cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25318/9810029201-eng
title: Indigenous identity population by gender and age: Canada, provinces and territories, census metropolitan areas and census agglomerations

description: Indigenous identity by gender and age for the population in private households which includes counts, percentage distribution and percentage change from the 2011 National Household Survey, and the 2016 and 2021 censuses.
DOI: 10.23728/b2share.b6336c505b9d431ea3d76251edb7fc64
title: Quest for Orthologs - 2022 Reference Proteomes participant dataset submitted by Ensembl_Compara

description: Participant dataset OEBD002000034A OEBD002000033M OEBD002000033C OEBD002000035X OEBD002000033W OEBD0020000330 OEBD002000033G OEBD0020000363 OEBD002000033S OEBD0020000336 OEBD0020000346 OEBD0020000340 submitted by Ensembl_Compara for event Quest for Orthologs - 2022 Reference Proteomes, organized by community QfO. Predictions made by Ensembl_Compara participant
DOI: 10.5517/ccdc.csd.cc2gv9rd
title: CCDC 2289404: Experimental Crystal Structure Determination

description: Related Article: Xin-Yang Fan, Jia-Cheng Li, Ji-Jia Zhou, Bo Zhou, Long-Wu Ye|2023|Green Chemistry|25|10638|doi:10.1039/D3GC03222A
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: 10.5517/ccdc.csd.cc2g1lch
title: CCDC 2265646: Experimental Crystal Structure Determination

description: Related Article: Wen-Wen Yong, Hong-Tao Zhang, Yu-Hua Guo, Fei Xie, Ming-Tian Zhang|2023|ACS Org. Inorg. Au|3|384|doi:10.1021/acsorginorgau.3c00027
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: 10.5517/ccdc.csd.cc2h2pt3
title: CCDC 2296505: Experimental Crystal Structure Determination

description: Related Article: Avinash Madhesiya, Sibananda G. Dash, Princi Gupta, Abdul Akhir, Deepanshi Saxena, Rahul Maitra, Sidharth Chopra, Tejender S. Thakur|2023|CrystEngComm|25|6758|doi:10.1039/D3CE00948C
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: 10.5517/ccdc.csd.cc2h2pv4
title: CCDC 2296506: Experimental Crystal Structure Determination

description: Related Article: Avinash Madhesiya, Sibananda G. Dash, Princi Gupta, Abdul Akhir, Deepanshi Saxena, Rahul Maitra, Sidharth Chopra, Tejender S. Thakur|2023|CrystEngComm|25|6758|doi:10.1039/D3CE00948C
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: 10.5517/ccdc.csd.cc2h2pw5
title: CCDC 2296507: Experimental Crystal Structure Determination

description: Related Article: Avinash Madhesiya, Sibananda G. Dash, Princi Gupta, Abdul Akhir, Deepanshi Saxena, Rahul Maitra, Sidharth Chopra, Tejender S. Thakur|2023|CrystEngComm|25|6758|doi:10.1039/D3CE00948C
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: 10.5517/ccdc.csd.cc2h2w3l
title: CCDC 2296669: Experimental Crystal Structure Determination

description: Related Article: Avinash Madhesiya, Sibananda G. Dash, Princi Gupta, Abdul Akhir, Deepanshi Saxena, Rahul Maitra, Sidharth Chopra, Tejender S. Thakur|2023|CrystEngComm|25|6758|doi:10.1039/D3CE00948C
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: 10.5517/ccdc.csd.cc2h8ndv
title: CCDC 2302227: Experimental Crystal Structure Determination

description: Related Article: Kyzgaldak Ramazanova, Soumyadeep Chakrabortty, Bernd Mueller, Peter Lönnecke, Johannes de Vries, Evamarie Hey-Hawkins|2023|RSC Advances|13|34439|doi:10.1039/D3RA07630J
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: 10.5517/ccdc.csd.cc2h9fqz
title: CCDC 2302981: Experimental Crystal Structure Determination

description: Related Article: Duqingcuo Li, Yichen Liu, Ruifang Xiang, Yuyan Li, Tianrui Qin, Xiu Yan Dong, Hiroshi Sakiyama, Mohd. Muddassir, Jianqiang Liu|2023|CrystEngComm|25|6777|doi:10.1039/D3CE01060K
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: 10.25318/9810029201-fra
title: Population ayant une identité autochtone selon le genre et l'âge : Canada, provinces et territoires, régions métropolitaines de recensement et agglomérations de recensement

description: Identité autochtone selon le genre et l'âge pour la population dans les ménages privés, qui comprend les chiffres, la répartition en pourcentage et la variation en pourcentage provenant de l'Enquête nationale auprès des ménages de 2011, et des recensements de 2016 et 2021.
DOI: 10.6084/m9.figshare.24799524
title: Swedish Cervical Screening Samples 2011-2021

description: The Cervical Screening Cohort enrols women screened for human papillomavirus (HPV) and cervical abnormalities within the capital region of Sweden, both from the organised screening program and the non-organised testing of cervical samples. The cohort started in 2011 and has enrolled more than 670,000 women at more than 1.2 million screening visits. Most women have contributed, on average, with two samples between 2011 and 2022. The enrolment process uses written invitations and informed consent, including an opt-out procedure. The cohort data is systematically updated with individual-level data from the Swedish National Cervical Screening Registry (NKCx). Key variables include birthdate, sampling date, cytological and HPV analysis results, histopathological diagnoses (results of cervical biopsies or conisations), and screening invitations, including the invitation date. Each round of sampling and subsequent clinical follow-up is sequentially saved in the database, allowing for longitudinal analyses of screening results and associated results of the clinical workup. Systematic epigenomics and metagenomics analyses performed on the samples enrich the cohort data.The cohort is ideal for longitudinal, long-term follow-up studies due to its validated documentation and registry-derived information. From the data, it is possible to penetrate important human health mechanisms. The data are available as open-access, GDPR-compliant. Samples are available after getting the required permissions. Results will help researchers understand factors that increase cancer risk and other diseases.
DOI: 10.5517/ccdc.csd.cc2hbhn0
title: CCDC 2304002: Experimental Crystal Structure Determination

description: Related Article: Avinash Madhesiya, Sibananda G. Dash, Princi Gupta, Abdul Akhir, Deepanshi Saxena, Rahul Maitra, Sidharth Chopra, Tejender S. Thakur|2023|CrystEngComm|25|6758|doi:10.1039/D3CE00948C
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: 10.5517/ccdc.csd.cc2hdnxh
title: CCDC 2306087: Experimental Crystal Structure Determination

description: Related Article: Wenjun Luo, Xinghua Zheng, Fanglian Zhang, Qiuya Luo, Wen-Ting Deng, Lipeng Long, Daohong Yu, Zhong-Xia Wang, Zhengwang Chen|2023|Org.Biomol.Chem.|21|9524|doi:10.1039/D3OB01547E
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: 10.23728/b2share.666b6e54780e4450a45a840fad0647cb
title: Quest for Orthologs - 2022 Reference Proteomes participant dataset submitted by OrthoInspector_3.5

description: Participant dataset OEBD002000035E OEBD002000035K OEBD0020000358 OEBD002000034W OEBD0020000352 OEBD002000034Q OEBD002000034E OEBD002000035Q submitted by OrthoInspector_3.5 for event Quest for Orthologs - 2022 Reference Proteomes, organized by community QfO. Predictions made by OrthoInspector_3.5 participant
DOI: 10.23728/b2share.db2e09f374e647ecb0fc4e70a58f3186
title: Quest for Orthologs - 2022 Reference Proteomes participant dataset submitted by Hieranoid_2

description: Participant dataset OEBD00200002YJ submitted by Hieranoid_2 for event Quest for Orthologs - 2022 Reference Proteomes, organized by community QfO. Predictions made by Hieranoid_2 participant
DOI: 10.6084/m9.figshare.24799824
title: Data.sav

description: SPSS file.
DOI: 10.6084/m9.figshare.24799824.v1
title: Data.sav

description: SPSS file.
DOI: 10.25318/9810029301-eng
title: Indigenous identity population by gender and age: Canada, provinces and territories, census divisions and census subdivisions

description: Indigenous identity by gender and age for the population in private households which includes counts, percentage distribution and percentage change from the 2016 and 2021 censuses.
DOI: 10.23728/b2share.3a8b55dece994f07bd2731fc9e2d4d0a
title: Quest for Orthologs - 2022 Reference Proteomes participant dataset submitted by BBH_(SW_alignments)

description: Participant dataset OEBD00200002V8 submitted by BBH_(SW_alignments) for event Quest for Orthologs - 2022 Reference Proteomes, organized by community QfO. Predictions made by BBH_(SW_alignments) participant
DOI: 10.23728/b2share.98403df68d6b496c84ddadd5eacb8815
title: Quest for Orthologs - 2022 Reference Proteomes participant dataset submitted by OMA_Groups

description: Participant dataset OEBD00200002WC submitted by OMA_Groups for event Quest for Orthologs - 2022 Reference Proteomes, organized by community QfO. Predictions made by OMA_Groups participant
DOI: 10.23728/b2share.622d8d55214c4236b939fda3a2edbd28
title: Quest for Orthologs - 2022 Reference Proteomes participant dataset submitted by OMA_HOGs

description: Participant dataset OEBD00200002XF submitted by OMA_HOGs for event Quest for Orthologs - 2022 Reference Proteomes, organized by community QfO. Predictions made by OMA_HOGs participant
DOI: 10.23728/b2share.510956961e674776817fbab4115d3a7b
title: Quest for Orthologs - 2022 Reference Proteomes participant dataset submitted by OMA_Pairs

description: Participant dataset OEBD00200002ZN submitted by OMA_Pairs for event Quest for Orthologs - 2022 Reference Proteomes, organized by community QfO. Predictions made by OMA_Pairs participant
DOI: 10.25318/9810029301-fra
title: Population ayant une identité autochtone selon le genre et l'âge : Canada, provinces et territoires, divisions de recensement et subdivisions de recensement

description: Identité autochtone selon le genre et l'âge pour la population dans les ménages privés, qui comprend les chiffres, la répartition en pourcentage et la variation en pourcentage provenant des recensements de 2016 et 2021.
DOI: 10.23728/b2share.a547f13970b54d0a80352dd009f5b578
title: Quest for Orthologs - 2022 Reference Proteomes participant dataset submitted by RSD_(SW-Alignments)

description: Participant dataset OEBD00200003G9 submitted by RSD_(SW-Alignments) for event Quest for Orthologs - 2022 Reference Proteomes, organized by community QfO. Predictions made by RSD_(SW-Alignments) participant
DOI: 10.23728/b2share.ca7309a899ed410f88ca37044692cf2b
title: Quest for Orthologs - 2022 Reference Proteomes participant dataset submitted by MetaPhOrs_v2.5

description: Participant dataset OEBD00200003F5 submitted by MetaPhOrs_v2.5 for event Quest for Orthologs - 2022 Reference Proteomes, organized by community QfO. Predictions made by MetaPhOrs_v2.5 participant
DOI: 10.23728/b2share.994214e4a3404bb2bc3150d0e505700f
title: Quest for Orthologs - 2022 Reference Proteomes participant dataset submitted by OrthoFFGC

description: Participant dataset OEBD00200003E1 submitted by OrthoFFGC for event Quest for Orthologs - 2022 Reference Proteomes, organized by community QfO. Predictions made by OrthoFFGC participant
DOI: 10.6084/m9.figshare.24799906
title: Supplementary Table 1 from Exploring the relationships between psychological variables and loot box engagement, part 1: pre-registered hypotheses

description: Supplementary Table 1. Mean typical monthly spend and RLI by employment status.
DOI: 10.6084/m9.figshare.24799906.v1
title: Supplementary Table 1 from Exploring the relationships between psychological variables and loot box engagement, part 1: pre-registered hypotheses

description: Supplementary Table 1. Mean typical monthly spend and RLI by employment status.
DOI: 10.23728/b2share.764359aae3d7405ea0d9ef72aaa9645f
title: Quest for Orthologs - 2022 Reference Proteomes participant dataset submitted by phylomedb_v5

description: Participant dataset OEBD002000037D submitted by phylomedb_v5 for event Quest for Orthologs - 2022 Reference Proteomes, organized by community QfO. Predictions made by phylomedb_v5 participant
DOI: 10.23728/b2share.01f7480e09134fa5b75464003576cc70
title: Quest for Orthologs - 2022 Reference Proteomes participant dataset submitted by PANTHER_(v18)_all

description: Participant dataset OEBD002000039L submitted by PANTHER_(v18)_all for event Quest for Orthologs - 2022 Reference Proteomes, organized by community QfO. Predictions made by PANTHER_(v18)_all participant
DOI: 10.23728/b2share.8ba2308dbe0d4dd0890af5836ffa53b8
title: Quest for Orthologs - 2022 Reference Proteomes participant dataset submitted by PANTHER_(v18)_LDO

description: Participant dataset OEBD002000038H submitted by PANTHER_(v18)_LDO for event Quest for Orthologs - 2022 Reference Proteomes, organized by community QfO. Predictions made by PANTHER_(v18)_LDO participant
DOI: 10.23728/b2share.932c9a5e976e495fb23b0c023de0efd0
title: Quest for Orthologs - 2022 Reference Proteomes participant dataset submitted by SonicParanoid2-sens

description: Participant dataset OEBD00200003CX submitted by SonicParanoid2-sens for event Quest for Orthologs - 2022 Reference Proteomes, organized by community QfO. Predictions made by SonicParanoid2-sens participant
DOI: 10.23728/b2share.beb9216ad6754c65abec9005a2df64f3
title: Quest for Orthologs - 2022 Reference Proteomes participant dataset submitted by SonicParanoid2-sens-g

description: Participant dataset OEBD00200003AP submitted by SonicParanoid2-sens-g for event Quest for Orthologs - 2022 Reference Proteomes, organized by community QfO. Predictions made by SonicParanoid2-sens-g participant
DOI: 10.6084/m9.figshare.24799927
title: Raw data and code from Social administration of juvenile hormone to larvae increases body size and nutritional needs for pupation

description: Contains the raw data and the R script of the analysis
DOI: 10.23728/b2share.028e02c9c38f4698ba074a7f0ae158d6
title: Quest for Orthologs - 2022 Reference Proteomes participant dataset submitted by FastOMA_v0.1.4_nov

description: Participant dataset OEBD00200003HD submitted by FastOMA_v0.1.4_nov for event Quest for Orthologs - 2022 Reference Proteomes, organized by community QfO. Predictions made by FastOMA_v0.1.4_nov participant
DOI: 10.5281/zenodo.10371464
title: FATURA Dataset

description: The dataset consists of 10000 jpg images with white backgrounds, 10000 jpg images with colored backgrounds (the same colors used in the paper) as well as 3x10000 json annotation files. The images are generated from 50 different templates. For each template, 200 images were generated. We provide annotations in three formats: our own original format, the COCO format and a format compatible with HuggingFace Transformers. Background color varies across templates but not across instances from the same template.
In terms of objects, the dataset contains 24 different classes. The classes vary considerably in their numbers of occurrences and thus, the dataset is somewhat imbalanced.
The annotations contain bounding box coordinates, bounding box text and object classes.
We propose two methods for training and evaluating models. The models were trained until convergence ie until the model reaches optimal performance on the validation split and started overfitting. The model version used for evaluation is the one with the best validation performance.
First Evaluation strategy:For each template, the generated images are randomly split into 3 subsets: training, validation and testing.In this scenario, the model trains on all templates and is thus tested on new images rather than new layouts.
Second Evaluation strategy:The real templates are randomly split into a training set, and a common set of templates for validation and testing. All the variants created from the training templates are used as training dataset. The same is done to form the validation and testing datasets. The validation and testing sets are made up of the same templates but of different images.This approach tests the models' performance on different unseen templates/layouts, rather than the same templates with different content.
We provide the data splits we used for every evaluation scenario. We also provide the background colors we used as augmentation for each template.
description: This dataset was developed in the Digital Research Center of Sfax.
DOI: 10.5281/zenodo.8261507
title: FATURA Dataset

description: The dataset consists of 10000 jpg images with white backgrounds, 10000 jpg images with colored backgrounds (the same colors used in the paper) as well as 3x10000 json annotation files. The images are generated from 50 different templates. For each template, 200 images were generated. We provide annotations in three formats: our own original format, the COCO format and a format compatible with HuggingFace Transformers. Background color varies across templates but not across instances from the same template.
In terms of objects, the dataset contains 24 different classes. The classes vary considerably in their numbers of occurrences and thus, the dataset is somewhat imbalanced.
The annotations contain bounding box coordinates, bounding box text and object classes.
We propose two methods for training and evaluating models. The models were trained until convergence ie until the model reaches optimal performance on the validation split and started overfitting. The model version used for evaluation is the one with the best validation performance.
First Evaluation strategy:For each template, the generated images are randomly split into 3 subsets: training, validation and testing.In this scenario, the model trains on all templates and is thus tested on new images rather than new layouts.
Second Evaluation strategy:The real templates are randomly split into a training set, and a common set of templates for validation and testing. All the variants created from the training templates are used as training dataset. The same is done to form the validation and testing datasets. The validation and testing sets are made up of the same templates but of different images.This approach tests the models' performance on different unseen templates/layouts, rather than the same templates with different content.
We provide the data splits we used for every evaluation scenario. We also provide the background colors we used as augmentation for each template.
description: This dataset was developed in the Digital Research Center of Sfax.
DOI: 10.5281/zenodo.8261508
title: FATURA Dataset

description: The dataset consists of 10000 jpg images and 3x10000 json annotation files. The images are generated from 50 different templates. For each template, 200 images were generated. We provide annotations in three formats: our own original format, the COCO format and a format compatible with HuggingFace Transformers.


In terms of objects, the dataset contains 24 different classes. The classes vary considerably in their numbers of occurrences and thus, the dataset is somewhat imbalanced.


The annotations contain bounding box coordinates, bounding box text and object classes.


We propose two methods for training and evaluating models. The models were trained until convergence ie until the model reaches optimal performance on the validation split and started overfitting. The model version used for evaluation is the one with the best validation performance.


First Evaluation strategy:
For each template, the generated images are randomly split into 3 subsets: training, validation and testing.
In this scenario, the model trains on all templates and is thus tested on new images rather than new layouts.


Second Evaluation strategy:
The real templates are randomly split into a training set, and a common set of templates for validation and testing. All the variants created from the training templates are used as training dataset. The same is done to form the validation and testing datasets. The validation and testing sets are made up of the same templates but of different images.
This approach tests the models' performance on different unseen templates/layouts, rather than the same templates with different content.


We provide the data splits we used for every evaluation scenario. We also provide the background colors we used as augmentation for each template.
description: This dataset was developed in the Digital Research Center of Sfax.