cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.17900102.v2
title: Suplementary Table 1

description: Describe the georeference information of the article "<b>Genotyping-by-sequencing (GBS) to determine the genetic diversity and population structure of <i>Lippia alba</i> "</b><b></b>
DOI: 10.15468/mmfdsk
title: Ischnura praematura sp. nov. (Odonata: Zygoptera: Coenagrionidae): a species from Yunnan (China) whose females mate in the teneral state

description: This dataset contains the digitized treatments in Plazi based on the original journal article Sanmartín-Villar, Iago, Lorenzo-Carballa, M. Olalla, Zhang, Haomiao, Cordero-Rivera, Adolfo (2022): Ischnura praematura sp. nov. (Odonata: Zygoptera: Coenagrionidae): a species from Yunnan (China) whose females mate in the teneral state. Zootaxa 5087 (1): 59-74, DOI: https://doi.org/10.11646/zootaxa.5087.1.3
DOI: 10.5517/ccdc.csd.cc26215j
title: CCDC 2027746: Experimental Crystal Structure Determination

description: Related Article: Guo-Jin Zha, Wei Ji, Zheng-Hang Qi, Wen-Jie Qiu, Ai-Min Li, Dun-Ru Zhu, Su Jing|2022|J.Catalysis|405|313|doi:10.1016/j.jcat.2021.12.007
DOI: 10.5517/ccdc.csd.cc26216k
title: CCDC 2027747: Experimental Crystal Structure Determination

description: Related Article: Guo-Jin Zha, Wei Ji, Zheng-Hang Qi, Wen-Jie Qiu, Ai-Min Li, Dun-Ru Zhu, Su Jing|2022|J.Catalysis|405|313|doi:10.1016/j.jcat.2021.12.007
DOI: 10.5517/ccdc.csd.cc280h5z
title: CCDC 2085840: Experimental Crystal Structure Determination

description: Related Article: Jenna L. Sartucci, Arindam Maity, Manikandan Mohanan, Jeffery Bertke, Miklos Kertesz, Nagarjuna Gavvalapalli|2021|Org.Biomol.Chem.|20|375|doi:10.1039/D1OB02293H
DOI: 10.5517/ccdc.csd.cc280h60
title: CCDC 2085841: Experimental Crystal Structure Determination

description: Related Article: Jenna L. Sartucci, Arindam Maity, Manikandan Mohanan, Jeffery Bertke, Miklos Kertesz, Nagarjuna Gavvalapalli|2021|Org.Biomol.Chem.|20|375|doi:10.1039/D1OB02293H
DOI: 10.5517/ccdc.csd.cc280h71
title: CCDC 2085842: Experimental Crystal Structure Determination

description: Related Article: Jenna L. Sartucci, Arindam Maity, Manikandan Mohanan, Jeffery Bertke, Miklos Kertesz, Nagarjuna Gavvalapalli|2021|Org.Biomol.Chem.|20|375|doi:10.1039/D1OB02293H
DOI: 10.5517/ccdc.csd.cc2873k6
title: CCDC 2092207: Experimental Crystal Structure Determination

description: Related Article: Alina Ivanova, Evgenii Korotaev, Vladislav Komarov, Taisiya Sukhikh, Svetlana Trubina, Lilia Sheludyakova, Sergey Petrov, Alexsei Tikhonov, Ludmila Lavrenova|2022|Inorg.Chim.Acta|532|120746|doi:10.1016/j.ica.2021.120746
DOI: 10.5517/ccdc.csd.cc288r7k
title: CCDC 2093778: Experimental Crystal Structure Determination

description: Related Article: Chen-Fan Zhou, Yun-Qian Zhang, Yong Ling, Liang Ming, Xia Xi, Gong-Qing Liu, Yanan Zhang|2021|Org.Biomol.Chem.|20|420|doi:10.1039/D1OB02196F
DOI: 10.21227/fped-0p25
title: PCB Fault Detection

description: Dataset for detecting faults during PCB manufacturing
DOI: 10.5066/p933l12n
title: Health assessment of invasive northern snakehead in the Potomac River drainage, 2006 - 2017.

description: Data described herein was collected by USGS personnel and the project funded by USGS. Northern snakehead specimens were collected from the Potomac River and its tributaries by regional natural resource management agencies in the course of their field investigations working with this priority invasive aquatic species. Specimens were provided to USGS for the purpose of conducting health related assessments of northern snakehead in the region, with approximately 90 specimens evaluated grossly and histologically for the presence of lesions and abnormalities. Data presented here was collected seasonally (spring through fall) in 2006 and from 2015 to 2017. Data for each specimen includes date and location of collection, morphometrics, gender (if known), and gross abnormalities, and histological lesions observed. This body of data is stored in spreadsheet format.
DOI: 10.6084/m9.figshare.17900102.v3
title: Suplementary Table 1

description: Describe the georeference information of the article "<b>Genotyping-by-sequencing (GBS) to determine the genetic diversity and population structure of <i>Lippia alba</i> "</b><b></b>
DOI: 10.6084/m9.figshare.17910818
title: Telling the wood from the trees: ranking a tree species list to aid urban afforestation in the Amazon

description: These are supplementary files to the manuscript "Telling the wood from the trees: ranking a tree species list to aid urban afforestation in the Amazon" submitted for publication in the Sustainability
DOI: 10.6084/m9.figshare.17910818.v1
title: Telling the wood from the trees: ranking a tree species list to aid urban afforestation in the Amazon

description: These are supplementary files to the manuscript "Telling the wood from the trees: ranking a tree species list to aid urban afforestation in the Amazon" submitted for publication in the Sustainability
DOI: 10.5281/zenodo.5823006
title: Dataset DL Cloning

description: TSV file for T5 training
DOI: 10.5281/zenodo.5823005
title: Dataset DL Cloning

description: TSV file for T5 training
DOI: 10.6084/m9.figshare.16967331
title: A General Framework for Inference on Algorithm-Agnostic Variable Importance

description: In many applications, it is of interest to assess the relative contribution of features (or subsets of features) toward the goal of predicting a response—in other words, to gauge the variable importance of features. Most recent work on variable importance assessment has focused on describing the importance of features within the confines of a given prediction algorithm. However, such assessment does not necessarily characterize the prediction potential of features, and may provide a misleading reflection of the intrinsic value of these features. To address this limitation, we propose a general framework for nonparametric inference on interpretable algorithm-agnostic variable importance. We define variable importance as a population-level contrast between the oracle predictiveness of all available features versus all features except those under consideration. We propose a nonparametric efficient estimation procedure that allows the construction of valid confidence intervals, even when machine learning techniques are used. We also outline a valid strategy for testing the null importance hypothesis. Through simulations, we show that our proposal has good operating characteristics, and we illustrate its use with data from a study of an antibody against HIV-1 infection. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.16967331.v2
title: A General Framework for Inference on Algorithm-Agnostic Variable Importance

description: In many applications, it is of interest to assess the relative contribution of features (or subsets of features) toward the goal of predicting a response—in other words, to gauge the variable importance of features. Most recent work on variable importance assessment has focused on describing the importance of features within the confines of a given prediction algorithm. However, such assessment does not necessarily characterize the prediction potential of features, and may provide a misleading reflection of the intrinsic value of these features. To address this limitation, we propose a general framework for nonparametric inference on interpretable algorithm-agnostic variable importance. We define variable importance as a population-level contrast between the oracle predictiveness of all available features versus all features except those under consideration. We propose a nonparametric efficient estimation procedure that allows the construction of valid confidence intervals, even when machine learning techniques are used. We also outline a valid strategy for testing the null importance hypothesis. Through simulations, we show that our proposal has good operating characteristics, and we illustrate its use with data from a study of an antibody against HIV-1 infection. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.16967331.v1
title: A general framework for inference on algorithm-agnostic variable importance

description: In many applications, it is of interest to assess the relative contribution of features (or subsets of features) toward the goal of predicting a response — in other words, to gauge the variable importance of features. Most recent work on variable importance assessment has focused on describing the importance of features within the confines of a given prediction algorithm. However, such assessment does not necessarily characterize the prediction potential of features, and may provide a misleading reflection of the intrinsic value of these features. To address this limitation, we propose a general framework for nonparametric inference on interpretable algorithm-agnostic variable importance. We define variable importance as a population-level contrast between the oracle predictiveness of all available features versus all features except those under consideration. We propose a nonparametric efficient estimation procedure that allows the construction of valid confidence intervals, even when machine learning techniques are used. We also outline a valid strategy for testing the null importance hypothesis. Through simulations, we show that our proposal has good operating characteristics, and we illustrate its use with data from a study of an antibody against HIV-1 infection.
DOI: 10.6084/m9.figshare.17021845
title: High-Order Joint Embedding for Multi-Level Link Prediction

description: Link prediction infers potential links from observed networks, and is one of the essential problems in network analyses. In contrast to traditional graph representation modeling which only predicts two-way pairwise relations, we propose a novel tensor-based joint network embedding approach on simultaneously encoding pairwise links and hyperlinks onto a latent space, which captures the dependency between pairwise and multi-way links in inferring potential unobserved hyperlinks. The major advantage of the proposed embedding procedure is that it incorporates both the pairwise relationships and subgroup-wise structure among nodes to capture richer network information. In addition, the proposed method introduces a hierarchical dependency among links to infer potential hyperlinks, and leads to better link prediction. In theory we establish the estimation consistency for the proposed embedding approach, and provide a faster convergence rate compared to link prediction using pairwise links or hyperlinks only. Numerical studies on both simulation settings and Facebook ego-networks indicate that the proposed method improves both hyperlink and pairwise link prediction accuracy compared to existing link prediction algorithms. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.17021845.v2
title: High-Order Joint Embedding for Multi-Level Link Prediction

description: Link prediction infers potential links from observed networks, and is one of the essential problems in network analyses. In contrast to traditional graph representation modeling which only predicts two-way pairwise relations, we propose a novel tensor-based joint network embedding approach on simultaneously encoding pairwise links and hyperlinks onto a latent space, which captures the dependency between pairwise and multi-way links in inferring potential unobserved hyperlinks. The major advantage of the proposed embedding procedure is that it incorporates both the pairwise relationships and subgroup-wise structure among nodes to capture richer network information. In addition, the proposed method introduces a hierarchical dependency among links to infer potential hyperlinks, and leads to better link prediction. In theory we establish the estimation consistency for the proposed embedding approach, and provide a faster convergence rate compared to link prediction using pairwise links or hyperlinks only. Numerical studies on both simulation settings and Facebook ego-networks indicate that the proposed method improves both hyperlink and pairwise link prediction accuracy compared to existing link prediction algorithms. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.17021845.v1
title: High-order Joint Embedding for Multi-Level Link Prediction

description: Link prediction infers potential links from observed networks, and is one of the essential problems in network analyses. In contrast to traditional graph representation modeling which only predicts two-way pairwise relations, we propose a novel tensor-based joint network embedding approach on simultaneously encoding pairwise links and hyperlinks onto a latent space, which captures the dependency between pairwise and multi-way links in inferring potential unobserved hyperlinks. The major advantage of the proposed embedding procedure is that it incorporates both the pairwise relationships and subgroup-wise structure among nodes to capture richer network information. In addition, the proposed method introduces a hierarchical dependency among links to infer potential hyperlinks, and leads to better link prediction. In theory we establish the estimation consistency for the proposed embedding approach, and provide a faster convergence rate compared to link prediction utilizing pairwise links or hyperlinks only. Numerical studies on both simulation settings and Facebook ego-networks indicate that the proposed method improves both hyperlink and pairwise link prediction accuracy compared to existing link prediction algorithms.
DOI: 10.6084/m9.figshare.16967319
title: Transport Monte Carlo: High-Accuracy Posterior Approximation via Random Transport

description: In Bayesian applications, there is a huge interest in rapid and accurate estimation of the posterior distribution, particularly for high dimensional or hierarchical models. In this article, we propose to use optimization to solve for a joint distribution (random transport plan) between two random variables, <i>θ</i> from the posterior distribution and <i>β</i> from the simple multivariate uniform. Specifically, we obtain an approximate estimate of the conditional distribution Π(β|θ) as an infinite mixture of simple location-scale changes; applying the Bayes’ theorem, Π(θ|β) can be sampled as one of the reversed transforms from the uniform, with the weight proportional to the posterior density/mass function. This produces independent random samples with high approximation accuracy, as well as nice theoretical guarantees. Our method shows compelling advantages in performance and accuracy, compared to the state-of-the-art Markov chain Monte Carlo and approximations such as variational Bayes and normalizing flow. We illustrate this approach via several challenging applications, such as sampling from multi-modal distribution, estimating sparse signals in high dimension, and soft-thresholding of a graph with a prior on the degrees. Supplementary materials for this article, including the source code and additional comparison with popular alternative algorithms are available on the journal website.
DOI: 10.6084/m9.figshare.16967319.v1
title: Transport Monte Carlo: High-Accuracy Posterior Approximation via Random Transport

description: In Bayesian applications, there is a huge interest in rapid and accurate estimation of the posterior distribution, particularly for high dimensional or hierarchical models. In this article, we propose to use optimization to solve for a joint distribution (random transport plan) between two random variables, <i>θ</i> from the posterior distribution and <i>β</i> from the simple multivariate uniform. Specifically, we obtain an approximate estimate of the conditional distribution Π(β|θ) as an infinite mixture of simple location-scale changes; applying the Bayes’ theorem, Π(θ|β) can be sampled as one of the reversed transforms from the uniform, with the weight proportional to the posterior density/mass function. This produces independent random samples with high approximation accuracy, as well as nice theoretical guarantees. Our method shows compelling advantages in performance and accuracy, compared to the state-of-the-art Markov chain Monte Carlo and approximations such as variational Bayes and normalizing flow. We illustrate this approach via several challenging applications, such as sampling from multi-modal distribution, estimating sparse signals in high dimension, and soft-thresholding of a graph with a prior on the degrees. Supplementary materials for this article, including the source code and additional comparison with popular alternative algorithms are available on the journal website.
DOI: 10.6084/m9.figshare.17912009
title: Machine learning to predict journal article proxy quality scores

description: Data, code and figures from the paper:Can the quality of published academic journal articles be assessed with machine learning?<br>This includes data and figures not included/discussed in the paper.
DOI: 10.6084/m9.figshare.17912009.v1
title: Machine learning to predict journal article proxy quality scores

description: Data, code and figures from the paper:Can the quality of published academic journal articles be assessed with machine learning?<br>This includes data and figures not included/discussed in the paper.
DOI: 10.6073/pasta/8f920f4f7c9634889493c4c1f9189981
title: WSC - Temperature and relative humidity data from 150 locations in and around Madison, Wisconsin from 2012 - 2018

description: To study the urban heat island and other local climatic processes in Madison, Wisconsin, in March 2012, 135 HOBO U23 Pro v2 temperature/relative humidity sensors in RS1 solar shields (Onset Computing) were attached to streetlight and utility poles in and around Madison, Wisconsin. Additional locations were added in 2012 and 2013 for a total of 150 locations. The sensors were installed at a height of 3.5 meters, and they automatically record instantaneous temperature and relative humidity every 15 minutes. This dataset includes all temperature/humidity measurements and a separate file with the coordinates of each measurement location.
DOI: 10.6084/m9.figshare.17912012
title: Dermoscopy project data for repository.xlsx

description: Project evaluating the use of a magnifying loupe device to interpret 10 different skin lesions.<br>
DOI: 10.6084/m9.figshare.17912012.v1
title: Dermoscopy project data for repository.xlsx

description: Project evaluating the use of a magnifying loupe device to interpret 10 different skin lesions.<br>
DOI: 10.5281/zenodo.4479068
title: US_Horror_Trailers_Data_Set

description: This data set contains audio, colour, editing, and motion data for trailers for the fifty highest grossing horror films at the US box office from 2011 to 2015. All trailers were pre-processed to trim MPAA tag screens and YouTube channel promotional materials, and cropped to remove letterbox blanking. The sample is described in the file US_Horror_Trailers_Sample_Summary.csv, which contains the title of the film promoted, the URL for the trailer on YouTube (all URLs were correct as of 27 January 2021), the dimensions of the video file after pre-processing, the number of frames after pre-processing, the native frame rate, and the running time of a trailer after pre-processing in seconds. For each trailer the following information is available: Audio: the time contour of the normalized aggregated power envelope. One csv file per trailer with the naming convention: *_audio.csv Colour: three unnamed columns containing the average colour of each frame as an RGB triplet. One csv file per trailer with the naming convention: *_rgb.csv Motion: total magnitude and horizontal and vertical pixel displacement between consecutive frames. One csv file per trailer with the naming convention: *_motion.csv Editing: Shot length data in seconds. Shot length data is contained in the csv file US_Horror_Trailers_SL_Data.csv, with one column per data. All variables are time ordered from frame/shot/window 1 to <em>n</em> even when no explicit time variable is provided. A guide to visualising the data in this data set is available online here: https://rpubs.com/nr62_rp33/visualizing_trailers.
description: A data paper describing this data set can be accessed at: Redfern, N. (2021) A Data Set for US Horror Film Trailers, Research Data Journal for the Humanities, and the Social Sciences 6 (1): 1-12. https://doi.org/10.1163/24523666-bja10017.
DOI: 10.5281/zenodo.4479067
title: US_Horror_Trailers_Data_Set

description: This data set contains audio, colour, editing, and motion data for trailers for the fifty highest grossing horror films at the US box office from 2011 to 2015. All trailers were pre-processed to trim MPAA tag screens and YouTube channel promotional materials, and cropped to remove letterbox blanking. The sample is described in the file US_Horror_Trailers_Sample_Summary.csv, which contains the title of the film promoted, the URL for the trailer on YouTube (all URLs were correct as of 27 January 2021), the dimensions of the video file after pre-processing, the number of frames after pre-processing, the native frame rate, and the running time of a trailer after pre-processing in seconds. For each trailer the following information is available: Audio: the time contour of the normalized aggregated power envelope. One csv file per trailer with the naming convention: *_audio.csv Colour: three unnamed columns containing the average colour of each frame as an RGB triplet. One csv file per trailer with the naming convention: *_rgb.csv Motion: total magnitude and horizontal and vertical pixel displacement between consecutive frames. One csv file per trailer with the naming convention: *_motion.csv Editing: Shot length data in seconds. Shot length data is contained in the csv file US_Horror_Trailers_SL_Data.csv, with one column per data. All variables are time ordered from frame/shot/window 1 to <em>n</em> even when no explicit time variable is provided. A guide to visualising the data in this data set is available online here: https://rpubs.com/nr62_rp33/visualizing_trailers.
description: A data paper describing this data set can be accessed at: Redfern, N. (2021) A Data Set for US Horror Film Trailers, Research Data Journal for the Humanities, and the Social Sciences 6 (1): 1-12. https://doi.org/10.1163/24523666-bja10017.
DOI: 10.25345/c5d007
title: MassIVE MSV000088642 - Gut microbiome metabolites present in lactation and pregnancy stages
title: This data belongs to human gut microbiome metabolites associated with pregnancy and lactation stages
DOI: 10.25345/c57z9r
title: MassIVE MSV000088643 - Analysis of proteomic consequences of TYROBP deletion in a Huntington's disease mouse model.
title: Analysis of proteomic consequences of Tyrobp deletion in a Huntington's disease mouse model. Examination of protein expression alterations in the striatum of mutant huntingtin-Q175 with and without TYROBP (protein tyrosine kinase-binding protein).
DOI: 10.5281/zenodo.5816258
title: WWV 10 MHz Doppler Recording, AD8Y

description: 10 MHz data collected by AD8Y during 2020 and 2021. Details at www.hamsci.org/grape1. Timelapse available here: https://www.youtube.com/watch?v=U-0qZsqzNFE
DOI: 10.34740/kaggle/dsv/3009223
title: Omicron daily cases by country (COVID-19 variant)
DOI: 10.6084/m9.figshare.14903262
title: Biogemma BALANCE MAGIC 600K SNP Genotype Data

description: Affymetrix 600K SNP array genotype data for 344 MAGIC DH lines, 16 founders, and the tester, MBS847
DOI: 10.6084/m9.figshare.14903262.v1
title: Biogemma BALANCE MAGIC 600K SNP Genotype Data

description: Affymetrix 600K SNP array genotype data for 344 MAGIC DH lines, 16 founders, and the tester, MBS847
DOI: 10.5517/ccdc.csd.cc25yh2q
title: CCDC 2024333: Experimental Crystal Structure Determination

description: Related Article: Antoine Barthélemy, Kim Glootz, Harald Scherer, Annaleah Hanske, Ingo Krossing|2021|Chemical Science|13|439|doi:10.1039/D1SC05331K
DOI: 10.25346/s6/sjgrky/wjvfj3
title: islnd.nc
DOI: 10.25346/s6/sjgrky/2gishb
title: pixel_ivt_limit.nc
DOI: 10.25346/s6/sjgrky/rw0mme
title: target.m
DOI: 10.25346/s6/sjgrky/bfts6i
title: ivt_example.nc
DOI: 10.25346/s6/sjgrky/qsiwlo
title: README.txt