cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.22002/s0vdx-0k302
title: Caltech Mouse Social Interactions (CalMS21) Dataset

description: Multi-agent behavior modeling aims to understand the interactions that occur between agents. We present a multi-agent dataset from behavioral neuroscience, the Caltech Mouse Social Interactions (CalMS21) Dataset. Our dataset consists of trajectory data of social interactions, recorded from videos of freely behaving mice in a standard resident-intruder assay. To help accelerate behavioral studies, the CalMS21 dataset provides benchmarks to evaluate the performance of automated behavior classification methods in three settings: (1) for training on large behavioral datasets all annotated by a single annotator, (2) for style transfer to learn inter-annotator differences in behavior definitions, and (3) for learning of new behaviors of interest given limited training data. The dataset consists of 6 million frames of unlabeled tracked poses of interacting mice, as well as over 1 million frames with tracked poses and corresponding frame-level behavior annotations. The challenge of our dataset is to be able to classify behaviors accurately using both labeled and unlabeled tracking data, as well as being able to generalize to new settings.
description: Our trajectory dataset is available in json format. Please download and unzip the zip files for each task to access the data. Our video datasets are available as mp4 format (task1_videos_mp4.zip) or seq format (task1_videos_seq.zip). Additionally, we release the pre-computed MARS features on our dataset in task1_MARS_features.zip. The readme included for download with the dataset provides a more detailed description of the data format, and a script to read json files and convert them to npy files (Python) is included. See our website for more info: https://sites.google.com/view/computational-behavior/our-datasets/calms21-dataset See our paper for more details on the dataset and benchmarks: https://arxiv.org/abs/2104.02710
description: Please cite the following paper if you find the dataset useful: https://arxiv.org/abs/2104.02710 BibTeX: @article{calms21, title={The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions}, author={Sun, Jennifer J and Karigo, Tomomi and Chakraborty, Dipam and Mohanty, Sharada P and Wild, Benjamin and Sun, Quan and Chen, Chen and Anderson, David J and Perona, Pietro and Yue, Yisong and Kennedy, Ann}, journal={arXiv preprint arXiv:2104.02710}, year={2021} }
DOI: 10.25318/9810025501-eng
title: Shelter-cost-to-income ratio by tenure including presence of mortgage payments and subsidized housing : Canada, provinces and territories, census divisions and census subdivisions

description: Shelter-cost-to-income ratio by tenure including presence of mortgage payments and subsidized housing for Canada, provinces and territories, census divisions and census subdivisions. Includes residence on or off reserve, and household type including census family structure.
DOI: 10.25318/9810025501-fra
title: Rapport des frais de logement au revenu selon le mode d'occupation incluant la présence de paiements hypothécaires et le logement subventionné : Canada, provinces et territoires, divisions de recensement et subdivisions de recensement

description: Rapport des frais de logement au revenu selon le mode d'occupation incluant la présence de paiements hypothécaires et le logement subventionné pour le Canada, les provinces et les territoires, les divisions de recensement et les subdivisions de recensement. Comprend la résidence dans une réserve ou hors réserve, et le type de ménage incluant la structure de la famille de recensement.
DOI: 10.25318/9810025601-eng
title: Value (owner-estimated) of dwelling by structural type of dwelling: Canada, provinces and territories, census metropolitan areas and census agglomerations

description: Value (owner-estimated) of dwelling by structural type of dwelling for Canada, provinces and territories, census metropolitan areas and census agglomerations. Includes age of primary household maintainer, presence of mortgage payments, number of bedrooms and condominium status.
DOI: 10.25318/9810025601-fra
title: Valeur (estimée par le propriétaire) du logement selon le type de construction résidentielle : Canada, provinces et territoires, régions métropolitaines de recensement et agglomérations de recensement

description: Valeur (estimée par le propriétaire) du logement selon le type de construction résidentielle pour le Canada, les provinces et les territoires, les régions métropolitaines de recensement et les agglomérations de recensement. Comprend l'âge du principal soutien du ménage, la présence de paiements hypothécaires, le nombre de chambres à coucher et le statut de copropriété.
DOI: 10.25318/9810025701-eng
title: Value (owner-estimated) of dwelling by structural type of dwelling : Canada, provinces and territories and census subdivisions with a population 5,000 or more

description: Value (owner-estimated) of dwelling by structural type of dwelling for Canada, provinces and territories and census subdivisions with a population 5,000 or more. Includes age of primary household maintainer, presence of mortgage payments and number of bedrooms.
DOI: 10.6084/m9.figshare.21214184
title: Raw data for the prediction of the three-dimensional structures

description: Raw data for the prediction of the three-dimensional structures
DOI: 10.6084/m9.figshare.21214184.v1
title: Raw data for the prediction of the three-dimensional structures

description: Raw data for the prediction of the three-dimensional structures
DOI: 10.6084/m9.figshare.21280152.v1
title: Raw Data

description: the data contains raw data as values of depth of sealer penetration into the dentinal tubules measured by an in built ruler in mm in Confocal Laser Scanning Microscopy. <br> The data contains an image of coronal, middle and apical third sections of the root specimen.
DOI: 10.25318/9810025701-fra
title: Valeur (estimée par le propriétaire) du logement selon le type de construction résidentielle : Canada, provinces et territoires et subdivisions de recensement ayant une population de 5 000 habitants ou plus

description: Valeur (estimée par le propriétaire) du logement selon le type de construction résidentielle pour le Canada, les provinces et les territoires et les subdivisions de recensement ayant une population de 5 000 habitants ou plus. Comprend l'âge du principal soutien du ménage, la présence de paiements hypothécaires et le nombre de chambres à coucher.
DOI: 10.6084/m9.figshare.20536983
title: Optimal Nonparametric Inference with Two-Scale Distributional Nearest Neighbors

description: The weighted nearest neighbors (WNN) estimator has been popularly used as a flexible and easy-to-implement nonparametric tool for mean regression estimation. The bagging technique is an elegant way to form WNN estimators with weights automatically generated to the nearest neighbors (Steele 2009; Biau, Cérou, and Guyader 2010); we name the resulting estimator as the distributional nearest neighbors (DNN) for easy reference. Yet, there is a lack of distributional results for such estimator, limiting its application to statistical inference. Moreover, when the mean regression function has higher-order smoothness, DNN does not achieve the optimal nonparametric convergence rate, mainly because of the bias issue. In this work, we provide an in-depth technical analysis of the DNN, based on which we suggest a bias reduction approach for the DNN estimator by linearly combining two DNN estimators with different subsampling scales, resulting in the novel two-scale DNN (TDNN) estimator. The two-scale DNN estimator has an equivalent representation of WNN with weights admitting explicit forms and some being negative. We prove that, thanks to the use of negative weights, the two-scale DNN estimator enjoys the optimal nonparametric rate of convergence in estimating the regression function under the fourth-order smoothness condition. We further go beyond estimation and establish that the DNN and two-scale DNN are both asymptotically normal as the subsampling scales and sample size diverge to infinity. For the practical implementation, we also provide variance estimators and a distribution estimator using the jackknife and bootstrap techniques for the two-scale DNN. These estimators can be exploited for constructing valid confidence intervals for nonparametric inference of the regression function. The theoretical results and appealing finite-sample performance of the suggested two-scale DNN method are illustrated with several simulation examples and a real data application.
DOI: 10.6084/m9.figshare.20536983.v1
title: Optimal Nonparametric Inference with Two-Scale Distributional Nearest Neighbors

description: The weighted nearest neighbors (WNN) estimator has been popularly used as a flexible and easy-to-implement nonparametric tool for mean regression estimation. The bagging technique is an elegant way to form WNN estimators with weights automatically generated to the nearest neighbors (Steele 2009; Biau, Cérou, and Guyader 2010); we name the resulting estimator as the distributional nearest neighbors (DNN) for easy reference. Yet, there is a lack of distributional results for such estimator, limiting its application to statistical inference. Moreover, when the mean regression function has higher-order smoothness, DNN does not achieve the optimal nonparametric convergence rate, mainly because of the bias issue. In this work, we provide an in-depth technical analysis of the DNN, based on which we suggest a bias reduction approach for the DNN estimator by linearly combining two DNN estimators with different subsampling scales, resulting in the novel two-scale DNN (TDNN) estimator. The two-scale DNN estimator has an equivalent representation of WNN with weights admitting explicit forms and some being negative. We prove that, thanks to the use of negative weights, the two-scale DNN estimator enjoys the optimal nonparametric rate of convergence in estimating the regression function under the fourth-order smoothness condition. We further go beyond estimation and establish that the DNN and two-scale DNN are both asymptotically normal as the subsampling scales and sample size diverge to infinity. For the practical implementation, we also provide variance estimators and a distribution estimator using the jackknife and bootstrap techniques for the two-scale DNN. These estimators can be exploited for constructing valid confidence intervals for nonparametric inference of the regression function. The theoretical results and appealing finite-sample performance of the suggested two-scale DNN method are illustrated with several simulation examples and a real data application.
DOI: 10.6084/m9.figshare.21213563
title: In vitro RAW DATA.xlsx

description: Raw data for encyst, excyst, and adhesion assay.
DOI: 10.5281/zenodo.7150290
title: prietella_phreatophila_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7150289
title: prietella_phreatophila_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7007633
title: Genomic Prediction for the Germplasm Enhancement of Maize Project

description: The Germplasm Enhancement of Maize (GEM) project was initiated in 1993 as a cooperative effort of public and private sector maize breeders to enhance the genetic diversity of the U.S. maize crop. The GEM project selects progeny lines with high topcross yield potential from crosses between elite temperate lines and exotic parents. The GEM project has released hundreds of useful breeding lines based on phenotypic selection within selfing generations and multi-environment yield evaluations of GEM line topcrosses to elite adapted testers. Developing genomic selection (GS) models for the GEM project may contribute to increases in the rate of genetic gain. Here we evaluated the prediction ability of GS models trained on six years of topcross evaluations from the two GEM programs in Raleigh, NC and Ames, IA, documenting prediction abilities ranging from 0.36 to 0.75 for grain yield and from 0.78 to 0.96 for grain moisture when models were cross-validated within program and heterotic group. Predicted genetic gain from genomic selection ranged from 0.95 to 2.58 times the gain from phenotypic selection. Prediction ability across program and heterotic group was generally poorer than within groups. Based on observed genomic relationships between GEM breeding lines and their tropical ancestors, GS for either yield or moisture would reduce recovery of exotic germplasm only slightly. Using GS models trained within-program, the GEM programs should be able to more effectively deliver on its mission to broaden the genetic base of U.S. germplasm.
DOI: 10.5281/zenodo.7150254
title: Genomic Prediction for the Germplasm Enhancement of Maize Project

description: The Germplasm Enhancement of Maize (GEM) project was initiated in 1993 as a cooperative effort of public and private sector maize breeders to enhance the genetic diversity of the U.S. maize crop. The GEM project selects progeny lines with high topcross yield potential from crosses between elite temperate lines and exotic parents. The GEM project has released hundreds of useful breeding lines based on phenotypic selection within selfing generations and multi-environment yield evaluations of GEM line topcrosses to elite adapted testers. Developing genomic selection (GS) models for the GEM project may contribute to increases in the rate of genetic gain. Here we evaluated the prediction ability of GS models trained on six years of topcross evaluations from the two GEM programs in Raleigh, NC and Ames, IA, documenting prediction abilities ranging from 0.36 to 0.75 for grain yield and from 0.78 to 0.96 for grain moisture when models were cross-validated within program and heterotic group. Predicted genetic gain from genomic selection ranged from 0.95 to 2.58 times the gain from phenotypic selection. Prediction ability across program and heterotic group was generally poorer than within groups. Based on observed genomic relationships between GEM breeding lines and their tropical ancestors, GS for either yield or moisture would reduce recovery of exotic germplasm only slightly. Using GS models trained within-program, the GEM programs should be able to more effectively deliver on its mission to broaden the genetic base of U.S. germplasm.
DOI: 10.5281/zenodo.6993868
title: Dataset: Understanding and Detecting Hateful Content using Contrastive Learning

description: This is the dataset released with the paper titled: "<strong>Understanding and Detecting Hateful Content using Contrastive Learning</strong>". We release our dataset in four CSV files. These files contain the textual and visual dataset and the textual and visual ground truth we obtained after our manual annotations. For a detailed description of every <strong><em>column </em></strong>in the CSV structure, along with the type of the <strong><em>value</em></strong>, please read the readme.pdf file provided with this dataset. The images are stored in the zip files. If you find our dataset useful, please cite our paper: <pre><code>@inproceedings{gonzalez2023understanding, title={Understanding and Detecting Hateful Content using Contrastive Learning}, author={Gonz{\'a}lez-Pizarro, Felipe and Zannettou, Savvas}, booktitle={17th International AAAI Conference On Web And Social Media (ICWSM), 2023}, year={2023} } </code></pre> In case of questions, please do not hesitate to contact us: felipegp[at]cs.ubc.ca (https://gonzalezf.github.io)
DOI: 10.5281/zenodo.6993867
title: Dataset: Understanding and Detecting Hateful Content using Contrastive Learning

description: This is the dataset released with the paper titled: "<strong>Understanding and Detecting Hateful Content using Contrastive Learning</strong>". We release our dataset in four CSV files. These files contain the textual and visual dataset and the textual and visual ground truth we obtained after our manual annotations. For a detailed description of every <strong><em>column </em></strong>in the CSV structure, along with the type of the <strong><em>value</em></strong>, please read the readme.pdf file provided with this dataset. The images are stored in the zip files. If you find our dataset useful, please cite our paper: <pre><code>@inproceedings{gonzalez2023understanding, title={Understanding and Detecting Hateful Content using Contrastive Learning}, author={Gonz{\'a}lez-Pizarro, Felipe and Zannettou, Savvas}, booktitle={17th International AAAI Conference On Web And Social Media (ICWSM), 2023}, year={2023} } </code></pre> In case of questions, please do not hesitate to contact us: felipegp[at]cs.ubc.ca (https://gonzalezf.github.io)
DOI: 10.5519/qd.wt7k3ij9
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (189 records)
DOI: 10.34740/kaggle/dsv/4287708
title: CIC-IoT-Dataset2022
DOI: 10.5281/zenodo.7150313
title: xyliphius_kryptos_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7150312
title: xyliphius_kryptos_zanfona_1.fasta.gz

description: .
DOI: 10.5517/ccdc.csd.cc2888tn
title: CCDC 2093331: Experimental Crystal Structure Determination

description: Related Article: Loai Al Sheakh|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.ccw23vw
title: CCDC 806398: Experimental Crystal Structure Determination

description: Related Article: Ashish R. Naik, Christian Näther, Sunder N Dhuri, Kedar U Narvekar, Wolfgang Bensch, Bikshandarkoil  R Srinivasan|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.ccw23wx
title: CCDC 806399: Experimental Crystal Structure Determination

description: Related Article: Ashish R. Naik, Christian Näther, Sunder N Dhuri, Kedar U Narvekar, Wolfgang Bensch, Bikshandarkoil  R Srinivasan|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2c5dts
title: CCDC 2179945: Experimental Crystal Structure Determination

description: Related Article: Yishi Wu|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2c703r
title: CCDC 2181473: Experimental Crystal Structure Determination

description: Related Article: Dieuwertje K. Modder, Mikhail S. Batov, Thayalan Rajeshkumar, Andrzej Sienkiewicz, Ivica Zivkovic, Rosario Scopelliti, Laurent Maron, Marinella Mazzanti|2022|Chemical Science|13|11294|doi:10.1039/D2SC03592H
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.cc2c704s
title: CCDC 2181474: Experimental Crystal Structure Determination

description: Related Article: Dieuwertje K. Modder, Mikhail S. Batov, Thayalan Rajeshkumar, Andrzej Sienkiewicz, Ivica Zivkovic, Rosario Scopelliti, Laurent Maron, Marinella Mazzanti|2022|Chemical Science|13|11294|doi:10.1039/D2SC03592H
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.cc2c705t
title: CCDC 2181475: Experimental Crystal Structure Determination

description: Related Article: Dieuwertje K. Modder, Mikhail S. Batov, Thayalan Rajeshkumar, Andrzej Sienkiewicz, Ivica Zivkovic, Rosario Scopelliti, Laurent Maron, Marinella Mazzanti|2022|Chemical Science|13|11294|doi:10.1039/D2SC03592H
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.cc2c706v
title: CCDC 2181476: Experimental Crystal Structure Determination

description: Related Article: Dieuwertje K. Modder, Mikhail S. Batov, Thayalan Rajeshkumar, Andrzej Sienkiewicz, Ivica Zivkovic, Rosario Scopelliti, Laurent Maron, Marinella Mazzanti|2022|Chemical Science|13|11294|doi:10.1039/D2SC03592H
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.cc2c708x
title: CCDC 2181478: Experimental Crystal Structure Determination

description: Related Article: Dieuwertje K. Modder, Mikhail S. Batov, Thayalan Rajeshkumar, Andrzej Sienkiewicz, Ivica Zivkovic, Rosario Scopelliti, Laurent Maron, Marinella Mazzanti|2022|Chemical Science|13|11294|doi:10.1039/D2SC03592H
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.cc2c7m6g
title: CCDC 2182065: Experimental Crystal Structure Determination

description: Related Article: Lijun He, Chenyang Jiang, Zhuoyue Chen, Dejun Ma, Long Yi, Zhen Xi|2022|Org.Biomol.Chem.|20|7577|doi:10.1039/D2OB01583H
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.cc2c99hh
title: CCDC 2183686: Experimental Crystal Structure Determination

description: Related Article: Sourav Biswas, Anish Kumar Das, Surya Sekhar Manna, Biswarup Pathak, Sukhendu Mandal|2022|Chemical Science|13|11394|doi:10.1039/D2SC04390D
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.