cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc295y5m
title: CCDC 2120870: Experimental Crystal Structure Determination

description: Related Article: Shuai Yuan, Jiayu Peng, Bin Cai, Zhehao Huang, Angel T. Garcia-Esparza, Dimosthenis Sokaras, Yirui Zhang, Livia Giordano, Karthik Akkiraju, Yun Guang Zhu, René Hübner, Xiaodong Zou, Yuriy Román-Leshkov, Yang Shao-Horn|2022|Nat.Mater|21|673|doi:10.1038/s41563-022-01199-0
description: Related Article: Shuai Yuan|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.6615348
title: Isoetes engelmannii decontaminated gx

description: .
DOI: 10.5281/zenodo.6615347
title: Isoetes engelmannii decontaminated gx

description: .
DOI: 10.6084/m9.figshare.14390573
title: XL-MS and Native MS data for "Chaperoning of the histone octamer by the acidic domain of DNA repair factor APLF"

description: This dataset includes raw data and the associated output of cross-linking mass spectrometry experiments and native mass spectrometry data. The results were included in the manuscript "<strong>Chaperoning of the histone octamer by the acidic domain of DNA repair factor APLF"</strong>
DOI: 10.6084/m9.figshare.14390573.v1
title: XL-MS and Native MS data for "Chaperoning of the histone octamer by the acidic domain of DNA repair factor APLF"

description: This dataset includes raw data and the associated output of cross-linking mass spectrometry experiments and native mass spectrometry data. The results were included in the manuscript "<strong>Chaperoning of the histone octamer by the acidic domain of DNA repair factor APLF"</strong>
DOI: 10.6084/m9.figshare.20001533
title: Technical Report.docx

description: Technical report of the paper A Systematic Mapping Study on Usability and User eXperience<br> Evaluation of Multi-Touch Systems. Contain extractions, list of papers returned and filtered and summary tables.
DOI: 10.6084/m9.figshare.20001533.v1
title: Technical Report.docx

description: Technical report of the paper A Systematic Mapping Study on Usability and User eXperience<br> Evaluation of Multi-Touch Systems. Contain extractions, list of papers returned and filtered and summary tables.
DOI: 10.5281/zenodo.6615420
title: command line interface practical - 2022 bioinformatics summer school

description: Files and folders for the comman line interface practical of the 2022 bioinformatics summer school. Second release
DOI: 10.5281/zenodo.6607767
title: command line interface practical - 2022 bioinformatics summer school

description: Files and folders for the comman line interface practical of the 2022 bioinformatics summer school. Second release
DOI: 10.6084/m9.figshare.20001536.v1
title: Technical report

description: Technical report of the paper A Systematic Mapping Study on Usability and User eXperience<br> Evaluation of Multi-Touch Systems. Contain extractions, summary tables and papers returned on SMS.
DOI: 10.6084/m9.figshare.19749949
title: Identifying Causes of Fracture Beyond Bone Mineral Density: Evidence from Human Genetics

description: Full Summary Statistics and Supplementary Tables ---------- Genomic structural equation models were built to investigate genetic effects on fracture that were mediated vs. not mediated by bone mineral density (BMD). ---------- Summary Statistics Frax-eBMD-related.gwas.txt.gz: genetic effects mediated by estimated BMD Frax-non-eBMD.gwas.txt.gz: genetic effects not mediated by estimated BMD Frax-FNBMD-related.gwas.txt.gz: genetic effects mediated by femoral neck BMD Frax-non-FNBMD.gwas.txt.gz: genetic effects not mediated by femoral neck BMD Frax-LSBMD-related.gwas.txt.gz: genetic effects mediated by lumbar spine BMD Frax-non-LSBMD.gwas.txt.gz: genetic effects not mediated by lumbar spine BMD ---------- Supplementary Tables Table S3. List of COJO-independent SNPs with BMD-related effects on fracture risk. Table S4. Statistical fine-mapping of loci conferring non-BMD effects. Table S5. GWAS signals identified by PhenoScanner for each COJO-independent SNP. Table S7. Parameter optimization for polygenic risk scores.
DOI: 10.5281/zenodo.6615440
title: Data for blog post on dfm.io: "An experiment in open science: exoplanet population inference"

description: The data set used be the blog post "An experiment in open science: exoplanet population inference" published at https://dfm.io/posts/exopop/
DOI: 10.5281/zenodo.6615439
title: Data for blog post on dfm.io: "An experiment in open science: exoplanet population inference"

description: The data set used be the blog post "An experiment in open science: exoplanet population inference" published at https://dfm.io/posts/exopop/
DOI: 10.5281/zenodo.6615455
title: PASS: An ImageNet replacement for self-supervised pretraining without humans

description: Computer vision has long relied on ImageNet and other large datasets of images sampled from the Internet for pretraining models. However, these datasets have ethical and technical shortcomings, such as containing personal information taken without consent, unclear license usage, biases, and, in some cases, even problematic image content. On the other hand, state-of-the-art pretraining is nowadays obtained with unsupervised methods, meaning that labelled datasets such as ImageNet may not be necessary, or perhaps not even optimal, for model pretraining. We thus propose an unlabelled dataset <strong>PASS</strong>: Pictures without humAns for Self-Supervision. PASS only contains images with CC-BY license and complete attribution metadata, addressing the copyright issue. Most importantly, it contains no images of people at all, and also avoids other types of images that are problematic for data protection or ethics. We show that PASS can be used for pretraining with methods such as MoCo-v2, SwAV and DINO. In the transfer learning setting, it yields similar downstream performances to ImageNet pretraining even on tasks that involve humans, such as human pose estimation. PASS does not make existing datasets obsolete, as for instance it is insufficient for benchmarking. However, it shows that model pretraining is often possible while using safer data, and it also provides the basis for a more robust evaluation of pretraining methods.<br> <br> A simple download script is here: https://github.com/yukimasano/PASS/blob/main/download.sh<br> Visit our webpage here: https://www.robots.ox.ac.uk/~vgg/research/pass/
description: compared to v2.0 we have removed further 131 images that contained humans. See details here: https://github.com/yukimasano/PASS/blob/main/version_history.txt
DOI: 10.5281/zenodo.5267181
title: PASS: An ImageNet replacement for self-supervised pretraining without humans

description: Computer vision has long relied on ImageNet and other large datasets of images sampled from the Internet for pretraining models. However, these datasets have ethical and technical shortcomings, such as containing personal information taken without consent, unclear license usage, biases, and, in some cases, even problematic image content. On the other hand, state-of-the-art pretraining is nowadays obtained with unsupervised methods, meaning that labelled datasets such as ImageNet may not be necessary, or perhaps not even optimal, for model pretraining. We thus propose an unlabelled dataset <strong>PASS</strong>: Pictures without humAns for Self-Supervision. PASS only contains images with CC-BY license and complete attribution metadata, addressing the copyright issue. Most importantly, it contains no images of people at all, and also avoids other types of images that are problematic for data protection or ethics. We show that PASS can be used for pretraining with methods such as MoCo-v2, SwAV and DINO. In the transfer learning setting, it yields similar downstream performances to ImageNet pretraining even on tasks that involve humans, such as human pose estimation. PASS does not make existing datasets obsolete, as for instance it is insufficient for benchmarking. However, it shows that model pretraining is often possible while using safer data, and it also provides the basis for a more robust evaluation of pretraining methods.<br> <br> A simple download script is here: https://github.com/yukimasano/PASS/blob/main/download.sh<br> Visit our webpage here: https://www.robots.ox.ac.uk/~vgg/research/pass/
description: compared to v2.0 we have removed further 131 images that contained humans. See details here: https://github.com/yukimasano/PASS/blob/main/version_history.txt
DOI: 10.6084/m9.figshare.3761562.v2240
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5281/zenodo.6615465
title: bosentan pediatrics

description: bosentan pediatrics
description: 8345 NW 66 ST #E2321
DOI: 10.5281/zenodo.6613875
title: Wikidata subset with revision history information [RDF]

description: This dataset is composed of 300 instances from the 100 most important classes in Wikidata, for a total of around 30000 entities and 390000 triples. The dataset is geared towards knowledge graph refinement models that leverage edit history information from the graph. There are two versions of the dataset: The <strong>static</strong> version (files postfixed with '_static') contains the simple statements of each entity fetched from Wikidata. The <strong>dynamic</strong> version (files postfixed with '_dynamic') contains information about the operations and revisions made to these entities, and the triples that were added or removed. Each version is split into three subsets: train, validation (val), and test. Each split contains every entity from the dataset. The train split contains the first 70% of revisions made to each entity, the validation split contains the 70% to 85% revisions, and the test set contains the last 15% revisions. This is a sample from the static datasets: <pre><code>wd:Q217432 a uo:entity ; wdt:P1082 1.005904e+06 ; wdt:P1296 "0052280" ; wdt:P1791 wd:Q18704103 ; wdt:P18 "Pitakwa.jpg" ; wdt:P244 "n80066826" ; wdt:P571 "+1912-00-00T00:00:00Z" ; wdt:P6766 "421180027" .</code></pre> Each entity has the type <em>uo:entity</em>, and contains the statements added during that time period following Wikidata's data model. In the following code snippet we show an example from the dynamic dataset: <pre><code>uo:rev703872813 a uo:revision ; uo:timestamp "2018-06-28T22:31:32Z" . uo:op703872813_0 a uo:operation ; uo:fromRevision uo:rev703872813 ; uo:newObject wd:Q82955 ; uo:opType uo:add ; uo:revProp wdt:P106 ; uo:revSubject wd:Q6097419 . uo:op703878666_0 a uo:operation ; uo:fromRevision uo:rev703878666 ; uo:opType uo:remove ; uo:prevObject wd:Q1108445 ; uo:revProp wdt:P460 ; uo:revSubject wd:Q1147883 .</code></pre> This dataset is composed of revisions, which have a timestamp. Each revision is composed of 1 to n operations, in which there is a change to a statement from the entity. There are two types of operations: <em>uo:add</em> and <em>uo:remove</em>. In both cases, the property and the subject being modified are shown with the <em>uo:revProp</em> and <em>uo:revSubject</em> properties. In the case of additions, <em>uo:newObject</em> and <em>uo:prevObject</em> properties are added to show the previous and new objects after the addition. In the case of removals, there is a <em>uo:prevObject </em>property to record the object that was removed.
DOI: 10.5281/zenodo.6613874
title: Wikidata subset with revision history information [RDF]

description: This dataset is composed of 300 instances from the 100 most important classes in Wikidata, for a total of around 30000 entities and 390000 triples. The dataset is geared towards knowledge graph refinement models that leverage edit history information from the graph. There are two versions of the dataset: The <strong>static</strong> version (files postfixed with '_static') contains the simple statements of each entity fetched from Wikidata. The <strong>dynamic</strong> version (files postfixed with '_dynamic') contains information about the operations and revisions made to these entities, and the triples that were added or removed. Each version is split into three subsets: train, validation (val), and test. Each split contains every entity from the dataset. The train split contains the first 70% of revisions made to each entity, the validation split contains the 70% to 85% revisions, and the test set contains the last 15% revisions. This is a sample from the static datasets: <pre><code>wd:Q217432 a uo:entity ; wdt:P1082 1.005904e+06 ; wdt:P1296 "0052280" ; wdt:P1791 wd:Q18704103 ; wdt:P18 "Pitakwa.jpg" ; wdt:P244 "n80066826" ; wdt:P571 "+1912-00-00T00:00:00Z" ; wdt:P6766 "421180027" .</code></pre> Each entity has the type <em>uo:entity</em>, and contains the statements added during that time period following Wikidata's data model. In the following code snippet we show an example from the dynamic dataset: <pre><code>uo:rev703872813 a uo:revision ; uo:timestamp "2018-06-28T22:31:32Z" . uo:op703872813_0 a uo:operation ; uo:fromRevision uo:rev703872813 ; uo:newObject wd:Q82955 ; uo:opType uo:add ; uo:revProp wdt:P106 ; uo:revSubject wd:Q6097419 . uo:op703878666_0 a uo:operation ; uo:fromRevision uo:rev703878666 ; uo:opType uo:remove ; uo:prevObject wd:Q1108445 ; uo:revProp wdt:P460 ; uo:revSubject wd:Q1147883 .</code></pre> This dataset is composed of revisions, which have a timestamp. Each revision is composed of 1 to n operations, in which there is a change to a statement from the entity. There are two types of operations: <em>uo:add</em> and <em>uo:remove</em>. In both cases, the property and the subject being modified are shown with the <em>uo:revProp</em> and <em>uo:revSubject</em> properties. In the case of additions, <em>uo:newObject</em> and <em>uo:prevObject</em> properties are added to show the previous and new objects after the addition. In the case of removals, there is a <em>uo:prevObject </em>property to record the object that was removed.
DOI: 10.5281/zenodo.6614264
title: Wikidata subset with revision history information [JSON]

description: This dataset consists the complete revision history of every instance of the 100 most important classes in Wikidata. It contains 9.3 million classes and around 450 million revisions made to those classes. This dataset was exported from a MongoDB database. After decompressing the files, the resulting JSON files can be imported into MongoDB using the following commands: <pre><code>mongoimport --db=db_name --collection=wd_entities --file=wd_entities.json mongoimport --db=db_name --collection=wd_revisions --file=wd_revisions.json </code></pre> Make sure that <em>db_name</em> is replaced by the database where this data will be imported. Documents within the <em>wd_entities</em> collection have the following schema: <strong>id</strong>: Internal id of the entity used by Wikidata (e.g. 8195238). <strong>entity_id</strong>: Public id of the entity in Wikidata (e.g. 'Q42') <strong>class_ids</strong>: List of classes that the entity belongs to (e.g. ['Q5', 'Q100']) <strong>entity_json</strong>: JSON contents of the entity, following Wikidata's JSON data model (https://doc.wikimedia.org/Wikibase/master/php/md_docs_topics_json.html). Documents within the <em>wd_revisions</em> collection have the following schema: <strong>id</strong>: Identifier of the revision (e.g. 15921539) <strong>entity_id</strong>: Public id of the entity in Wikidata affected by this revision (e.g. 'Q42') <strong>class_ids</strong>: List of classes that the entity affected by this revision belongs to (e.g. ['Q5', 'Q100']) <strong>parent_id</strong>: Identifier of the previous revision to this one, if it exists (e.g. 15921214) <strong>timestamp</strong>: Date where the revision was made, following the ISO 8601 format (e.g. +2019-05-27T09:31:10Z) <strong>username</strong>: Username of the user that made the revision. <strong>comment</strong>: Comments made by the user in the revision, if any. <strong>entity_diff</strong>: List of operations made in this revision, following the JSON Patch format.
DOI: 10.5281/zenodo.6614263
title: Wikidata subset with revision history information [JSON]

description: This dataset consists the complete revision history of every instance of the 100 most important classes in Wikidata. It contains 9.3 million classes and around 450 million revisions made to those classes. This dataset was exported from a MongoDB database. After decompressing the files, the resulting JSON files can be imported into MongoDB using the following commands: <pre><code>mongoimport --db=db_name --collection=wd_entities --file=wd_entities.json mongoimport --db=db_name --collection=wd_revisions --file=wd_revisions.json </code></pre> Make sure that <em>db_name</em> is replaced by the database where this data will be imported. Documents within the <em>wd_entities</em> collection have the following schema: <strong>id</strong>: Internal id of the entity used by Wikidata (e.g. 8195238). <strong>entity_id</strong>: Public id of the entity in Wikidata (e.g. 'Q42') <strong>class_ids</strong>: List of classes that the entity belongs to (e.g. ['Q5', 'Q100']) <strong>entity_json</strong>: JSON contents of the entity, following Wikidata's JSON data model (https://doc.wikimedia.org/Wikibase/master/php/md_docs_topics_json.html). Documents within the <em>wd_revisions</em> collection have the following schema: <strong>id</strong>: Identifier of the revision (e.g. 15921539) <strong>entity_id</strong>: Public id of the entity in Wikidata affected by this revision (e.g. 'Q42') <strong>class_ids</strong>: List of classes that the entity affected by this revision belongs to (e.g. ['Q5', 'Q100']) <strong>parent_id</strong>: Identifier of the previous revision to this one, if it exists (e.g. 15921214) <strong>timestamp</strong>: Date where the revision was made, following the ISO 8601 format (e.g. +2019-05-27T09:31:10Z) <strong>username</strong>: Username of the user that made the revision. <strong>comment</strong>: Comments made by the user in the revision, if any. <strong>entity_diff</strong>: List of operations made in this revision, following the JSON Patch format.
DOI: 10.5281/zenodo.6615489
title: Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 1. Weight changes due to fasting, bleeding, and chilling

description: This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 1. Weight changes due to fasting, bleeding, and chilling" to be published in the journal Animal - Open Space.
DOI: 10.5281/zenodo.6615491
title: Square Street Dataset in Unreal with AirSim installed

description: Thanks to Unreal Engine (UE) Editor, we were able to create custom scenes to test our approach. We created a street in the shape of a square, having 47 buildings spread over 0.16 square kilometers. The environment is feature-rich and, therefore, can be easily used for benchmarking monocular vision-based perception and planning pipelines. The whole environment is modular and can be easily expanded or configured to meet custom requirements. How to use: 1. Extract the zip 2. Open .uproject file in unreal 3. Open SquareStreet Map 4. Press Play to start AirSim Unreal Version: 4.25 OS: Linux
DOI: 10.5281/zenodo.6615490
title: Square Street Dataset in Unreal with AirSim installed

description: Thanks to Unreal Engine (UE) Editor, we were able to create custom scenes to test our approach. We created a street in the shape of a square, having 47 buildings spread over 0.16 square kilometers. The environment is feature-rich and, therefore, can be easily used for benchmarking monocular vision-based perception and planning pipelines. The whole environment is modular and can be easily expanded or configured to meet custom requirements. How to use: 1. Extract the zip 2. Open .uproject file in unreal 3. Open SquareStreet Map 4. Press Play to start AirSim Unreal Version: 4.25 OS: Linux
DOI: 10.5281/zenodo.6615499
title: Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 2. Developing prediction models

description: This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 2. Developing prediction models" to be published in the journal Animal - Open Space.
DOI: 10.34740/kaggle/dsv/3751236
title: Annotated US Supreme Court Arguments
DOI: 10.5281/zenodo.6615505
title: Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 3. Validation of prediction models

description: This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 3. Validation of prediction models" to be published in the journal Animal - Open Space.
DOI: 10.5281/zenodo.6615561
title: Just Yellow Graphs and stats yellows and base lines

description: At the top of the stats file are enlisted which type of statistics were performed (in which order); they were TrimmedVariance (lowest and highest values dropped of to 20%); InterquartileRange; Variance; StandardDeviation Graphs are composed of all the Yellows, per day.
DOI: 10.5281/zenodo.6615560
title: Just Yellow Graphs and stats yellows and base lines

description: At the top of the stats file are enlisted which type of statistics were performed (in which order); they were TrimmedVariance (lowest and highest values dropped of to 20%); InterquartileRange; Variance; StandardDeviation Graphs are composed of all the Yellows, per day.
DOI: 10.5281/zenodo.6615574
title: Mendelian gene identification through mouse embryo viability screening

description: Supplementary files to support a manuscript entitled "Mendelian gene identification through mouse embryo viability screening"
DOI: 10.6084/m9.figshare.20001554.v1
title: DSI

description: Dataset containing files for unit testing of TRX implementations.
DOI: 10.6084/m9.figshare.20001542
title: Mock Fungal Community - metagenomic sequening datasets

description: The simulating metagenomic sequencing datasets using the NCBI ITS and LSU fungal RefSeq database sequences. Specific for testing the fungal classification performance of MicroFisher.
DOI: 10.6084/m9.figshare.20001554.v2
title: DSI

description: Dataset containing files for unit testing of TRX implementations.