cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2hf8x4
title: CCDC 2306676: Experimental Crystal Structure Determination

description: Related Article: Zhenwei Chu, Zhishun Peng, Xu Cheng, Yuhui Hua, Guomei He, Jiangxi Chen, Guochen Jia|2024|Inorg.Chem.Front.|11|8479|doi:10.1039/D4QI01833H
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.cc2hfpm8
title: CCDC 2307070: Experimental Crystal Structure Determination

description: Related Article: Congcong Zhang, Minghui Shi, Ning Yu, Bowen Zhang, Feng Han, Chengxia Miao|2024|RSC Sustainability|2|3959|doi:10.1039/D4SU00556B
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: 10.6084/m9.figshare.1233382
title: Challenges and Prospects of Tree Seedlings Production in Katsina State of Nigeria
DOI: 10.6084/m9.figshare.1233384
title: Social Exclusion Its types and impact on Dalits in India
DOI: 10.6084/m9.figshare.1233742
title: Perceived Maternal Neglect and Adjustment Problems of High  School Students
DOI: 10.6084/m9.figshare.1233741
title: Martha Nussbaums Capabilities and Urban working poor  women in Bangladesh
DOI: 10.6084/m9.figshare.1233744
title: Effects of Monological and Sophrological Trainings on the Development of Attention and Retention in Tunisian  Male Kick Boxers
DOI: 10.15468/dl.cscavf
title: Occurrence Download

description: A dataset containing 1493 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Colección de bacterias aisladas de Musa sp."
 ]
}

 The dataset includes 1493 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030520-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.1233386
title: Impact of Indian Dramas on Interaction behavior of female
DOI: 10.6084/m9.figshare.1233385
title: Significance of the two Atharvavedic Socio-political Institutions in  the modern Indian political system
DOI: 10.6084/m9.figshare.1233743
title: Psychoanalysis of Ghulam Azam: A Person Alleged of War  Crimes
DOI: 10.6084/m9.figshare.1233746
title: Effects of Self-Talk and Sophrological Trainings on the  Development of Self-Confidence and Managing Emotions in  Tunisian Male Kick Boxers
DOI: 10.6084/m9.figshare.1233387
title: End-of-life care - Perceptions of Public
DOI: 10.6084/m9.figshare.1233745
title: Effects of Monological and Sophrological Trainings on the Development of Attention and Retention in Tunisian  Male Kick Boxers
DOI: 10.6084/m9.figshare.1233747
title: Social-Professional Antecedents and Prevalence of Burnout  Syndrome in Tunisian Male Kick Boxers
DOI: 10.6084/m9.figshare.1233748
title: A Study on Exclusion and Inclusion of Dalits in Education
DOI: 10.6084/m9.figshare.1233750
title: Socio-economic and political status of panchayats Elected  representatives (A Study of Mysore District-Karnataka)
DOI: 10.5517/ccdc.csd.cc2hm0hn
title: CCDC 2312181: Experimental Crystal Structure Determination

description: Related Article: Ranjana Aggarwal, Shilpa Sharma, Naman Jain, Dionisia Sanz, Rosa M. Claramunt, Patricia Delgado, M. Carmen Torralba|2024|RSC Advances|14|35585|doi:10.1039/D4RA05436A
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: 10.6084/m9.figshare.1233389
title: An Analysis on the Rate of Medicinal Herbs Consumption(Case  Study KhoramabadCity)
DOI: 10.5517/ccdc.csd.cc2hm0jp
title: CCDC 2312182: Experimental Crystal Structure Determination

description: Related Article: Ranjana Aggarwal, Shilpa Sharma, Naman Jain, Dionisia Sanz, Rosa M. Claramunt, Patricia Delgado, M. Carmen Torralba|2024|RSC Advances|14|35585|doi:10.1039/D4RA05436A
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: 10.6084/m9.figshare.1233388
title: Study on Absenteeism among Children in School
DOI: 10.6084/m9.figshare.1233749
title: Human vs. Nonhuman: Environmental Issues and Concerns in  AmitavGhoshsThe Hungry Tide
DOI: 10.6084/m9.figshare.1233390
title: Hussaini The Influence of the Sectarian Doctrine on the Architecture of the Shia Mosques in Iran
DOI: 10.5281/zenodo.14394609
title: SOGʻLIQNI SAQLASHDA MINTAQAVIY BOSHQARUV
DOI: 10.5281/zenodo.14394610
title: SOGʻLIQNI SAQLASHDA MINTAQAVIY BOSHQARUV
DOI: 10.6084/m9.figshare.1233751
title: Level of sachet water consumption in Nsukka urban, Enugu  State, Nigeria
DOI: 10.6084/m9.figshare.1233752
title: Family Burden on Parents of the Children with Cerebral Palsy:  Effectiveness of the Family Centered Psycho-Social Intervention  Programme
DOI: 10.6084/m9.figshare.1233753
title: The Relation between Ethics and Law according to the Doctors  Malpractice in Improving the Healthcare Quality in Jordanian  Government Hospitals
DOI: 10.15468/dl.4swmpu
title: Occurrence Download

description: A dataset containing 2056 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is MarineGEO Global Seagrass Habitat Monitoring Data"
 ]
}

 The dataset includes 2056 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030521-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.1233716
title: The Theological and Exegetical Interpretations of John 14:12 and  its Contextualization
DOI: 10.6084/m9.figshare.1233715
title: Analysis of Socio-Economic factors that Influence involvement in  Resource Based Conflicts in Kenya
DOI: 10.6084/m9.figshare.1233714
title: Collateral Damage: An Urgent Need for Legal Apparatus for  Protection of the Whistleblowers & RTI Activists
DOI: 10.6084/m9.figshare.1233717
title: Political Structure of the Khasis: With special reference to the  Nongthymmai Dorbar Pyllun
DOI: 10.6084/m9.figshare.1233718
title: Urban Socio-Economic and Environmental Condition of Hill  Tracts Bangladesh: A Case Study in Bandarban Municipality
DOI: 10.6084/m9.figshare.1233719
title: Effect of Science Technology Society Approach on Achievement  Motivation in Biology of Secondary School Students of Kasaragod  District
DOI: 10.6084/m9.figshare.1233720
title: Expansion of Red Corridor in Jangalmahal : A Demand for  Ethical Justice
DOI: 10.6084/m9.figshare.1233721
title: Managing Health and Safety Risk of Hepatitis B Transmission: A  Study on the Knowledge and Practice of Non-Professional Blood  Donors of Residential Halls of Dhaka University
DOI: 10.6084/m9.figshare.1233723
title: The Place and Importance of Malgudi in the novels of .K. Narayan
DOI: 10.6084/m9.figshare.1233722
title: The Issue of Gender Discrimination in Meghalaya With Special  Reference to East Khasi Hills
DOI: 10.6084/m9.figshare.1233725
title: Effect of TV Fashion Shows on Dressing Style of Female  Students of University Of Gujrat
DOI: 10.6084/m9.figshare.1233726
title: A Meta-Analysis of the Relationship between Information and  Communications Technology and Academic Achievement in Iran
DOI: 10.6084/m9.figshare.1233724
title: A Short account of the historical geography of Early Assam
DOI: 10.6084/m9.figshare.1233311
title: Participatory water sources management model for schools: Case  of Buhera district, Zimbabwe.
DOI: 10.6084/m9.figshare.1233727
title: The Influence of Poverty on Crime among the Abanyole of  Emuhaya District, Western Kenya
DOI: 10.6084/m9.figshare.1233314
title: Human Rights and Realities of Tribals Lives in India: A Perfect  Storm
DOI: 10.6084/m9.figshare.1233315
title: Impact of Consanguinity on Fertility and Mortality
DOI: 10.6084/m9.figshare.1233313
title: Use of Internet by the Research Scholars & Faculty Members: A Survey  Study of Kurukshetra University Kurukshetra in the Disciplines of Science
DOI: 10.6084/m9.figshare.1233312
title: Themes of Maba Manuk Mbur Texts in Karonese Society
DOI: 10.6084/m9.figshare.1233316
title: Interrelation of Environmental Communications through Radio  Broadcast Resources (Research on Love of House Yard  Movement in Sub-Province Sangihe, Indonesia)
DOI: 10.6084/m9.figshare.1233317
title: Domestic Tourism Development and Anchor Attractions:  Tourisming the Small Towns in Zimbabwe - The Tourists  Perspectives.
DOI: 10.6084/m9.figshare.1233318
title: Passives in Magahi
DOI: 10.6084/m9.figshare.1233319
title: The State, Development and Governance in Africa: The Nigerian  Experience
DOI: 10.6084/m9.figshare.1233321
title: Effect of Post Retirement Occupation on Survival of Academic  Staff Retirees: A Case Study of University Of Ibadan, Nigeria
DOI: 10.6084/m9.figshare.1233320
title: Libyan English as a Foreign Language School Teachers  (LEFLSTs) Knowledge of Teaching: Action Research as  Continuing Professional Development Model for Libyan School  Teachers
DOI: 10.5281/zenodo.14262736
title: Data repository for the 2022 AAPM deep-learning spectral CT grand challenge

description: This repository contains all the materials necessary for training deep-learning based algorithmsfor the DL-spectral CT Challenge.The training data folder contains 1000 cases with which to train your networks.For more information about the spectral CT modeling, please see starting_kit.tgz.

 

 

Data are in python numpy's .npy format, and arrays are stored in float32 (single precision).The .npy files can be read into python with the numpy.load command, yielding single precisionfloating point arrays of the proper dimensions.

Files:Phantom_[Tissue].npy.gz   ([Tissue] can be Adipose, Fibroglandular, or Calcification)These arrays are 1000x512x512. Files are gzipped for faster data transfer.1000 images of pixel dimensions 512x512.These are ground truth Tissue maps for the simulated breast phantom.

[low/high]kVpTransmission.npy.gzThese arrays are 1000x256x1024The represent 1000 cases of the low and high kVp transmission data, each of which has 256 projectionsonto a linear 1024-pixel detector.

[low/high]kVpImages.npy.gzThese arrays are 1000x512x5121000 images of pixel dimensions 512x512, generated from [low/high]kVpTransmission.npyby standard negative logarithm processing followed by filtered back-projection (FBP)image reconstruction.

For the challenge, the goal will be to estimate (or predict) Phantom_[Tissue].npyfrom either [low/high]kVpTransmission.npy or [low/high]kVpImages.npy or both.

Iterative or model-based approaches will necessarily use [low/high]kVpTransmission.npy as input.Deep-learning approaches can be:solely image-to-image; i.e. [low/high]kVpImages -> Phantom[Tissue] prediction.solely data-to-image; i.e. [low/high]kVpTransmission -> Phantom[Tissue] prediction.Or some combination of the two.Hybrid iterative/deep-learning approaches are also acceptable.

 

Participants using image-to-image approaches do not need to know the spectral CT model.All other approaches will need this knowledge and model specifications areprovided in the folder, which has been packed in the gzipped tar-file starting_kit.tgz.

 

Challenge report is published in Medical Phyiscs:

Sidky EY, Pan X. Report on the AAPM deep-learning spectral CT Grand Challenge. Med Phys. 2024; 51: 772–785.

See link below in Related Works.
DOI: 10.5281/zenodo.14262737
title: Data repository for the 2022 AAPM deep-learning spectral CT grand challenge

description: This repository contains all the materials necessary for training deep-learning based algorithmsfor the DL-spectral CT Challenge.The training data folder contains 1000 cases with which to train your networks.For more information about the spectral CT modeling, please see starting_kit.tgz.

 

 

Data are in python numpy's .npy format, and arrays are stored in float32 (single precision).The .npy files can be read into python with the numpy.load command, yielding single precisionfloating point arrays of the proper dimensions.

Files:Phantom_[Tissue].npy.gz   ([Tissue] can be Adipose, Fibroglandular, or Calcification)These arrays are 1000x512x512. Files are gzipped for faster data transfer.1000 images of pixel dimensions 512x512.These are ground truth Tissue maps for the simulated breast phantom.

[low/high]kVpTransmission.npy.gzThese arrays are 1000x256x1024The represent 1000 cases of the low and high kVp transmission data, each of which has 256 projectionsonto a linear 1024-pixel detector.

[low/high]kVpImages.npy.gzThese arrays are 1000x512x5121000 images of pixel dimensions 512x512, generated from [low/high]kVpTransmission.npyby standard negative logarithm processing followed by filtered back-projection (FBP)image reconstruction.

For the challenge, the goal will be to estimate (or predict) Phantom_[Tissue].npyfrom either [low/high]kVpTransmission.npy or [low/high]kVpImages.npy or both.

Iterative or model-based approaches will necessarily use [low/high]kVpTransmission.npy as input.Deep-learning approaches can be:solely image-to-image; i.e. [low/high]kVpImages -> Phantom[Tissue] prediction.solely data-to-image; i.e. [low/high]kVpTransmission -> Phantom[Tissue] prediction.Or some combination of the two.Hybrid iterative/deep-learning approaches are also acceptable.

 

Participants using image-to-image approaches do not need to know the spectral CT model.All other approaches will need this knowledge and model specifications areprovided in the folder, which has been packed in the gzipped tar-file starting_kit.tgz.

 

Challenge report is published in Medical Phyiscs:

Sidky EY, Pan X. Report on the AAPM deep-learning spectral CT Grand Challenge. Med Phys. 2024; 51: 772–785.

See link below in Related Works.
DOI: 10.5281/zenodo.14173521
title: Data repository for the 2021 AAPM deep-learning sparse-view CT grand challenge

description: The training data for developing the neural networks for DL sparse-view CT are contained in this repository.All files are compressed with gzip in order to facilate faster downloads.Data are partitioned into four batches, which also facilates downloading of theindividual files. Data are in python numpy's .npy format.After uncompressing with gunzip the .npy files can be read into pythonwith the numpy.load command, yielding single precision floating point arraysof the proper dimensions.

Description of data files:Phantom_batch?.npyThese arrays are 1000x512x512.1000 images of pixel dimensions 512x512.These are the true images.

FBP128_batch?.npyThese arrays are 1000x512x512.1000 images of pixel dimensions 512x512.These are the FBP reconstructed images from the 128-view sinograms.

Sinogram_batch?.npyThese arrays are 1000x128x1024.1000 sinograms of 128 projections over 360 degree scanning onto a 1024-pixel linear detector. 

There are four batches. Thus 4000 sets of data/image pairs are available for trainingthe neural networks for image reconstruction.The goal is to train a network that accepts the FBP128 image (and/or the 128-view sinogram)to yield an image that is as close as possible to the corresponding Phantom image.

 

The python code metric_script.py was used to score the submissions to DL sparse-view CT.

 

Challenge report is published in Medical Phyiscs:

Sidky EY, Pan X. Report on the AAPM deep-learning sparse-view CT grand challenge. Med Phys. 2022; 49: 4935–4943.

See link below in Related Works.
DOI: 10.5281/zenodo.14173522
title: Data repository for the 2021 AAPM deep-learning sparse-view CT grand challenge

description: The training data for developing the neural networks for DL sparse-view CT are contained in this repository.All files are compressed with gzip in order to facilate faster downloads.Data are partitioned into four batches, which also facilates downloading of theindividual files. Data are in python numpy's .npy format.After uncompressing with gunzip the .npy files can be read into pythonwith the numpy.load command, yielding single precision floating point arraysof the proper dimensions.

Description of data files:Phantom_batch?.npyThese arrays are 1000x512x512.1000 images of pixel dimensions 512x512.These are the true images.

FBP128_batch?.npyThese arrays are 1000x512x512.1000 images of pixel dimensions 512x512.These are the FBP reconstructed images from the 128-view sinograms.

Sinogram_batch?.npyThese arrays are 1000x128x1024.1000 sinograms of 128 projections over 360 degree scanning onto a 1024-pixel linear detector. 

There are four batches. Thus 4000 sets of data/image pairs are available for trainingthe neural networks for image reconstruction.The goal is to train a network that accepts the FBP128 image (and/or the 128-view sinogram)to yield an image that is as close as possible to the corresponding Phantom image.

 

The python code metric_script.py was used to score the submissions to DL sparse-view CT.

 

Challenge report is published in Medical Phyiscs:

Sidky EY, Pan X. Report on the AAPM deep-learning sparse-view CT grand challenge. Med Phys. 2022; 49: 4935–4943.

See link below in Related Works.
DOI: 10.15468/dl.v6s6c2
title: Occurrence Download

description: A dataset containing 17966920 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Abrothrix longipilis (Waterhouse, 1837), Abrothrix olivaceus (Waterhouse, 1837), Accipiter bicolor (Vieillot, 1817), Actitis macularius (Linnaeus, 1766), Agelasticus thilius (Molina, 1782), Agriornis lividus (Kittlitz, 1835), Aldrovandia phalacra (Vaillant, 1888), Alepisaurus brevirostris Gibbs, 1960, Alepisaurus ferox Lowe, 1833, Aluterus monoceros (Linnaeus, 1758), Anairetes parulus (Kittlitz, 1830), Anas bahamensis Linnaeus, 1758, Anas flavirostris Vieillot, 1816, Anas georgica J.F.Gmelin, 1789, Anisotremus scapularis (Tschudi, 1846), Anodontites trigona (Spix, 1827), Anoplogaster cornuta (Valenciennes, 1833), Anotopterus pharao Zugmayer, 1911, Anotopterus vorax (Regan, 1913), Anthus correndera Vieillot, 1818, Antimora rostrata (Günther, 1878), Aphos porosus (Valenciennes, 1837), Aphrastura spinicauda (Gmelin, 1789), Aplochiton zebra Jenyns, 1842, Apristurus nasutus de Buen, 1959, Archidasyphyllum diacanthoides (Less.) P.L.Ferreira, Saavedra & Groppo, Arctocephalus philippii (Peters, 1866), Arctozenus risso (Bonaparte, 1840), Ardea alba Linnaeus, 1758, Ardea cocoi Linnaeus, 1766, Ardenna grisea (J.F.Gmelin, 1789), Arenaria interpres (Linnaeus, 1758), Argyropelecus aculeatus Valenciennes, 1850, Argyropelecus gigas Norman, 1930, Argyropelecus hemigymnus Cocco, 1829, Argyropelecus olfersii (Cuvier, 1829), Argyropelecus sladeni Regan, 1908, Aristostomias tittmanni Welsh, 1923, Aristotelia chilensis (Molina) Stuntz, Asio flammeus (Pontoppidan, 1763), Asthenes anthoides (King, 1831), Astronesthes boulengeri Gilchrist, 1902, Astronesthes galapagensis Parin, Borodulina & Hulley, 1999, Astronesthes illuminatus Parin, Borodulina & Hulley, 1999, Astronesthes indicus Brauer, 1902)"
 ]
}

 The dataset includes 17966920 records from 4007 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030516-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13145/bacdive106802.20241212.9.2
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive106802.20251217.10
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0098617.10fu
title: SH0098617.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.13145/bacdive106803.20241212.9.2
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive106803.20251217.10
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0098618.10fu
title: SH0098618.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.13145/bacdive106804.20241212.9.2
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive106804.20251217.10
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.