cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.f5wab4
title: Occurrence Download

description: A dataset containing 2137 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ancistrocladaceae"
 ]
}

 The dataset includes 2137 records from 88 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010432-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccdc.csd.cc2cm236
title: CCDC 2193067: Experimental Crystal Structure Determination

description: Related Article: Adrien Madron du Vigné, Nicolai Cramer|2024|Chemical Science|15|13864|doi:10.1039/D4SC04278F
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.cc2cm247
title: CCDC 2193068: Experimental Crystal Structure Determination

description: Related Article: Adrien Madron du Vigné, Nicolai Cramer|2024|Chemical Science|15|13864|doi:10.1039/D4SC04278F
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.cc2cx3cs
title: CCDC 2201755: Experimental Crystal Structure Determination

description: Related Article: Subhi A. Al-Jibori, Ahmed S. Al-Janabi, Ahmed A. Irzoqi, Ali I. A. Abdullah, Sucharita Basak-Modi, Georgia R. F. Orton, Shishir Ghosh, Christof Wagner, Graeme Hogarth|2022|Dalton Trans.|51|18136|doi:10.1039/D2DT02681C
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.cc2dzqz3
title: CCDC 2234076: Experimental Crystal Structure Determination

description: Related Article: Joanna Masternak, Karol Okła, Adam Kubas, Jiří Voller, Karolína Kozlanská, Małgorzata Zienkiewicz-Machnik, Agnieszka Gilewska, Jerzy Sitkowski, Anna Kamecka, Katarzyna Kazimierczuk, Barbara Barszcz|2024|Dalton Trans.|53|14438|doi:10.1039/D4DT01796J
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.cc2dzvx5
title: CCDC 2234198: Experimental Crystal Structure Determination

description: Related Article: Yaqi Deng, Jiabin Yin, Zongjing Hu, Jian Xue, Jian Ji, Tong Zhu, Shunying Liu|2024|New J.Chem.|48|15002|doi:10.1039/D4NJ02699C
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.cc2fxfng
title: CCDC 2261656: Experimental Crystal Structure Determination

description: Related Article: Mengliang Zhu, Hang Zhang, Yuhang Yao, Mingpu Wen, Guangliu Ran, Yi Yu, Ruijing Zhang, Xing-Jie Liang, Jing Zhang, Wenkai Zhang, Jun-Long Zhang|2024|Chemical Science|15|13841|doi:10.1039/D4SC03642E
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.cc2fxfmf
title: CCDC 2261655: Experimental Crystal Structure Determination

description: Related Article: Mengliang Zhu, Hang Zhang, Yuhang Yao, Mingpu Wen, Guangliu Ran, Yi Yu, Ruijing Zhang, Xing-Jie Liang, Jing Zhang, Wenkai Zhang, Jun-Long Zhang|2024|Chemical Science|15|13841|doi:10.1039/D4SC03642E
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.cc2fxftm
title: CCDC 2261661: Experimental Crystal Structure Determination

description: Related Article: Mengliang Zhu, Hang Zhang, Yuhang Yao, Mingpu Wen, Guangliu Ran, Yi Yu, Ruijing Zhang, Xing-Jie Liang, Jing Zhang, Wenkai Zhang, Jun-Long Zhang|2024|Chemical Science|15|13841|doi:10.1039/D4SC03642E
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.cc2gpmg8
title: CCDC 2284900: Experimental Crystal Structure Determination

description: Related Article: Barbara Kubiak, Tadeusz Muzioł, Mirosław Jabłoński, Aleksandra Radtke, Piotr Piszczek|2024|Dalton Trans.|53|14457|doi:10.1039/D4DT01710B
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.cc2gz8b2
title: CCDC 2293204: Experimental Crystal Structure Determination

description: Related Article: Pinar Alayoglu, Suresh C. Rathnayaka, Tieyan Chang, SuYin Grass Wang, Yu-Sheng Chen, Neal P. Mankad|2024|Chemical Science|15|13668|doi:10.1039/D4SC00701H
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: 10.15468/dl.a86kpc
title: Occurrence Download

description: A dataset containing 47985 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Issue is one of (Coordinate rounded, Geodetic datum invalid, Geodetic datum assumed WGS84, Coordinate reprojected, Coordinate reprojection failed, Coordinate reprojection suspicious, Footprint SRS invalid, Footprint WKT mismatch, Footprint WKT invalid, Country coordinate mismatch, Country mismatch, Country invalid, Country derived from coordinates, Continent coordinate mismatch, Continent country mismatch, Continent invalid, Continent derived from country, Continent derived from coordinates, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Recorded date mismatch, Recorded date invalid, Recorded date unlikely, Taxon match fuzzy, Taxon match higher rank, Taxon match aggregate, The scientific name ID was not used in backbone matching, The taxon concept ID was not used in backbone matching, The taxon ID was not used in backbone matching, The scientific name ID was not found in the checklist, The taxon concept ID was not found in the checklist, The taxon ID was not found in the checklist, The scientific name differs on the record from that found in the checklist, Taxon match none, The backbone match found using the taxon IDs differs from what would be found if using the scientific names , Depth not metric, Depth unlikely, Depth min max swapped, Depth non numeric, Elevation unlikely, Elevation min max swapped, Elevation not metric, Elevation non numeric, Modified date invalid, Modified date unlikely, Identified date unlikely, Identified date invalid, Basis of record invalid, Type status invalid, Multimedia date invalid, Multimedia URI invalid, References URI invalid, Error during interpretation, Individual count invalid, Individual count conflicts with occurrence status, Occurrence status unparsable, Occurrence status inferred from individual count, ccurrence status inferred from basis of record, Georeferenced date unlikely, Georeferenced date invalid, Ambiguous institution, Ambiguous collection, Institution match none, Collection match none, Institution match fuzzy, Collection match fuzzy, Institution collection mismatch, Possibly on loan, Different owner institution)",
 "TaxonKey is Sphyraena barracuda (Edwards, 1771)"
 ]
}

 The dataset includes 47985 records from 301 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010433-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57841/casdc.0003938
title: 重离子辐照实验数据集
DOI: 10.6084/m9.figshare.25444241
title: Federated learning with blockchain-based model aggregation and incentives

description: Federated learning is a privacy-preserving machine learning technique that allows mutually distrusting parties to collaboratively train a model without sharing their data. Most federated learning techniques require a centralized aggregator that stores and aggregates models received from multiple parties. However, having a centralized entity may lead to a single point of failure problem. Another problem of federated learning is the leakage of sensitive data through model updates. To address these issues, we propose a Blockchain-based protocol for federated learning. Our protocol uses Blockchain as a model aggregator solving single-point-of-failure problems. Also, we use a Blockchain-based privacy-preserving technique to avoid data leakage problems. We also incorporate a Blockchain-based incentive distribution module to distribute incentives to model contributors. We perform experiments with well-known datasets and show that the proposed model's accuracy is close to that of a centralized aggregator. We also show the overhead of Blockchain by implementing the protocol and running it on Ethereum Blockchain.
DOI: 10.6084/m9.figshare.25444241.v1
title: Federated learning with blockchain-based model aggregation and incentives

description: Federated learning is a privacy-preserving machine learning technique that allows mutually distrusting parties to collaboratively train a model without sharing their data. Most federated learning techniques require a centralized aggregator that stores and aggregates models received from multiple parties. However, having a centralized entity may lead to a single point of failure problem. Another problem of federated learning is the leakage of sensitive data through model updates. To address these issues, we propose a Blockchain-based protocol for federated learning. Our protocol uses Blockchain as a model aggregator solving single-point-of-failure problems. Also, we use a Blockchain-based privacy-preserving technique to avoid data leakage problems. We also incorporate a Blockchain-based incentive distribution module to distribute incentives to model contributors. We perform experiments with well-known datasets and show that the proposed model's accuracy is close to that of a centralized aggregator. We also show the overhead of Blockchain by implementing the protocol and running it on Ethereum Blockchain.
DOI: 10.6084/m9.figshare.25314902
title: TOPSIS-ACO based feature selection for multi-label classification

description: This paper introduces an approach for optimal feature selection (FS) in multi-label (ML) data, where each sample can be associated with multiple class labels. The complexity of the feature space is significantly increased in comparison to single-label data, making decision-making challenging. Therefore, FS plays a crucial role in ML classification. The proposed method, called ML-TOPSIS-ACO, leverages the ‘Technique of Order Preference by Similarity to Ideal Solution’ (TOPSIS) as a Multi-Criteria Decision Making (MCDM) technique and ‘Ant Colony Optimization’ (ACO) as a meta-heuristic technique. The ML-TOPSIS-ACO method operates in two phases. In the first phase, Modified TOPSIS (MTOPSIS) is used to perform feature ranking and select the most important features. These selected features are then passed to Modified-ACO for feature re-ranking, aiming to identify the best subset of features for ML classification. To evaluate the effectiveness of ML-TOPSIS-ACO, experiments were conducted on nine benchmark datasets. The experimental results demonstrate that ML-TOPSIS-ACO outperforms other existing methods, achieving a winning percentage of 83%.
DOI: 10.6084/m9.figshare.25314902.v1
title: TOPSIS-ACO based feature selection for multi-label classification

description: This paper introduces an approach for optimal feature selection (FS) in multi-label (ML) data, where each sample can be associated with multiple class labels. The complexity of the feature space is significantly increased in comparison to single-label data, making decision-making challenging. Therefore, FS plays a crucial role in ML classification. The proposed method, called ML-TOPSIS-ACO, leverages the ‘Technique of Order Preference by Similarity to Ideal Solution’ (TOPSIS) as a Multi-Criteria Decision Making (MCDM) technique and ‘Ant Colony Optimization’ (ACO) as a meta-heuristic technique. The ML-TOPSIS-ACO method operates in two phases. In the first phase, Modified TOPSIS (MTOPSIS) is used to perform feature ranking and select the most important features. These selected features are then passed to Modified-ACO for feature re-ranking, aiming to identify the best subset of features for ML classification. To evaluate the effectiveness of ML-TOPSIS-ACO, experiments were conducted on nine benchmark datasets. The experimental results demonstrate that ML-TOPSIS-ACO outperforms other existing methods, achieving a winning percentage of 83%.
DOI: 10.6084/m9.figshare.25400212.v1
title: Adapting face recognition to the masked world: leveraging deep attention networks with a custom dataset

description: In the era of COVID-19, past face recognition algorithms' performance is downgraded due to the partial occlusion of face mask. A new Indian face image dataset has been proposed titled, <i>Handcrafted Indian Face (HIF)</i> dataset, addressing the issues, viz. variegated illumination, pose, and partial occlusion conditions. It bridges the gap between the performance of the DL models used, pre and post COVID-19 effect. A novel idea for choosing the train-test sample has been presented in the paper, which improves the accuracy on existing state of art DL models. In this paper, a new DL architecture has been proposed named the InceptBlock Enhanced Attention Fusion Network (IBEAFNet) which consists of the combination of ECBAM (Enhanced Convolution Block Attention Module) and InceptionV3 architecture. The proposed architecture's attention layer placement allows it to suppress less relevant mask regions of face, while emphasizing on significant fine and coarse level features with reduced complexity. IBEAFNet is trained and tested on two existing datasets, viz. Casia & Yale (including simulated masked images) and the proposed HIF dataset. The performance of IBEAFNet is compared with the results fetched by changing the attention layers in IBEAFNet with the blocks of SENet and CBAM. IBEAFNet outperformed the state-of-art models with the accuracy of 91.00%, 89.5%, and 93.00% on CASIA, Yale, and HIF dataset, respectively.
DOI: 10.6084/m9.figshare.25400212
title: Adapting face recognition to the masked world: leveraging deep attention networks with a custom dataset

description: In the era of COVID-19, past face recognition algorithms' performance is downgraded due to the partial occlusion of face mask. A new Indian face image dataset has been proposed titled, <i>Handcrafted Indian Face (HIF)</i> dataset, addressing the issues, viz. variegated illumination, pose, and partial occlusion conditions. It bridges the gap between the performance of the DL models used, pre and post COVID-19 effect. A novel idea for choosing the train-test sample has been presented in the paper, which improves the accuracy on existing state of art DL models. In this paper, a new DL architecture has been proposed named the InceptBlock Enhanced Attention Fusion Network (IBEAFNet) which consists of the combination of ECBAM (Enhanced Convolution Block Attention Module) and InceptionV3 architecture. The proposed architecture's attention layer placement allows it to suppress less relevant mask regions of face, while emphasizing on significant fine and coarse level features with reduced complexity. IBEAFNet is trained and tested on two existing datasets, viz. Casia & Yale (including simulated masked images) and the proposed HIF dataset. The performance of IBEAFNet is compared with the results fetched by changing the attention layers in IBEAFNet with the blocks of SENet and CBAM. IBEAFNet outperformed the state-of-art models with the accuracy of 91.00%, 89.5%, and 93.00% on CASIA, Yale, and HIF dataset, respectively.
DOI: 10.15468/dl.qxd5u9
title: Occurrence Download

description: A dataset containing 1643 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhabdodendraceae"
 ]
}

 The dataset includes 1643 records from 103 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010435-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26078/bwx2-5p65
title: Supplementary files for "Impact of Snow Accumulation on Structural Integrity: Present and Future Perspectives"

description: Evaluating the impact of weight exerted by settled snow (i.e., snow load) on structures poses numerous statistical challenges, including missing data, biased distribution parameters, and the influence of climate change. This dissertation aims to address challenges related to the use both direct and indirect measurements of snow load (or equivalently, snow water equivalent), as well as the anticipated impact of climate change on future extreme snow loads. The first paper within this dissertation investigates short-term snow loads by comparing various techniques for estimating extreme values of short-term snow accumulations. Additionally, the first paper includes a comparative analysis of short-term and long-term snow accumulations, revealing significant differences in snow load accumulation patterns across geographical regions. The second paper focuses on bias correction in the scale parameter of the generalized extreme value distribution describing extreme snow loads in situations where the snow load is estimated indirectly using snow depth data. The bias correction is accomplished using bootstrap techniques when some of the snow load data is only approximated, rather than directly measured. We demonstrate the effectiveness of our approach in correcting scale parameter bias, as evidenced by simulation studies and real-life snow data. In the third paper, we incorporate the effects of climate change in the snow load estimation process and discuss the implications of considering the effects of climate change in snow load design. Our findings indicate that most locations in the United States have a reduced risk of snow-induced structural failure in a future climate. However, other locations appear to have an increased risk of structure failure, though there is no agreement among climate models as to which areas are at increased risk. Together, these interconnected papers refine methods for characterizing extreme snow accumulations and address the statistical complexities of estimating design snow loads for both current and future conditions.
DOI: 10.57841/casdc.0003939
title: 透平测试数据集
DOI: 10.15468/dl.zh8zjz
title: Occurrence Download

description: A dataset containing 8671 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen, Occurrence evidence)",
 "Geometry POLYGON((-103.76052 20.52638,-103.41157 20.52638,-103.41157 20.90077,-103.76052 20.90077,-103.76052 20.52638))",
 "TaxonKey is Plantae",
 "Year 1980-2024"
 ]
}

 The dataset includes 8671 records from 75 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010436-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dd.mp2vea
title: Biodiversité Quebec Atlas

description: Biodiversité Québec is an integrated scientific monitoring system. It allows better observation, analysis and sharing of the state and changes of biodiversity for better management and conservation of ecosystems in Québec. The strength and innovation of this infrastructure depend on the collective effort to gather information on biodiversity.

Its Atlas data infrastructure aims to integrate and provide access to biodiversity data for the Province of Quebec. It standardizes various data types (abundance, occurrences, surveys, population time-series, and taxonomy) for integration into monitoring and modeling workflows. Data is sourced from open science repositories (e.g., GBIF, eBird, iNaturalist, Living Planet Database) and through direct partnerships with local and national organizations.

All occurences covering the province of Quebec within GBIF are downloaded via API and then filtered to select only those with complete datetime, location, and taxonomy information. These occurrences are then transformed to fit the BQ Atlas observations data model, preserving dataset attributes such as publishers, licenses, and DOIs for citation purposes, as well as maintaining original identifiers from GBIF and the source datasets.

This data is stored on on-premises relational database servers and made available for exploration and download through the Atlas data portal and R packages.
DOI: 10.5522/04/26968909
title: Family experience of gastrointestinal symptoms in autistic children - qualitative data

description: Indexed data from 12 semistructured interviews with parents of Autistic children with persistent gastrointestinal (GI) symptoms exploring the nature of the GI symptoms, family experience of healthcare and family experience of participation in a clinical trial of a probiotic.
DOI: 10.5522/04/26968909.v1
title: Family experience of gastrointestinal symptoms in autistic children - qualitative data

description: Indexed data from 12 semistructured interviews with parents of Autistic children with persistent gastrointestinal (GI) symptoms exploring the nature of the GI symptoms, family experience of healthcare and family experience of participation in a clinical trial of a probiotic.
DOI: 10.5281/zenodo.13742116
title: Data sets and code for Sullivan and Koski "The role of photosynthetic response to environmental variation in shaping an elevational cline in leaf variegation"
DOI: 10.5281/zenodo.13742117
title: Data sets and code for Sullivan and Koski "The role of photosynthetic response to environmental variation in shaping an elevational cline in leaf variegation"