cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7054493
title: Small-scale fisheries adaptations understudied in climate change hotspots - database

description: Using a systematic review approach, we identified a global dataset of 301 reported adaptation responses of small-scale fishers to climate change. The adaptations were extracted from academic publications and grey literature (reports and Ph.D. theses) published from 2008 to 2020. The database provides coordinates and/or location, climate change hazard identified as motivating the response, small-scale fisher adaptation response, and any other stressor related to the response.
DOI: 10.5281/zenodo.7097406
title: Small-scale fisheries adaptations understudied in climate change hotspots - database

description: Using a systematic review approach, we identified a global dataset of 301 reported adaptation responses of small-scale fishers to climate change. The adaptations were extracted from academic publications and grey literature (reports and Ph.D. theses) published from 2008 to 2020. The database provides coordinates and/or location, climate change hazard identified as motivating the response, small-scale fisher adaptation response, and any other stressor related to the response.
description: J.G.M. is supported by a Japanese Society for the Promotion of Science (JSPS) KAKENHI Grant Number 19H04322.
DOI: 10.25921/6849-dz26
title: ES60 Water Column Sonar Data Collected During EBS05AL
DOI: 10.5281/zenodo.7097415
title: Local adaptation to hosts and parasitoids shape Hamiltonella defensa genotypes across aphid species - Datasets and scripts

description: Datasets and scripts from "Local adaptation to hosts and parasitoids shape <em>Hamiltonella defensa</em> genotypes across aphid species"
DOI: 10.5281/zenodo.7097414
title: Local adaptation to hosts and parasitoids shape Hamiltonella defensa genotypes across aphid species - Datasets and scripts

description: Datasets and scripts from "Local adaptation to hosts and parasitoids shape <em>Hamiltonella defensa</em> genotypes across aphid species"
DOI: 10.5281/zenodo.7097368
title: NAPS Integrated Data

description: Scripts used to download and clean NAPS Integrated data
DOI: 10.5281/zenodo.7097367
title: NAPS Integrated Data

description: Scripts used to download and clean NAPS Integrated data
DOI: 10.5287/bodleian:broe4obpd
title: Zwitterions fine-tune interactions in electrolyte solutions (data)
DOI: 10.6084/m9.figshare.20343753
title: LESA: Longitudinal Elastic Shape Analysis of Brain Subcortical Structures

description: Over the past 30 years, magnetic resonance imaging has become a ubiquitous tool for accurately visualizing the change and development of the brain’s subcortical structures (e.g., hippocampus). Although subcortical structures act as information hubs of the nervous system, their quantification is still in its infancy due to many challenges in shape extraction, representation, and modeling. Here, we develop a simple and efficient framework of longitudinal elastic shape analysis (LESA) for subcortical structures. Integrating ideas from elastic shape analysis of static surfaces and statistical modeling of sparse longitudinal data, LESA provides a set of tools for systematically quantifying changes of longitudinal subcortical surface shapes from raw structure MRI data. The key novelties of LESA include: (i) it can efficiently represent complex subcortical structures using a small number of basis functions and (ii) it can accurately delineate the spatiotemporal shape changes of the human subcortical structures. We applied LESA to analyze three longitudinal neuroimaging datasets and showcase its wide applications in estimating continuous shape trajectories, building life-span growth patterns, and comparing shape differences among different groups. In particular, with the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, we found that Alzheimer’s Disease (AD) can significantly speed the shape change of the lateral ventricle and the hippocampus from 60 to 75 years olds compared with normal aging. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20343753.v1
title: LESA: Longitudinal Elastic Shape Analysis of Brain Subcortical Structures

description: Over the past 30 years, magnetic resonance imaging has become a ubiquitous tool for accurately visualizing the change and development of the brain’s subcortical structures (e.g., hippocampus). Although subcortical structures act as information hubs of the nervous system, their quantification is still in its infancy due to many challenges in shape extraction, representation, and modeling. Here, we develop a simple and efficient framework of longitudinal elastic shape analysis (LESA) for subcortical structures. Integrating ideas from elastic shape analysis of static surfaces and statistical modeling of sparse longitudinal data, LESA provides a set of tools for systematically quantifying changes of longitudinal subcortical surface shapes from raw structure MRI data. The key novelties of LESA include: (i) it can efficiently represent complex subcortical structures using a small number of basis functions and (ii) it can accurately delineate the spatiotemporal shape changes of the human subcortical structures. We applied LESA to analyze three longitudinal neuroimaging datasets and showcase its wide applications in estimating continuous shape trajectories, building life-span growth patterns, and comparing shape differences among different groups. In particular, with the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, we found that Alzheimer’s Disease (AD) can significantly speed the shape change of the lateral ventricle and the hippocampus from 60 to 75 years olds compared with normal aging. Supplementary materials for this article are available online.
DOI: 10.34943/b9039350-b6c2-456b-a553-42a0a50d9194
title: Victoria Peak Accelerometer Deployed 2018-09-26

description: The Nanometrics Titan SMA (S/N 000839) was deployed on 2018-09-26 at Victoria Peak. Victoria Peak is a mountain located in the Sutton Range of the Vancouver Island Ranges. This device is a Accelerometer. Accelerometers are instruments that measure accelerations. Acceleration can be static such as gravity pulling objects toward the earth, or dynamic as caused by oscillatory movements of the instrument. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/9fcea3e9-dc5f-47a2-9e80-ceb9f2ec2729
title: Victoria Peak Accelerometer Deployed 2018-09-26

description: The Nanometrics Titan SMA (S/N 000839) was deployed on 2018-09-26 at Victoria Peak. Victoria Peak is a mountain located in the Sutton Range of the Vancouver Island Ranges. This device is a Accelerometer. Accelerometers are instruments that measure accelerations. Acceleration can be static such as gravity pulling objects toward the earth, or dynamic as caused by oscillatory movements of the instrument. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 2.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.5281/zenodo.7097455
title: Human influence intensity index

description: The human influence intensity (HII) index proposed by Sanderson et al., (2002) was used. We applied a road distance map (https://www.openstreetmap.org/), a grided population density map from 2015 (1 km × 1 km) (https://www.resdc.cn/), nighttime lights from 2018 (500 m × 500 m) (https://dataverse.harvard.edu/) (Chen et al., 2020) and land use data from 2020 (30 m × 30 m) (http://www.globeland30.org/) to generate the HII at 500 m × 500 m. We assigned scores to population density, roads and railways, land use/ cover, which were summed to quantify the HII, which ranges from 0.05 to 30
DOI: 10.5281/zenodo.7097454
title: Human influence intensity index

description: The human influence intensity (HII) index proposed by Sanderson et al., (2002) was used. We applied a road distance map (https://www.openstreetmap.org/), a grided population density map from 2015 (1 km × 1 km) (https://www.resdc.cn/), nighttime lights from 2018 (500 m × 500 m) (https://dataverse.harvard.edu/) (Chen et al., 2020) and land use data from 2020 (30 m × 30 m) (http://www.globeland30.org/) to generate the HII at 500 m × 500 m. We assigned scores to population density, roads and railways, land use/ cover, which were summed to quantify the HII, which ranges from 0.05 to 30
DOI: 10.5281/zenodo.6578593
title: Right for the Right Reason: Evidence Extraction for Trustworthy Tabular Reasoning

description: This repository contains resources developed for the paper: Gupta, V., Zhang, S., Vempala, A., He, Y., Choji, T., Srikumar V., Right for the Right Reason: Evidence Extraction for Trustworthy Tabular Reasoning. In: Proceeding of the The Association of Computational Linguistic 2022 (ACL ’22), May 2022". <strong>I</strong>t includes the relevant rows marking for the train set of the InfoTabS dataset (https://infotabs.github.io/) Gupta et. al. 2020 [1]. We followed the protocol of Gupta et al. (2022) [2] which annotated the development and test sets (alpha1, alpha2, alpha3) sets: one table and three distinct hypotheses formed a HIT. We divide the tasks equally into 110 batches, each batch having 51 HITs each having three examples. In total, we collected 81,282 annotations from 90 distinct annotators. Overall, twenty five annotators completed over 1000 tasks, corresponding to 87.75 % of the examples, indicating a tail distribution with the annotations. Overall, 16,248 training set table-hypothesis pairs were successfully labeled with the evidence rows. On average, we obtain 89.49% F1-score with equal precision and recall for annotation agreement when compared with majority vote. It also includes an annotation template used on the mTurk platform for crowdsourcing. The cited datasets were used in this work. The cited datasets were used in this work. Files to access the annotation follow the below structure: annotation_batches batches_test: contain final results “.csv” files for all the development and test set batches (taken from Gupta et. al. 2022) batches_train: contain our annotated results “.csv” files for all the train set batches README.md: contain the readme for the annotation batches details main_template_row_relevant.html: content the annotation template used for each HIT i.e. marking the relevant row for each instance annotation_stats.md: Have details of the annotation statistics release_mturk: contain the release batches details i.e. csv for corresponding batches released Files to recreate the annotation statistics and pre-processed data: results_test: contain the pre-processed batch csv for dev and test set each batch. In the dev and test set. The integrated one computes the agreement stats for all the batches.(taken from Gupta et. al. 2022) results_train: similar to resutls_train expect contain the pre-processed batch csv for train set. scripts: contain the scripts needed to create the csv in the results_test and results_train sets. The script title denotes the function (the statistic it computes) for the scripts. src: the scripts use these python files to create the relevant statistics. <strong>References:</strong> [1] InfoTabS: Inference on Tables as Semi-structured Data, Vivek Gupta, Maitrey Mehta, Pegah Nokhiz, Vivek Srikumar, ACL 2020 [2] Is My Model Using The Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning, Vivek Gupta, Riyaz A. Bhat, Atreya Ghosal, Manish Srivastava, Maneesh Singh, Vivek Srikumar, TACL 2022, presented at ACL 2022
DOI: 10.5281/zenodo.6578592
title: Right for the Right Reason: Evidence Extraction for Trustworthy Tabular Reasoning

description: This repository contains resources developed for the paper: Gupta, V., Zhang, S., Vempala, A., He, Y., Choji, T., Srikumar V., Right for the Right Reason: Evidence Extraction for Trustworthy Tabular Reasoning. In: Proceeding of the The Association of Computational Linguistic 2022 (ACL ’22), May 2022". <strong>I</strong>t includes the relevant rows marking for the train set of the InfoTabS dataset (https://infotabs.github.io/) Gupta et. al. 2020 [1]. We followed the protocol of Gupta et al. (2022) [2] which annotated the development and test sets (alpha1, alpha2, alpha3) sets: one table and three distinct hypotheses formed a HIT. We divide the tasks equally into 110 batches, each batch having 51 HITs each having three examples. In total, we collected 81,282 annotations from 90 distinct annotators. Overall, twenty five annotators completed over 1000 tasks, corresponding to 87.75 % of the examples, indicating a tail distribution with the annotations. Overall, 16,248 training set table-hypothesis pairs were successfully labeled with the evidence rows. On average, we obtain 89.49% F1-score with equal precision and recall for annotation agreement when compared with majority vote. It also includes an annotation template used on the mTurk platform for crowdsourcing. The cited datasets were used in this work. The cited datasets were used in this work. Files to access the annotation follow the below structure: annotation_batches batches_test: contain final results “.csv” files for all the development and test set batches (taken from Gupta et. al. 2022) batches_train: contain our annotated results “.csv” files for all the train set batches README.md: contain the readme for the annotation batches details main_template_row_relevant.html: content the annotation template used for each HIT i.e. marking the relevant row for each instance annotation_stats.md: Have details of the annotation statistics release_mturk: contain the release batches details i.e. csv for corresponding batches released Files to recreate the annotation statistics and pre-processed data: results_test: contain the pre-processed batch csv for dev and test set each batch. In the dev and test set. The integrated one computes the agreement stats for all the batches.(taken from Gupta et. al. 2022) results_train: similar to resutls_train expect contain the pre-processed batch csv for train set. scripts: contain the scripts needed to create the csv in the results_test and results_train sets. The script title denotes the function (the statistic it computes) for the scripts. src: the scripts use these python files to create the relevant statistics. <strong>References:</strong> [1] InfoTabS: Inference on Tables as Semi-structured Data, Vivek Gupta, Maitrey Mehta, Pegah Nokhiz, Vivek Srikumar, ACL 2020 [2] Is My Model Using The Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning, Vivek Gupta, Riyaz A. Bhat, Atreya Ghosal, Manish Srivastava, Maneesh Singh, Vivek Srikumar, TACL 2022, presented at ACL 2022
DOI: 10.6084/m9.figshare.21164776
title: Responding to an ultimatum: injecting deadwood at the base of Cerro Poqui Mountain might save the only population of the critically endangered and localized endemic Chilean stag beetle Sclerostomulus nitidus (Coleoptera: Lucanidae)

description: dataset submitted article "Responding to an ultimatum: injecting deadwood at the base of Cerro Poqui Mountain might save the only population of the critically endangered and localized endemic Chilean stag beetle Sclerostomulus nitidus (Coleoptera: Lucanidae)"
DOI: 10.6084/m9.figshare.21164776.v1
title: Responding to an ultimatum: injecting deadwood at the base of Cerro Poqui Mountain might save the only population of the critically endangered and localized endemic Chilean stag beetle Sclerostomulus nitidus (Coleoptera: Lucanidae)

description: dataset submitted article "Responding to an ultimatum: injecting deadwood at the base of Cerro Poqui Mountain might save the only population of the critically endangered and localized endemic Chilean stag beetle Sclerostomulus nitidus (Coleoptera: Lucanidae)"
DOI: 10.5066/p9c7xfvd
title: Estimated low-flow statistics at ungaged stream locations in New Jersey, water year 2021

description: Estimates of various low-flow statistics were computed at 66 ungaged stream locations throughout New Jersey during the 2021 water year using methods in the published reports, 1) Streamflow characteristics and trends in New Jersey, water years 1897-2003 (Watson and others, 2005) and 2) Implementation of MOVE.1, censored MOVE.1, and piecewise MOVE.1 low-flow regressions with applications at partial-record streamgaging stations in New Jersey (Colarullo and others, 2018). The estimates are computed as needed for use in water resources permitting, assessment, and management by the New Jersey Department of Environmental Protection. The data release includes the stream name, location, drainage area, method of estimation, lowest annual and winter average flows, and the 75 percent flow duration computed during the 2021 water year. The data are provided in plain text file and ArcGIS shapefile formats. References for publications cited: - Colarullo, S.J., Sullivan, S.L., and McHugh, A.R., 2018, Implementation of MOVE.1, censored MOVE.1, and piecewise MOVE.1 low-flow regressions with applications at partial-record streamgaging stations in New Jersey: U.S. Geological Survey Open-File Report 2018-1089, 20 p., accessed March 31, 2022, at�https://doi.org/10.3133/ofr20181089. - Watson, K.M., Reiser, R.G., Nieswand, S.P., and Schopp, R.D., 2005, Streamflow characteristics and trends in New Jersey, water years 1897-2003: U.S. Geological Survey Scientific Investigations Report 2005-5105, 131 p., accessed March 31, 2022, at https://doi.org/10.3133/sir20055105.
DOI: 10.6084/m9.figshare.21167152
title: ciconia_ciconia_zanfona_1.fasta.gz

description: ......
DOI: 10.6084/m9.figshare.21167152.v1
title: ciconia_ciconia_zanfona_1.fasta.gz

description: ......
DOI: 10.6084/m9.figshare.20362004.v2
title: Heavy-Tailed Density Estimation

description: A novel statistical method is proposed and investigated for estimating a heavy tailed density under mild smoothness assumptions. Statistical analyses of heavy-tailed distributions are susceptible to the problem of sparse information in the tail of the distribution getting washed away by unrelated features of a hefty bulk. The proposed Bayesian method avoids this problem by incorporating smoothness and tail regularization through a carefully specified semiparametric prior distribution, and is able to consistently estimate both the density function and its tail index at near minimax optimal rates of contraction. A joint, likelihood driven estimation of the bulk and the tail is shown to help improve uncertainty assessment in estimating the tail index parameter and offer more accurate and reliable estimates of the high tail quantiles compared to thresholding methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20362004
title: Heavy-Tailed Density Estimation

description: A novel statistical method is proposed and investigated for estimating a heavy tailed density under mild smoothness assumptions. Statistical analyses of heavy-tailed distributions are susceptible to the problem of sparse information in the tail of the distribution getting washed away by unrelated features of a hefty bulk. The proposed Bayesian method avoids this problem by incorporating smoothness and tail regularization through a carefully specified semiparametric prior distribution, and is able to consistently estimate both the density function and its tail index at near minimax optimal rates of contraction. A joint, likelihood driven estimation of the bulk and the tail is shown to help improve uncertainty assessment in estimating the tail index parameter and offer more accurate and reliable estimates of the high tail quantiles compared to thresholding methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20362004.v1
title: Heavy-Tailed Density Estimation

description: A novel statistical method is proposed and investigated for estimating a heavy tailed density under mild smoothness assumptions. Statistical analyses of heavy-tailed distributions are susceptible to the problem of sparse information in the tail of the distribution getting washed away by unrelated features of a hefty bulk. The proposed Bayesian method avoids this problem by incorporating smoothness and tail regularization through a carefully specified semiparametric prior distribution, and is able to consistently estimate both the density function and its tail index at near minimax optimal rates of contraction. A joint, likelihood driven estimation of the bulk and the tail is shown to help improve uncertainty assessment in estimating the tail index parameter and offer more accurate and reliable estimates of the high tail quantiles compared to thresholding methods.
DOI: 10.6084/m9.figshare.21167545
title: ciconia_nigra_zanfona_1.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.21167545.v1
title: ciconia_nigra_zanfona_1.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.21167677
title: ciconia_stormi_zanfona_1.fasta.gz

description: .....
DOI: 10.6084/m9.figshare.21167677.v1
title: ciconia_stormi_zanfona_1.fasta.gz

description: .....
DOI: 10.34740/kaggle/dsv/4228973
title: SMART-PDM Dataset
DOI: 10.6084/m9.figshare.21167833
title: umls_semantic_type_tree.tsv

description: UMLS Semantic Type Tree
DOI: 10.6084/m9.figshare.21167833.v1
title: umls_semantic_type_tree.tsv

description: UMLS Semantic Type Tree
DOI: 10.5281/zenodo.6651642
title: "Determinants of football players' valuation: a systematic review" datasets

description: 3 interdependent tables are available and are the materials used for a systematic review of the determinants of football players' valuation. - model_specifications is a table where each row represents one of the 111 model specifications included in the systematic review. Characteristics of the article from which specification was retrieved (title, year, authors, journal), attributes of the specification (sample size, population, econometric modeling, etc.), the significance levels of included variables, and the associated coefficients for significant variables. - model_specifications_dictionnary precise the names of the columns of the table model_specifications. - variables_definitions_and_classification is a table that details all the 471 variables used in the 29 articles analysed with definitions quoted from articles when possible and presents a classification of these variables into 6 categories and several subcategories.
DOI: 10.6084/m9.figshare.21656786
title: Crudele_Reboreda_Fiorini_R script final

description: Crudele_Reboreda_Fiorini_R script final
DOI: 10.6084/m9.figshare.21656786.v1
title: Crudele_Reboreda_Fiorini_R script final

description: Crudele_Reboreda_Fiorini_R script final