cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/cc3h883
title: CCDC 104044: Experimental Crystal Structure Determination

description: Related Article: F.Robin, F.Mercier, L.Ricard, F.Mathey|1999|Eur.J.Org.Chem.|1999|1091|doi:10.1002/(SICI)1099-0690(199905)1999:5<1091::AID-EJOC1091>3.0.CO;2-1
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/cc3dwzb
title: CCDC 101772: Experimental Crystal Structure Determination

description: Related Article: H.Irngartinger, S.Strack, F.Gredel, A.Dreuw, E.W.Della|1999|Eur.J.Org.Chem.|1999|1253|doi:10.1002/(SICI)1099-0690(199905)1999:5<1253::AID-EJOC1253>3.0.CO;2-M
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/cc3dx0d
title: CCDC 101773: Experimental Crystal Structure Determination

description: Related Article: H.Irngartinger, S.Strack, F.Gredel, A.Dreuw, E.W.Della|1999|Eur.J.Org.Chem.|1999|1253|doi:10.1002/(SICI)1099-0690(199905)1999:5<1253::AID-EJOC1253>3.0.CO;2-M
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/cc3dx1f
title: CCDC 101774: Experimental Crystal Structure Determination

description: Related Article: H.Irngartinger, S.Strack, F.Gredel, A.Dreuw, E.W.Della|1999|Eur.J.Org.Chem.|1999|1253|doi:10.1002/(SICI)1099-0690(199905)1999:5<1253::AID-EJOC1253>3.0.CO;2-M
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/cc3dx2g
title: CCDC 101775: Experimental Crystal Structure Determination

description: Related Article: H.Irngartinger, S.Strack, F.Gredel, A.Dreuw, E.W.Della|1999|Eur.J.Org.Chem.|1999|1253|doi:10.1002/(SICI)1099-0690(199905)1999:5<1253::AID-EJOC1253>3.0.CO;2-M
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/cc3dx3h
title: CCDC 101776: Experimental Crystal Structure Determination

description: Related Article: H.Irngartinger, S.Strack, F.Gredel, A.Dreuw, E.W.Della|1999|Eur.J.Org.Chem.|1999|1253|doi:10.1002/(SICI)1099-0690(199905)1999:5<1253::AID-EJOC1253>3.0.CO;2-M
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: 10.26093/cds/vizier.36970127
title: Atomic line list for 2MASS J05383296-5904280

description: VizieR online Data Catalogue associated with article published in journal Astronomy and Astrophysics with title ' The R-Process Alliance: Hunting for gold in the near-UV spectrum of 2MASS J05383296-5904280.' (bibcode: 2025A&A...697A.127H)
DOI: 10.5517/cc3rlcs
title: CCDC 112045: Experimental Crystal Structure Determination

description: Related Article: H.G.Aurich, M.Soeberdt, K.Harms|1999|Eur.J.Org.Chem.|1999|1249|doi:10.1002/(SICI)1099-0690(199905)1999:5<1249::AID-EJOC1249>3.0.CO;2-P
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/cc3rldt
title: CCDC 112046: Experimental Crystal Structure Determination

description: Related Article: H.G.Aurich, M.Soeberdt, K.Harms|1999|Eur.J.Org.Chem.|1999|1249|doi:10.1002/(SICI)1099-0690(199905)1999:5<1249::AID-EJOC1249>3.0.CO;2-P
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.2wg6oq
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057765-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2cek7e
title: Occurrence Download

description: A dataset containing 1909044346 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1909044346 records from 17988 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017431-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/ed9f0ba5
title: Complex Healthcare Dataset

description: The dataset used in this paper is a complex healthcare dataset, which includes various attributes such as medical coding, laboratory reports, imaging procedures, payment claims, and public health databases.
DOI: 10.5281/zenodo.10134391
title: Predictions for sRNA-mRNA interactions in E. coli - kGraphRNA, GraphRNA, sInterRF, and sInterXGB models

description: Bacterial small RNAs (sRNAs) are pivotal in post-transcriptional regulation, affecting functions like virulence, metabolism, and gene expression by binding specific mRNA targets. Identifying these targets is crucial to understanding sRNA regulation across species. Despite advancements in high-throughput (HT) experimental methods, they remain technically challenging and are limited to detecting sRNA-target interactions under specific environmental conditions. Therefore, computational approaches, especially machine learning (ML), are essential for identifying strong candidates for biological validation. 

In this study, we hypothesize that ML models trained on large-scale interaction data from specific conditions can accurately predict new interactions in unseen conditions within the same bacterial strain. To test this, we developed models from two families: (1) graph neural networks (GNNs), including GraphRNA and kGraphRNA, that learn transformed representations of interacting sRNA-mRNA pairs via graph relationships, and (2) decision forests, sInterRF (Random Forest) and sInterXGB (XGBoost), which use various interaction features for prediction. We also proposed Summation Ensemble Models (SEM) that combine scores from multiple models. Across three seen-to-unseen conditions evaluations, our models —particularly kGraphRNA— significantly improved the area under the ROC curve (AUC) and Precision-Recall curve (PR-AUC) compared to sRNARFTarget, CopraRNA, and RNAup. The SEM model combining GraphRNA and CopraRNA outperformed CopraRNA alone on a low-throughput (LT) interactions test set (HT-to-LT).

In this dataset, we provide the prediction scores of our models: kGraphRNA, GraphRNA, sInterRF, and sInterXGB for any pair of sRNA and mRNA of Escherichia coli K12 MG1655 (NC_000913). We also provide the true labels and the CopraRNA p-value scores computed for all possible pairs. Note that prediction scores are provided for all the unlabeled sRNA-mRNA pairs not included in the HT train set (see our paper for details).

For convenience, each CVS file contains the scores of a single sRNA with the following information: accession IDs, locus tags, and names of the sRNA the mRNA; CopraRNA p-value (if available); the prediction scores of kGraphRNA, GraphRNA, sInterRF, and sInterXGB models; true label (if available) – 1 for interaction and 0 for non-interaction;  whether the sRNA-mRNA pair was included in the train set – true or false; whether the sRNA-mRNA pair was sampled for the train set as a random negative sample – true or false.
DOI: 10.5281/zenodo.14590335
title: Datasets and predictions for sRNA-mRNA interactions in E. coli - kGraphRNA, GraphRNA, sInterRF, and sInterXGB models

description: Bacterial small RNAs (sRNAs) are pivotal in post-transcriptional regulation, affecting functions like virulence, metabolism, and gene expression by binding specific mRNA targets. Identifying these targets is crucial to understanding sRNA regulation across species. Despite advancements in high-throughput (HT) experimental methods, they remain technically challenging and are limited to detecting sRNA-target interactions under specific environmental conditions. Therefore, computational approaches, especially machine learning (ML), are essential for identifying strong candidates for biological validation. 

In this study, we hypothesize that ML models trained on large-scale interaction data from specific conditions can accurately predict new interactions in unseen conditions within the same bacterial strain. To test this, we developed models from two families: (1) graph neural networks (GNNs), including GraphRNA and kGraphRNA, that learn transformed representations of interacting sRNA-mRNA pairs via graph relationships, and (2) decision forests, sInterRF (Random Forest) and sInterXGB (XGBoost), which use various interaction features for prediction. We also proposed Summation Ensemble Models (SEM) that combine scores from multiple models. Across three seen-to-unseen conditions evaluations, our models —particularly kGraphRNA— significantly improved the area under the ROC curve (AUC) and Precision-Recall curve (PR-AUC) compared to sRNARFTarget, CopraRNA, and RNAup. The SEM model combining GraphRNA and CopraRNA outperformed CopraRNA alone on a low-throughput (LT) interactions test set (HT-to-LT).

This data source provides the EcoCyc metadata about all the sRNAs and mRNAs of Escherichia coli K12 MG1655 (NC_000913) and the HT and LT interaction datasets used for our study (see: Data/Datasets). In addition, we provide the prediction scores of our models: kGraphRNA, GraphRNA, sInterRF, and sInterXGB for any pair of sRNA and mRNA of Escherichia coli K12 MG1655 (NC_000913). We also provide the true labels and the CopraRNA p-value scores computed for all possible pairs. Note that prediction scores are not provided for sRNA-mRNA pairs that were used to train the models, i.e., all the labeled interactions (HT and LT) and negative interactions sampled randomly (see our paper for more details).

For convenience, each CVS file contains the scores of a single sRNA with the following information: accession IDs, locus tags, and names of the sRNA the mRNA; CopraRNA p-value (if available); the prediction scores of kGraphRNA, GraphRNA, sInterRF, and sInterXGB models; true label (if available) – 1 for interaction and 0 for non-interaction;  whether the sRNA-mRNA pair was sampled for the train set as a random negative sample – true or false.
DOI: 10.57702/see7kfya
title: Internet Loan dataset

description: The Internet Loan dataset contains information about applicants for internet loans, including credit card information, shopping history, mobile phone bills, credit reports, and fraud information.
DOI: 10.5281/zenodo.10134390
title: Datasets and predictions for sRNA-mRNA interactions in E. coli - kGraphRNA, GraphRNA, sInterRF, and sInterXGB models

description: Bacterial small RNAs (sRNAs) are pivotal in post-transcriptional regulation, affecting functions like virulence, metabolism, and gene expression by binding specific mRNA targets. Identifying these targets is crucial to understanding sRNA regulation across species. Despite advancements in high-throughput (HT) experimental methods, they remain technically challenging and are limited to detecting sRNA-target interactions under specific environmental conditions. Therefore, computational approaches, especially machine learning (ML), are essential for identifying strong candidates for biological validation. 

In this study, we hypothesize that ML models trained on large-scale interaction data from specific conditions can accurately predict new interactions in unseen conditions within the same bacterial strain. To test this, we developed models from two families: (1) graph neural networks (GNNs), including GraphRNA and kGraphRNA, that learn transformed representations of interacting sRNA-mRNA pairs via graph relationships, and (2) decision forests, sInterRF (Random Forest) and sInterXGB (XGBoost), which use various interaction features for prediction. We also proposed Summation Ensemble Models (SEM) that combine scores from multiple models. Across three seen-to-unseen conditions evaluations, our models —particularly kGraphRNA— significantly improved the area under the ROC curve (AUC) and Precision-Recall curve (PR-AUC) compared to sRNARFTarget, CopraRNA, and RNAup. The SEM model combining GraphRNA and CopraRNA outperformed CopraRNA alone on a low-throughput (LT) interactions test set (HT-to-LT).

This data source provides the EcoCyc metadata about all the sRNAs and mRNAs of Escherichia coli K12 MG1655 (NC_000913) and the HT and LT interaction datasets used for our study (see: Data/Datasets). In addition, we provide the prediction scores of our models: kGraphRNA, GraphRNA, sInterRF, and sInterXGB for any pair of sRNA and mRNA of Escherichia coli K12 MG1655 (NC_000913). We also provide the true labels and the CopraRNA p-value scores computed for all possible pairs. Note that prediction scores are not provided for sRNA-mRNA pairs that were used to train the models, i.e., all the labeled interactions (HT and LT) and negative interactions sampled randomly (see our paper for more details).

For convenience, each CVS file contains the scores of a single sRNA with the following information: accession IDs, locus tags, and names of the sRNA the mRNA; CopraRNA p-value (if available); the prediction scores of kGraphRNA, GraphRNA, sInterRF, and sInterXGB models; true label (if available) – 1 for interaction and 0 for non-interaction;  whether the sRNA-mRNA pair was sampled for the train set as a random negative sample – true or false.
DOI: 10.5281/zenodo.14030380
title: Datasets and predictions for sRNA-mRNA interactions in E. coli - kGraphRNA, GraphRNA, sInterRF, and sInterXGB models

description: Bacterial small RNAs (sRNAs) are pivotal in post-transcriptional regulation, affecting functions like virulence, metabolism, and gene expression by binding specific mRNA targets. Identifying these targets is crucial to understanding sRNA regulation across species. Despite advancements in high-throughput (HT) experimental methods, they remain technically challenging and are limited to detecting sRNA-target interactions under specific environmental conditions. Therefore, computational approaches, especially machine learning (ML), are essential for identifying strong candidates for biological validation. 

In this study, we hypothesize that ML models trained on large-scale interaction data from specific conditions can accurately predict new interactions in unseen conditions within the same bacterial strain. To test this, we developed models from two families: (1) graph neural networks (GNNs), including GraphRNA and kGraphRNA, that learn transformed representations of interacting sRNA-mRNA pairs via graph relationships, and (2) decision forests, sInterRF (Random Forest) and sInterXGB (XGBoost), which use various interaction features for prediction. We also proposed Summation Ensemble Models (SEM) that combine scores from multiple models. Across three seen-to-unseen conditions evaluations, our models —particularly kGraphRNA— significantly improved the area under the ROC curve (AUC) and Precision-Recall curve (PR-AUC) compared to sRNARFTarget, CopraRNA, and RNAup. The SEM model combining GraphRNA and CopraRNA outperformed CopraRNA alone on a low-throughput (LT) interactions test set (HT-to-LT).

This data source provides the HT and LT interaction datasets used for our study. In addition, we provide the prediction scores of our models: kGraphRNA, GraphRNA, sInterRF, and sInterXGB for any pair of sRNA and mRNA of Escherichia coli K12 MG1655 (NC_000913). We also provide the true labels and the CopraRNA p-value scores computed for all possible pairs. Note that prediction scores are not provided for sRNA-mRNA pairs that were used to train the models, i.e., all the labeled interactions (HT and LT) and negative interactions sampled randomly (see our paper for more details).

For convenience, each CVS file contains the scores of a single sRNA with the following information: accession IDs, locus tags, and names of the sRNA the mRNA; CopraRNA p-value (if available); the prediction scores of kGraphRNA, GraphRNA, sInterRF, and sInterXGB models; true label (if available) – 1 for interaction and 0 for non-interaction;  whether the sRNA-mRNA pair was sampled for the train set as a random negative sample – true or false.
DOI: 10.57702/fsw21guj
title: Big data and big values: When companies need to rethink themselves

description: The dataset contains more than 94,000 tweets related to the core values of the firms listed in Fortune’s ranking of the World’s Most Admired Companies (2013-2017).
DOI: 10.57702/6ftl1tqp
title: Reﬂection Symmetry Detection in 2D and 3D Point Clouds

description: The dataset used in the paper for reﬂection symmetry detection in 2D and 3D point clouds.
DOI: 10.57702/zcgrh9i5
title: Reﬂection Symmetry Detection in Real Images

description: The dataset used in the paper for reﬂection symmetry detection in real images.
DOI: 10.57702/pf90wldq
title: Reﬂection Symmetry Detection in Real 3D Scans

description: The dataset used in the paper for reﬂection symmetry detection in real 3D scans.
DOI: 10.57702/s0ytlzxl
title: FashionGen [61] and FashionIQ [83]

description: FashionGen [61] for XMR, SCR, and FIC, FashionIQ [83] for TGIR
DOI: 10.57702/9k1i3z2x
title: GoalNet: Goal Areas Oriented Pedestrian Trajectory Prediction

description: Predicting the future trajectories of pedestrians on the road is an important task for autonomous driving. The pedestrian trajectory prediction is affected by scene paths, pedestrian’s intentions and decision-making, which is a multi-modal problem.
DOI: 10.57702/ghaehrx4
title: PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction

description: A large-scale dataset and models for pedestrian intention estimation and trajectory prediction.
DOI: 10.57702/ckk3zftf
title: JAAD: A Benchmark Dataset and Baseline for Pedestrian Crosswalk Behavior

description: A benchmark dataset and baseline for pedestrian crosswalk behavior.
DOI: 10.6073/pasta/0d2d1354a0371acef9932e20afcf82d6
title: Reciprocal Transplant Experiment in a Coastal Forest, Nassawadox, VA

description: In the Mid-Atlantic region, accelerated sea-level rise is provoking rapid shifts in many coastal areas. As salt water intrusion occurs, tidal wetland species are colonizing space opened up by upland dieback and can even invade coastal forest understory prior to said dieback. One such species is the high marsh grass Spartina patens, which is known to exhibit divergent growth forms under the differing conditions of marsh, dune, and swale habitats. It is now colonizing the understory of coastal pine forests experiencing salt water intrusion. I established plots at three sites on the Delmarva peninsula in adjacent marsh and forest areas and conducted a reciprocal transplant experiment to address the following questions: a) How do environmental conditions for S. patens differ between marsh and forest habitats? b) How do S. patens traits differ between these habitats and are these differences a product of plasticity? Our results showed lower salinity and light availability in the forest which corresponded to greater height and leaf area of S. patens plants. Though some traits showed an effect of their habitat of origin, many functional traits exhibited strong phenotypic plasticity in response to environment. This plasticity may prove crucial to the species' resilience to future change.
DOI: 10.57702/1gutvbb2
title: Misinformation Detection Dataset

description: A dataset of 248 well-cited papers in the field of misinformation detection
DOI: 10.57702/ri5smn8o
title: Focus Corpus

description: A dataset of 87 papers with in-depth analysis of method design
DOI: 10.57702/l8ie3cwa
title: Non-random network connectivity comes in pairs

description: The dataset is used to analyze the occurrence of bidirectional connections in local cortical networks.
DOI: 10.57702/9l2unof8
title: Temperature Measurements Dataset

description: The dataset used in the paper is a real-world dataset of temperature measurements from five wireless sensor nodes over five days.
DOI: 10.57702/yv0u0khy
title: ALOS World 3D - 30m (AW3D30)

description: A dataset of pairs of RGB satellite images and their corresponding DEM images.
DOI: 10.57702/cl8cnayb
title: Unsupervised Crowdsourcing with Accuracy and Cost Guarantees

description: The dataset used in this paper is a binary classification task on a multi-class crowdsourcing platform.
DOI: 10.57702/4qnazrbb
title: Path-MNIST

description: A dataset for active semi-supervised learning for class imbalanced datasets
DOI: 10.15468/dl.hshfpp
title: Occurrence Download

description: A dataset containing 1025 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sisyrinchium angustifolium Mill.",
 "Geometry POLYGON((-109 26, -87 26, -87 44, -109 44, -109 26))"
 ]
}

 The dataset includes 1025 records from 71 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055368-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/ja2ecwi7
title: Flexible Stereo: Constrained, Non-Rigid, Wide-Baseline Stereo Vision for Fixed-Wing Aerial Platforms

description: The dataset is used for estimating the time-varying relative pose between two camera-IMU rigs on a flexible wing UAV.
DOI: 10.57702/h65lunj2
title: Phase-Mapper: An AI Platform to Accelerate High Throughput Materials Discovery

description: High-throughput materials discovery involves the rapid synthesis, measurement, and characterization of many different but structurally-related materials. A key problem in materials discovery, the phase map identiﬁcation problem, involves the determination of the crystal phase diagram from the materials composition and structural characterization data.
DOI: 10.57702/d64f6er1
title: IWSLT 2015 English-Vietnamese

description: The IWSLT 2015 English-Vietnamese language data set, which has around 133k training sentence pairs.