul><p> </p><p><strong>OVERVIEW</strong></p><ul><li>Introduction </li><li>Knowledge Graph Builds</li><li>Challenge Data</li><li>Challenge Relations</li><li>Updates</li></ul><p> </p><p> </p><p><strong>INTRODUCTION</strong></p><p>PheKnowLator (Phenotype Knowledge Translator), is a Python 3 library that constructs semantically-rich, large-scale biomedical knowledge graphs under different semantic models. PheKnowLator is also a data sharing hub, providing downloadable versions of prebuilt knowledge graphs. For this challenge, the PheKnowLator knowledge graphs have been designed to model mechanisms of human disease and were built using 12 open biomedical ontologies, 24 linked open datasets, and results from two large-scale, experimentally-derived datasets. For additional information see the associated GitHub website: <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2.0.0">https://github.com/callahantiff/PheKnowLator/wiki/v2.0.0</a>. For a visual representation of the resources used (and their relationships) in the PheKnowLator knowledge graphs, click the link below.</p><ul><li>Knowledge Representation: <a href="https://user-images.githubusercontent.com/8030363/103718875-ffc04480-4f84-11eb-8852-65e557ba772b.jpg">https://user-images.githubusercontent.com/8030363/103718875-ffc04480-4f84-11eb-8852-65e557ba772b.jpg</a></li></ul><p> </p><p> </p><p><strong>KNOWLEDGE GRAPH BUILDS</strong></p><p>PheKnowLator was designed to generate knowledge graphs under different semantic models and to provide users with complete flexibility throughout the construction process. At its core, PheKnowLator is built on a core set of Open Biomedical Ontologies (OBOs), which are extended with external data sources by utilizing different knowledge models. The software allows users the flexibility to customize the following parameters:</p><ol><li><strong>Construction Approach:</strong> The semantic model utilized when integrating ontology and non-ontology data. The two available models are <i>instance</i> and <i>subclass</i> (details here: <a href="https://bit.ly/3p0ZNgg">https://bit.ly/3p0ZNgg</a>). We are providing an instance-based build for the challenge.</li><li><strong>Relations: </strong>A single relation can be added (<i>relations_only</i>) or each relation and its inverse can be added (<i>inverse_relations</i>). We are providing a knowledge graph built with inverse relations for the challenge.</li><li><strong>OWL Decoding: </strong>An OWL-decoded version of the full semantic knowledge graph. The method that we currently provide is called OWL-NETS (details here: <a href="https://bit.ly/35XCP2g">https://bit.ly/35XCP2g</a>), which decodes all triples needed to support OWL expressivity, but which alone are not biologically meaningful.</li></ol><p> </p><p> </p><p><strong>CHALLENGE DATA</strong></p><p>With this information in mind, the Google Cloud Storage Bucket includes the data files listed below. Additional information for each file type can be found here: <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2.0.0#knowledge-graph-output">https://github.com/callahantiff/PheKnowLator/wiki/v2.0.0#knowledge-graph-output</a>.</p><ul><li>Data for all builds can be found here: <a href="https://zenodo.org/record/8173020">https://zenodo.org/record/8173020.</a></li></ul><p> </p><p><strong>Knowledge Graph Data</strong></p><ul><li>PheKnowLator_v2.*.*_full_instance_inverseRelations_noOWL_OWLNETS.nt</li><li>PheKnowLator_v2.*.*_full_instance_inverseRelations_noOWL_OWLNETS_NetworkxMultiDiGraph.gpickle</li></ul><p><strong>Edge Lists</strong></p><ul><li>PheKnowLator_v2.*.*_full_instance_inverseRelations_noOWL_Triples_Identifiers.txt</li><li>PheKnowLator_v2.*.*_full_instance_inverseRelations_noOWL_Triples_Integers.txt</li><li>PheKnowLator_v2.*.*_full_instance_inverseRelations_noOWL_Triples_Integer_Identifier_Map.json</li></ul><p><strong>Metadata</strong></p><ul><li>Node and Relation Metadata including labels, synonyms, and definitions. Additional information can be found here: <ul><li>PheKnowLator_v2.*.*_full_instance_inverseRelations_noOWL_NodeLabels.txt</li><li>node_metadata_dict.pkl</li></ul></li></ul><p> </p><p> </p><p><strong>CHALLENGE RELATIONS</strong></p><p>We will evaluate predictions on 15 Relation Ontology (RO) relations utilized in 34 distinct edge types. Additional details on these edge types can be found here: <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2.0.0#edge-data">https://github.com/callahantiff/PheKnowLator/wiki/v2.0.0#edge-data</a>. The 15 RO relations and their associated edge types are shown in the table below.</p><ul><li><a href="http://purl.obolibrary.org/obo/RO_0000056">Participates in</a>: chemical-pathway; gene-pathway; protein-gobp; protein-pathway</li><li><a href="http://purl.obolibrary.org/obo/RO_0000085">Has function</a>: pathway-gomf; protein-gomf </li><li><a href="http://purl.obolibrary.org/obo/RO_0001025">Located in: </a>protein-anatomy; protein-cell; protein-gocc; rna-anatomy; rna-cell</li><li><a href="http://purl.obolibrary.org/obo/RO_0002180">Has component</a>: pathway-gocc</li><li><a href="http://purl.obolibrary.org/obo/RO_0002200">Has phenotype</a>: disease-phenotype</li><li><a href="http://purl.obolibrary.org/obo/RO_0002205">Has gene product</a>: gene-protein</li><li><a href="http://purl.obolibrary.org/obo/RO_0002434">Interacts with</a>: chemical-gene; chemical-protein</li><li><a href="http://purl.obolibrary.org/obo/RO_0002435">Genetically interacts with</a>: gene-gene</li><li><a href="http://purl.obolibrary.org/obo/RO_0002436">Molecularly interacts with</a>: chemical-gobp; chemical-gocc; chemical-gomf; protein-catalyst; protein-cofactor; protein-protein</li><li><a href="http://purl.obolibrary.org/obo/RO_0002511">Transcribed to</a>: gene-rna</li><li><a href="http://purl.obolibrary.org/obo/RO_0002513">Ribosomally translates to</a>: rna-protein</li><li><a href="http://purl.obolibrary.org/obo/RO_0002566">Causally influences</a>: variant-gene</li><li><a href="http://purl.obolibrary.org/obo/RO_0002606">Is substance that treats</a>: chemical-disease; chemical-phenotype</li><li><a href="http://purl.obolibrary.org/obo/RO_0003302">Causes or contributes to condition</a>: gene-disease; gene-phenotype; variant-disease; variant-phenotype</li><li><a href="http://purl.obolibrary.org/obo/RO_0009501">Realized in response to</a>: gobp-pathway</li></ul><p> </p><p> </p><p><strong>BUILD UPDATES</strong></p><p>Below we note important updates to each build. For additional information on each build please see the project Wiki (<a href="https://github.com/callahantiff/PheKnowLator/wiki/v2.0.0">https://github.com/callahantiff/PheKnowLator/wiki/v2.0.0</a>) and for more information on the data sources that are used for each build see: <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources">https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources</a>.</p><p> </p><p><strong>JANUARY 2021</strong></p><ul><li> No data found for the pathway-gomf edge type, which was identified as being due to a change in the input data file, which is downloaded from Reactome</li></ul><p><strong>APRIL 2021</strong></p><ul><li>Significant updates made to the workflow for building the graphs. This should have only a marginal impact on the resulting the knowledge graphs</li><li>Changes made to the edge types from the table above. We are no longer supporting `chemical-rna` edge types</li><li>The filtering applied to the input data when constructing the edges was updated to reduce potential variance in the quality of the resulting edges. Please see the descriptions for each data source (here: <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources">https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources</a>) for addition information</li></ul><p><strong>MAY 2021</strong></p><ul><li>pathway-gomf edges are back, Reactome appears to have resolved the errors we discovered in January 2021</li><li>disease-phenotype edge data may change slightly as the HPO changed the phenotype annotation file and associated formatting</li><li>gene-gene edge data count has drastically decreased. The cause has been identified as changes that GeneMania has made to their data (change to origin data noted as 04/27/2021)</li></ul><p><strong>JUNE 2021</strong></p><ul><li> Build successful. No issues to report </li></ul><p><strong>JULY 2021 </strong></p><ul><li> Build successful. No issues to report </li></ul><p><strong>AUGUST 2021 </strong></p><ul><li> Build successful. No issues to report </li></ul><p><strong>SEPTEMBER 2021 </strong></p><ul><li> Build successful. No issues to report</li></ul><p><strong>OCTOBER 2021 </strong></p><ul><li>Build successful. No issues to report. </li><li>Note that some changes were made to the OWL-NETS algorithm, which may result in larger graphs than prior builds. Details on these changes can be found here: https://github.com/callahantiff/PheKnowLator/pull/109 </li><li>The following issues (<a href="https://github.com/callahantiff/PheKnowLator/issues/116;">https://github.com/callahantiff/PheKnowLator/issues/116;</a> <a href="https://github.com/callahantiff/PheKnowLator/issues/118">https://github.com/callahantiff/PheKnowLator/issues/118</a>) identified significant bugs, which needed immediate repair. As a result a new October build (v3.0.2) was generated on 10/19/21. </li></ul><p><strong>NOVEMBER 2021</strong></p><ul><li> Build successful. No issues to report</li></ul>
Correlation challenges for North Sea offshore wind power: a Norwegian case study
<p>Dataset and results from this study: Hjelmeland, M., & Nøland, J. K. (2023). Correlation challenges for North Sea offshore wind power: a Norwegian case study. Scientific Reports 2023 13:1, 13(1), 1–17. <a href="https://doi.org/10.1038/s41598-023-45829-2">https://doi.org/10.1038/s41598-023-45829-2</a></p><p>The git hub repo with source code can be found here: <a href="https://github.com/martinhjel/wind-covariation">https://github.com/martinhjel/wind-covariation</a></p>
Educator Perceptions of DevOps Teaching Recommendations and Their Alignment with Common Challenges
<p>DevOps education presents unique pedagogical challenges due to the diversity of tools, rapid technological change, and the multidisciplinary nature of the field. Although previous work has proposed recommendations to address these challenges, it is unclear how educators perceive these recommendations and whether they align with the challenges encountered in practice. In this paper, we present a mixed methods study involving 11 DevOps educators who interacted with Improve, a tool that presents a curated set of educational challenges and recommendations derived from previous literature. Educators indicated which recommendations they already use, which they intend to use, and which challenges they experience and are motivated to address. Our findings show that 22.6% of the recommendations were new to educators and considered potentially useful, while 59.2% were already in use. Additionally, 66.3% of the challenges were considered relevant, with most of them having linked recommendations that educators were already using or willing to adopt. This study provides empirical insights into the perceived usefulness of existing recommendations, identifies gaps in challenge-recommendation mappings, and supports future efforts to design and disseminate more targeted educational guidance for DevOps teaching.</p>
"Ron de Oro" - Treasure Chest Challenge
The great treasure "El Ron de Oro" A rum that can only be obtained from a snake called Cannae Devorantem. Its poison is the most feared, since with its smell it can lead to madness, but every 100 years it undergoes a great transformation into solid gold from the scales to every vein in its body. That day the snake expels its venom, but instead of madness it delivers the Golden Rum, whoever ingests it will enjoy true happiness for the rest of their life. The snake was locked in a barrel after driving an entire town mad, but the temptation of those who captured it by the power of its venom led them to lock it up with enough food to survive for 100 years, hoping to retain its power. It was passed from generation to generation with a ritual that allowed it to be opened every 100 years to collect its poison and deliver food for its awakening. Even today the stories of people who lose their sanity are still heard ... the only thing they remember is a wooden barrel and a whistle inside... Tttsssssssss Source: Objaverse 1.0 / Sketchfab
Waste Bin - Household Props Challenge - Day 24
Day 24 of the household props challenge- an outdoor waste bin. Constraints: * 300 Tris * Albedo and AO maps only * 256x256 texture resolution Source: Objaverse 1.0 / Sketchfab
Challenger 1 [photogrammatry scan from YT video]
This 3D reconstruction generated with photogrammetry software 3DF Zephyr v4.513 processing 256 images which where taken from a YouTube video not filmed optimally for photogrammatry scanning. Source: Objaverse 1.0 / Sketchfab
Samurai Katana - 24 Hours Challenge [FREE]
The second model that I've made for the highpoly realistic models 24hrs challenge. Where I make as many highpoly models as possible in 24 Hours! And I give away these models for free! :) Source: Objaverse 1.0 / Sketchfab
Addressing the Challenge of Wildlife Conservation in Urban Landscapes by Increasing Human Tolerance for Wildlife
<p>Data that supports the journal manuscript: Puri, M., Johannsen, K.L., Goode, K.O., & Pienaar, E.F. (2024) Addressing the challenge of wildlife conservation in urban landscapes by increasing human tolerance for wildlife. <em>People and Nature.</em></p>
''Do you have time for a quick call?": Exploring Remote and Hybrid Requirements Engineering Practices and Challenges in Industry
<p>This replication package contains the survey and interview questions, list of codes derived from the analysis, and the survey respondent demographics from the paper titled <em><strong>''Do you have time for a quick call?": Exploring Remote and Hybrid Requirements Engineering Practices and Challenges in Industry </strong></em>accepted to International Requirements Engineering Conference 2024. </p>
Paper is not enough: Crowdsourcing the T<sub>1</sub> mapping common ground via the ISMRM reproducibility challenge
Dataset provided for NeuroLibre preprint. Author repo: https://www.github.com/rrsg2020/note NeuroLibre fork:https://github.com/roboneurolibre/note <p>For details, please visit the corresponding <a href="https://github.com/neurolibre/neurolibre-reviews/issues/23">NeuroLibre technical screening.</a></p> <p><strong><a href="https://neurolibre.org" target="NeuroLibre">https://neurolibre.org</a></strong></p>
A multi-omics systems vaccinology resource to develop and test computational models of immunity: 1st challenge dataset and submissions
<p>The goal of the CMI-PB prediction contest is to foster a collaborative research community that can collectively tackle challenges and accelerates scientific progress beyond the capabilities of individual researchers or groups. The CMI-PB consortium has curated multi-source data from multiple individuals, encompassing Ab titers (around four antibodies/features), cell frequency (approximately 20 cell types/features), gene expression (roughly 50,000 RNA transcripts/features), and plasma proteomics (around 50 proteins/features). The challenge requires integrating these diverse data sources to predict different immune responses or tasks. Specifically, you will utilize multi-source data from several individuals on day 0 (baseline) to predict specific immune responses at later time points (1, 3, 7, and 14 days post-booster vaccination).</p> <p>The first CMI-PB challenge, which is an internal challenge, was conducted using datasets from 2020 (train) and 2021 (test). In the following sections, we provide detailed information on the datasets, challenge tasks, submission format, descriptions, and access to the necessary data files for participants to develop their models and make predictions.</p> <p><br><strong>A) Multiomics CMI-PB dataset:</strong></p> <p>We propose a study design that enables a systems-level understanding of the immune responses through computational modeling. Our cohort comprises aP vs. wP infancy-primed subjects boosted with Tdap. We recruit individuals born before 1995 (wP) and after 1996 (aP), collect baseline plasma and blood samples, and then at 1, 3, 7 and 14 days post booster vaccination.</p> <p>With the obtained samples processed, we generated omics data by:</p> <ul> <li> <p>Bulk PBMCs transcriptomics,</p> </li> <li> <p>Plasma proteomics using Olink, which provides a quantitative readout of cytokines, chemokines, and other immune factors,</p> </li> <li> <p>Cell frequency in PBMCs using flow cytometry,</p> </li> <li> <p>Tdap-specific antibodies levels</p> </li> </ul> <p><strong>B) List of tasks can be accessed using the “List of tasks for challenge 1.docx” file, and submissions need to submit in provided format here: “submission template challenge 1.tsv”</strong></p> <p><strong>C) Datasets for model building and making predictions:</strong></p> <p> Data files are divided into two categories: 1) raw dataset and 2) computable matrices.</p> <ol> <li> <p><strong>Raw dataset: </strong>This raw-most dataset is divided into training and test datasets. </p> </li> <li> <p><strong>Computable matrices: </strong>There are three different types of computable matrices. a) Full: These files are generated by dividing raw files into sub-files specific to planned days specific to vaccination. b) harmonized: These are generated by preserving only overlapping features between train and test datasets. b) imputed: MICE imputation is performed to impute missing values in the dataset.</p> </li> </ol> <p><strong>D) Submission evaluation</strong></p> <p>This folder contains all submitted models with ranking files and code for evaluating these models.</p> <p><strong>To learn more about the CMI-PB prediction challenge, visit our website at www.cmi-pb.org.</strong></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.