cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8102201
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Frederik Liebmann, http://www.wikidata.org/entity/Q1335154. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q1335154
DOI: 10.5281/zenodo.8102200
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Frederik Liebmann, http://www.wikidata.org/entity/Q1335154. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q1335154
DOI: 10.5281/zenodo.8102203
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Frode Lieungh, http://www.wikidata.org/entity/Q106868096. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q106868096
DOI: 10.5281/zenodo.8102202
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Frode Lieungh, http://www.wikidata.org/entity/Q106868096. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q106868096
DOI: 10.25919/z1a1-hy86
title: Parkes observations for project PX500 semester 2023APRS_11

description: This proposal relates to time purchased by the Chinese Academy of Sciences for Parkes follow-up confirmation and timing of FAST discoveries. As the observing time has been purchased this proposal (and the related PX501) does not require TAC grading. The primary reason for submitting a proposal is to ensure an outreach statement and that the proposal is entered into OPAL for the data archive to access. The Chinese team have two time requests. The time scheduled for the first, relating to this proposal, will be allocated in blocks of 24 hours and the data will become available in the data archive with an 18 month embargo period. The second type of time will be scheduled in shorter blocks and the data archived with a 10 year embargo.
DOI: 10.48331/scielo.qqp943/7onhqp
title: 2023-AI_and_writing-Krauss-FINAL.pptx

description: :unav
DOI: 10.48331/scielo.qqp943
title: Can I use ChatGPT to generate a fully AI-authored thesis?

description: Presentation by Kirstin Krauss at the SciELO South Africa - SciELO 25 Years Seminar: Artificial intelligence detection tools and editorial decision-making policies. Event page: https://25.scielo.org/en/seminars/south-africa-ai/
DOI: 10.5281/zenodo.8102205
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Albert Lillehammer, http://www.wikidata.org/entity/Q55064923. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q55064923
DOI: 10.5281/zenodo.8102204
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Albert Lillehammer, http://www.wikidata.org/entity/Q55064923. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q55064923
DOI: 10.5281/zenodo.8102211
title: eutonina_indicans_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.8102210
title: eutonina_indicans_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.8102219
title: heterotrichella_gracilis_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.8102218
title: heterotrichella_gracilis_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.6084/m9.figshare.23264259
title: A Scalable Method to Exploit Screening in Gaussian Process Models with Noise

description: A common approach to approximating Gaussian log-likelihoods at scale exploits the fact that precision matrices can be well-approximated by sparse matrices in some circumstances. This strategy is motivated by the <i>screening effect</i>, which refers to the phenomenon in which the linear prediction of a process <i>Z</i> at a point x0 depends primarily on measurements nearest to x0. But simple perturbations, such as iid measurement noise, can significantly reduce the degree to which this exploitable phenomenon occurs. While strategies to cope with this issue already exist and are certainly improvements over ignoring the problem, in this work we present a new one based on the EM algorithm that offers several advantages. While in this work we focus on the application to Vecchia’s approximation (Vecchia), a particularly popular and powerful framework in which we can demonstrate true second-order optimization of M steps, the method can also be applied using entirely matrix-vector products, making it applicable to a very wide class of precision matrix-based approximation methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.23264259.v1
title: A Scalable Method to Exploit Screening in Gaussian Process Models with Noise

description: A common approach to approximating Gaussian log-likelihoods at scale exploits the fact that precision matrices can be well-approximated by sparse matrices in some circumstances. This strategy is motivated by the <i>screening effect</i>, which refers to the phenomenon in which the linear prediction of a process <i>Z</i> at a point x0 depends primarily on measurements nearest to x0. But simple perturbations, such as iid measurement noise, can significantly reduce the degree to which this exploitable phenomenon occurs. While strategies to cope with this issue already exist and are certainly improvements over ignoring the problem, in this work we present a new one based on the EM algorithm that offers several advantages. While in this work we focus on the application to Vecchia’s approximation (Vecchia), a particularly popular and powerful framework in which we can demonstrate true second-order optimization of M steps, the method can also be applied using entirely matrix-vector products, making it applicable to a very wide class of precision matrix-based approximation methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.23064575
title: Approximating Partial Likelihood Estimators via Optimal Subsampling

description: With the growing availability of large-scale biomedical data, it is often time-consuming or infeasible to directly perform traditional statistical analysis with relatively limited computing resources at hand. We propose a fast subsampling method to effectively approximate the full data maximum partial likelihood estimator in Cox’s model, which largely reduces the computational burden when analyzing massive survival data. We establish consistency and asymptotic normality of a general subsample-based estimator. The optimal subsampling probabilities with explicit expressions are determined via minimizing the trace of the asymptotic variance-covariance matrix for a linearly transformed parameter estimator. We propose a two-step subsampling algorithm for practical implementation, which has a significant reduction in computing time compared to the full data method. The asymptotic properties of the resulting two-step subsample-based estimator is also established. Extensive numerical experiments and a real-world example are provided to assess our subsampling strategy. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.23064575.v1
title: Approximating Partial Likelihood Estimators via Optimal Subsampling

description: With the growing availability of large-scale biomedical data, it is often time-consuming or infeasible to directly perform traditional statistical analysis with relatively limited computing resources at hand. We propose a fast subsampling method to effectively approximate the full data maximum partial likelihood estimator in Cox’s model, which largely reduces the computational burden when analyzing massive survival data. We establish consistency and asymptotic normality of a general subsample-based estimator. The optimal subsampling probabilities with explicit expressions are determined via minimizing the trace of the asymptotic variance-covariance matrix for a linearly transformed parameter estimator. We propose a two-step subsampling algorithm for practical implementation, which has a significant reduction in computing time compared to the full data method. The asymptotic properties of the resulting two-step subsample-based estimator is also established. Extensive numerical experiments and a real-world example are provided to assess our subsampling strategy. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.23232455
title: Statistically Valid Variational Bayes Algorithm for Ising Model Parameter Estimation

description: Ising models originated in statistical physics and are widely used in modeling spatial data and computer vision problems. However, statistical inference of this model remains challenging due to intractable nature of the normalizing constant in the likelihood. Here, we use a pseudo-likelihood instead, to study the Bayesian estimation of two-parameter, inverse temperature and magnetization, Ising model with a fully specified coupling matrix. We develop a computationally efficient variational Bayes procedure for model estimation. Under the Gaussian mean-field variational family, we derive posterior contraction rates of the variational posterior obtained under the pseudo-likelihood. We also discuss the loss incurred due to variational posterior over true posterior for the pseudo-likelihood approach. Extensive simulation studies validate the efficacy of mean-field Gaussian and bivariate Gaussian families as the possible choices of the variational family for inference of Ising model parameters. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.23232455.v2
title: Statistically Valid Variational Bayes Algorithm for Ising Model Parameter Estimation

description: Ising models originated in statistical physics and are widely used in modeling spatial data and computer vision problems. However, statistical inference of this model remains challenging due to intractable nature of the normalizing constant in the likelihood. Here, we use a pseudo-likelihood instead, to study the Bayesian estimation of two-parameter, inverse temperature and magnetization, Ising model with a fully specified coupling matrix. We develop a computationally efficient variational Bayes procedure for model estimation. Under the Gaussian mean-field variational family, we derive posterior contraction rates of the variational posterior obtained under the pseudo-likelihood. We also discuss the loss incurred due to variational posterior over true posterior for the pseudo-likelihood approach. Extensive simulation studies validate the efficacy of mean-field Gaussian and bivariate Gaussian families as the possible choices of the variational family for inference of Ising model parameters. Supplementary materials for this article are available online.
DOI: 10.5281/zenodo.8102221
title: lepidozona_retiporosa_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.8102220
title: lepidozona_retiporosa_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5517/ccdc.csd.cc24pxj9
title: CCDC 1987271: Experimental Crystal Structure Determination

description: Related Article: Sung Hwan Park, Sambhu Radhakrishnan, Wanuk Choi, C. Vinod Chandran, Kingsley Christian Kemp, Eric Breynaert, Robert G. Bell, Christine E. A. Kirschhock, Suk Bong Hong|2022|J.Am.Chem.Soc.|144|18054|doi:10.1021/jacs.2c07661
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.cc24pxkb
title: CCDC 1987272: Experimental Crystal Structure Determination

description: Related Article: Sung Hwan Park, Sambhu Radhakrishnan, Wanuk Choi, C. Vinod Chandran, Kingsley Christian Kemp, Eric Breynaert, Robert G. Bell, Christine E. A. Kirschhock, Suk Bong Hong|2022|J.Am.Chem.Soc.|144|18054|doi:10.1021/jacs.2c07661
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.cc24pxlc
title: CCDC 1987273: Experimental Crystal Structure Determination

description: Related Article: Sung Hwan Park, Sambhu Radhakrishnan, Wanuk Choi, C. Vinod Chandran, Kingsley Christian Kemp, Eric Breynaert, Robert G. Bell, Christine E. A. Kirschhock, Suk Bong Hong|2022|J.Am.Chem.Soc.|144|18054|doi:10.1021/jacs.2c07661
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.cc24pxmd
title: CCDC 1987274: Experimental Crystal Structure Determination

description: Related Article: Sung Hwan Park, Sambhu Radhakrishnan, Wanuk Choi, C. Vinod Chandran, Kingsley Christian Kemp, Eric Breynaert, Robert G. Bell, Christine E. A. Kirschhock, Suk Bong Hong|2022|J.Am.Chem.Soc.|144|18054|doi:10.1021/jacs.2c07661
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.cc24pxpg
title: CCDC 1987276: Experimental Crystal Structure Determination

description: Related Article: Sung Hwan Park, Sambhu Radhakrishnan, Wanuk Choi, C. Vinod Chandran, Kingsley Christian Kemp, Eric Breynaert, Robert G. Bell, Christine E. A. Kirschhock, Suk Bong Hong|2022|J.Am.Chem.Soc.|144|18054|doi:10.1021/jacs.2c07661
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.cc2g6frw
title: CCDC 2270308: Experimental Crystal Structure Determination

description: Related Article: Ian. R. Butler, Simon J. Coles, Michael B. Hursthouse|1998|University of Southampton, Crystal Structure Report Archive||768|
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.cc2g6fty
title: CCDC 2270310: Experimental Crystal Structure Determination

description: Related Article: Ian. R. Butler, Simon J. Coles, Michael B. Hursthouse|1998|University of Southampton, Crystal Structure Report Archive||767|
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.cc2g6fw0
title: CCDC 2270312: Experimental Crystal Structure Determination

description: Related Article: Ian. R. Butler, Simon J. Coles, Michael B. Hursthouse|1998|University of Southampton, Crystal Structure Report Archive||754|
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.23232455.v1
title: Statistically valid variational Bayes algorithm for Ising model parameter estimation

description: Ising models originated in statistical physics and are widely used in modeling spatial data and computer vision problems. However, statistical inference of this model remains challenging due to intractable nature of the normalizing constant in the likelihood. Here, we use a pseudo-likelihood instead, to study the Bayesian estimation of two-parameter, inverse temperature and magnetization, Ising model with a fully specified coupling matrix. We develop a computationally efficient variational Bayes procedure for model estimation. Under the Gaussian mean-field variational family, we derive posterior contraction rates of the variational posterior obtained under the pseudo-likelihood. We also discuss the loss incurred due to variational posterior over true posterior for the pseudo-likelihood approach. Extensive simulation studies validate the efficacy of mean-field Gaussian and bivariate Gaussian families as the possible choices of the variational family for inference of Ising model parameters.
DOI: 10.6084/m9.figshare.23165636.v1
title: omicverse-reproducibility processed data

description: Processed data used in the omicverse paper. See here for figure reproducing notebooks:<br> https://github.com/Starlitnightly/omicverse-reproducibility
DOI: 10.5281/zenodo.8102243
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Lim Boo Liat, http://www.wikidata.org/entity/Q21387858. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q21387858
DOI: 10.5281/zenodo.8102242
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Lim Boo Liat, http://www.wikidata.org/entity/Q21387858. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q21387858
DOI: 10.5281/zenodo.8102241
title: mastocarpus_papillatus_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.8102240
title: mastocarpus_papillatus_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.7767713
title: Naturally segregating variants contributing to thermal tolerance in a D. melanogaster model system.

description: Main_Incapacitation.zip and Incapacitation_founders.zip contain raw thermal tolerance scores for individuals measured within the heat box. Each folder is labeled with the RIL or founder ID and replicates within each file are labeled with group numbers. RNAi_files_to_tar.txt contains the metadata for the Combined_tracks_RNAi_1.Rds.zip and Combined_tracks_RNAi_2.Rds.zip. Combined_tracks_RNAi_1.Rds.zip and Combined_tracks_RNAi_2.Rds.zip. contains raw data for RNAi lines measured on the heat plate. plate_finder-kinglab-2021-05-02.zip contains the DeepLabCut model used for finding the corners of aluminum mounting plate used to hold the fly vials for thermal sensitivity testing. This directory contains the training data as well as the trained and evaluated model. No retraining should be necessary for use. fly_tracker_2-king-2021-09-27.zip contains the DeepLabCut model used for tracking individual flies during thermal sensitivity testing. This directory contains the training data as well as the trained and evaluated model. No retraining should be necessary for use. fly_tracker_batch.py is a python (&gt;= 3.0) script that processes the raw movie files collected via the Raspberry Pi. This script uses the plate finder DeepLabCut model to find the corners of the plate, rotate and crop the images, and output movie files for individual flies. It then uses the fly tracker DeepLabCut model to track the flies and output the data for subsequent processing in R.
DOI: 10.5281/zenodo.7767712
title: Naturally segregating variants contributing to thermal tolerance in a D. melanogaster model system.

description: Main_Incapacitation.zip and Incapacitation_founders.zip contain raw thermal tolerance scores for individuals measured within the heat box. Each folder is labeled with the RIL or founder ID and replicates within each file are labeled with group numbers. RNAi_files_to_tar.txt contains the metadata for the Combined_tracks_RNAi_1.Rds.zip and Combined_tracks_RNAi_2.Rds.zip. Combined_tracks_RNAi_1.Rds.zip and Combined_tracks_RNAi_2.Rds.zip. contains raw data for RNAi lines measured on the heat plate. plate_finder-kinglab-2021-05-02.zip contains the DeepLabCut model used for finding the corners of aluminum mounting plate used to hold the fly vials for thermal sensitivity testing. This directory contains the training data as well as the trained and evaluated model. No retraining should be necessary for use. fly_tracker_2-king-2021-09-27.zip contains the DeepLabCut model used for tracking individual flies during thermal sensitivity testing. This directory contains the training data as well as the trained and evaluated model. No retraining should be necessary for use. fly_tracker_batch.py is a python (&gt;= 3.0) script that processes the raw movie files collected via the Raspberry Pi. This script uses the plate finder DeepLabCut model to find the corners of the plate, rotate and crop the images, and output movie files for individual flies. It then uses the fly tracker DeepLabCut model to track the flies and output the data for subsequent processing in R.
DOI: 10.5281/zenodo.8102245
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Karl Gustav Limpricht, http://www.wikidata.org/entity/Q1374979. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q1374979
DOI: 10.5281/zenodo.8102244
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Karl Gustav Limpricht, http://www.wikidata.org/entity/Q1374979. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q1374979