cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19345022
title: Mann-Whitney U

description: Table 4: Mann-Whitney U test for comparing the surface characteristics of Damon and Pactive brackets (N=9 for each bracket type), showing non-significant differences between the frequencies of bracket gate smoothness/roughness scores despite higher frequencies of smoother Damon bracket gates.
DOI: 10.25739/w8yx-ad22
title: Data from the Development Evolution of a Vehicle for Custom Control

description: In order to develop custom controllers intended to operate vehicles on a live highway, a series of data collection- focused tests were performed at increasing stages of complexity. Modern vehicles with features like Adaptive Cruise Control (ACC) feature a rich set of sensors and drive-by-wire mech- anisms. The presented stages of data collection begins with the analysis of raw data provided by various vehicles, and eventually results in spoofing Controller Area Network (CAN) protocols for sending control commands to operate a vehicle. This paper covers the data and technical efforts needed at various stages. The raw data and tools to plot the data are also publicly available.
DOI: 10.6084/m9.figshare.19349171
title: 14DatasetsList

description: Number of participants in 14 public sEMG datasets
DOI: 10.5517/ccdc.csd.cc205jh7
title: CCDC 1852358: Experimental Crystal Structure Determination

description: Related Article: Oleg V. Kulikov, Arshad Mehmood, Yulia V. Sevryugina|2022|Results Mater.|13|100262|doi:10.1016/j.rinma.2022.100262
DOI: 10.5517/ccdc.csd.cc205jk9
title: CCDC 1852360: Experimental Crystal Structure Determination

description: Related Article: Oleg V. Kulikov, Arshad Mehmood, Yulia V. Sevryugina|2022|Results Mater.|13|100262|doi:10.1016/j.rinma.2022.100262
DOI: 10.5517/ccdc.csd.cc20shjv
title: CCDC 1870587: Experimental Crystal Structure Determination

description: Related Article: Meng Qinghua|2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc20shkw
title: CCDC 1870588: Experimental Crystal Structure Determination

description: Related Article: Meng Qinghua|2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26jft0
title: CCDC 2041592: Experimental Crystal Structure Determination

description: Related Article: Shuxia Cao, Kehui Han, Yanyan Wang, Yanchu Guo, Jun Wang, Xiaohui You, Yufen zhao|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26jgcl
title: CCDC 2041609: Experimental Crystal Structure Determination

description: Related Article: Shuxia Cao, Kehui Han, Yanyan Wang, Yanchu Guo, Jun Wang, Xiaohui You, Yufen zhao|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc285t3d
title: CCDC 2090953: Experimental Crystal Structure Determination

description: Related Article: Shuai Wang, Xin Li, Shengnan Jin, Kang Liu, Cong Dong, Jianke Su, Qiuling Song|2022|Org.Chem.Front.|9|1282|doi:10.1039/D1QO01899J
DOI: 10.5281/zenodo.6347205
title: Inventaires anciens d'archives ecclésiastiques (Paris, Saint-Germain-des-Prés, Saint-Denis)

description: Voir: https://www.siv.archives-nationales.culture.gouv.fr/siv/rechercheconsultation/consultation/ir/consultationIR.action?formCaller=GENERALISTE&irId=FRAN_IR_058159
DOI: 10.5281/zenodo.6347204
title: Inventaires anciens d'archives ecclésiastiques (Paris, Saint-Germain-des-Prés, Saint-Denis)

description: Voir: https://www.siv.archives-nationales.culture.gouv.fr/siv/rechercheconsultation/consultation/ir/consultationIR.action?formCaller=GENERALISTE&irId=FRAN_IR_058159
DOI: 10.6084/m9.figshare.19111207
title: Proper Scoring Rules for Evaluating Density Forecasts with Asymmetric Loss Functions

description: This article proposes a novel asymmetric continuous probabilistic score (ACPS) for evaluating and comparing density forecasts. It generalizes the proposed score and defines a weighted version, which emphasizes regions of interest, such as the tails or the center of a variable’s range. The (weighted) ACPS extends the symmetric (weighted) CRPS by allowing for asymmetries in the preferences underlying the scoring rule. A test is used to statistically compare the predictive ability of different forecasts. The ACPS is of general use in any situation where the decision-maker has asymmetric preferences in the evaluation of the forecasts. In an artificial experiment, the implications of varying the level of asymmetry in the ACPS are illustrated. Then, the proposed score and test are applied to assess and compare density forecasts of macroeconomic relevant datasets (U.S. employment growth) and of commodity prices (oil and electricity prices) with particular focus on the recent COVID-19 crisis period.
DOI: 10.6084/m9.figshare.19111207.v2
title: Proper Scoring Rules for Evaluating Density Forecasts with Asymmetric Loss Functions

description: This article proposes a novel asymmetric continuous probabilistic score (ACPS) for evaluating and comparing density forecasts. It generalizes the proposed score and defines a weighted version, which emphasizes regions of interest, such as the tails or the center of a variable’s range. The (weighted) ACPS extends the symmetric (weighted) CRPS by allowing for asymmetries in the preferences underlying the scoring rule. A test is used to statistically compare the predictive ability of different forecasts. The ACPS is of general use in any situation where the decision-maker has asymmetric preferences in the evaluation of the forecasts. In an artificial experiment, the implications of varying the level of asymmetry in the ACPS are illustrated. Then, the proposed score and test are applied to assess and compare density forecasts of macroeconomic relevant datasets (U.S. employment growth) and of commodity prices (oil and electricity prices) with particular focus on the recent COVID-19 crisis period.
DOI: 10.6084/m9.figshare.19111207.v1
title: Proper scoring rules for evaluating density forecasts with asymmetric loss functions

description: This paper proposes a novel asymmetric continuous probabilistic score (ACPS) for evaluating and comparing density forecasts. It generalizes the proposed score and defines a weighted version, which emphasizes regions of interest, such as the tails or the center of a variable’s range. The (weighted) ACPS extends the symmetric (weighted) CRPS by allowing for asymmetries in the preferences underlying the scoring rule. A test is used to statistically compare the predictive ability of different forecasts. The ACPS is of general use in any situation where the decision-maker has asymmetric preferences in the evaluation of the forecasts. In an artificial experiment, the implications of varying the level of asymmetry in the ACPS are illustrated. Then, the proposed score and test are applied to assess and compare density forecasts of macroeconomic relevant datasets (US employment growth) and of commodity prices (oil and electricity prices) with particular focus on the recent COVID-19 crisis period.
DOI: 10.5066/p9ab0ta7
title: Compilation of geologic slip rate constraints used in 1996-2014 U.S. National Seismic Hazard Models (ver. 2.0, February 2022)

description: A key input for probabilistic seismic hazard analysis (PSHA) is geologic slip rate data. Here, we compile all geologic slip rates that are reportedly used in U.S. National Seismic Hazard Map (NSHM) releases from 1996, 2002, 2007, 2008, and 2014. Although a new NSHM was released in 2018, no changes were made in geologic slip rate data used. The geologic slip rates are collated from existing NSHM reports and documentation, and no new data are reported herein. The geologic slip rates are coupled with the fault geometries used in NSHM2014/2018 calculations. The data are presented spatially as a shapefile (SHP), in keyhole markup language (KML) and geoJSON. A readme file accompanies this dataset explaining details of the data compilation. This data release corrects minor typographical errors in the initial compilation of geologic slip rates used in U.S. National Seismic Hazard Models (1996-2014). This updated version also provides a link to the NSHM2023 fault sections database via FaultID. The original compilation (version 1, published August 2020) is now superseded by this edited compilation (version 2, published February 2022). To compare changes between release, a 'Version Changes' file is included in this Data Release.
DOI: 10.3886/e164762
title: Implications of Epidemic-Pandemic Convergences for Routine Care Adoption of Contingency Management: A Case Study

description: To bridge the gap between opioid overdoses and treatment entry a smartphone-smart debit card contingency management program was developed to promote entry into medication-assisted treatment by out-of-treatment opioid users who have recently received care in a hospital emergency department. The case study described in this manuscript highlights the successful entry into medication assisted treatment despite numerous disruptions related to COVID-19 that would have made engagement difficult without the remote access to CM provided by this technology. The attached data includes the participant's earnings across the duration of the study including the cumulative earnings, and a breakdown of earnings across interviews, enrollment bonuses, downloading the CM platform, and working with their peer recovery coach. Notes include study events that occurred during their participation.
DOI: 10.3886/e164762v1
title: Implications of Epidemic-Pandemic Convergences for Routine Care Adoption of Contingency Management: A Case Study

description: To bridge the gap between opioid overdoses and treatment entry a smartphone-smart debit card contingency management program was developed to promote entry into medication-assisted treatment by out-of-treatment opioid users who have recently received care in a hospital emergency department. The case study described in this manuscript highlights the successful entry into medication assisted treatment despite numerous disruptions related to COVID-19 that would have made engagement difficult without the remote access to CM provided by this technology. The attached data includes the participant's earnings across the duration of the study including the cumulative earnings, and a breakdown of earnings across interviews, enrollment bonuses, downloading the CM platform, and working with their peer recovery coach. Notes include study events that occurred during their participation.
DOI: 10.5281/zenodo.6347988
title: Tabla. Búsqueda de palabras claves en WOS, Scopus, Scielo

description: Tabla. Búsqueda de palabras claves en WOS, Scopus, Scielo. Fecha 28/02/2022<br>
DOI: 10.5281/zenodo.6347987
title: Tabla. Búsqueda de palabras claves en WOS, Scopus, Scielo

description: Tabla. Búsqueda de palabras claves en WOS, Scopus, Scielo. Fecha 28/02/2022<br>
DOI: 10.18739/a2ng4gt0z
title: Shelfbreak Upwelling in the Western Beaufort Sea, Zooplankton Morphometrics data for August-September 2018

description: Atmospherically-forced wind-induced upwelling along the shelf break leads to enhanced feeding opportunities for intermediate links in the pelagic ecosystem that in turn sustain the exploitation of this environment by animals such as beluga, seabirds, and seals. The Beaufort Sea shelf break is a hotspot for upper trophic level animals because elevated numbers and biomass of large, high-energy zooplankton (e.g., lipid-rich copepods, euphausiids) are regularly upwelled from deeper water onto the shelf during winds from the east, retained there by frontal features when upwelling relaxes, and exploited by bowhead whales, seabirds, and forage fish that in turn are prey for piscivorous marine mammals, such as beluga whales. The zooplankton morphometric measurements were determined for dominant copepods and euphausiids collected with a MOCNESS (Multiple Opening/Closing Net and Environmental Sensing System). Data were acquired as part of the National Science Foundation (NSF)-funded project "The Importance of Shelf Break Upwelling to Upper Trophic Level Ecology in the Western Beaufort Sea". Net samples were collected within the box 165 West (W) - 145 W, 69 North (N) - 72N between 6 August and 24 August 2018. PIs: Carin Ashjian (WHOI), Robert Campbell (URI), Michael Jech (NOAA), Joel Llopiz (WHOI), Michael Lowe (WHOI), Stephen Okkonen (UAF), Kathleen Stafford (APL-UW), Jinlun Zhang (APL-UW)
DOI: 10.5281/zenodo.1403244
title: Aburra valley topography

description: Topography of the Aburrá valley in Antioquia, Colombia. Distances are in meters.
DOI: 10.5281/zenodo.1403243
title: Aburra valley topography

description: Topography of the Aburrá valley in Antioquia, Colombia. Distances are in meters.
DOI: 10.5281/zenodo.5548475
title: Dataset of traffic accidents reported on Twitter Bogotá Colombia

description: <strong>1 Classification Dataset</strong> This dataset for the classification model contains 3,804 tweets, where 1,902 are related to traffic accident reports (TA, positive class) and 1,902 are unrelated (NTA, negative class). For training the tweet classification model, a collaborative labeling strategy was designed. Here, 30 people labeled data according to the instructions given. Each participant had to evaluate a tweet to manually classify it into one of three categories defined as: traffic accident related, unrelated and don´t know/no response. Each tweet was evaluated by 3 participants. The correct label was selected by voting; the 3 people must agree on the selected label, otherwise the tweet was excluded from training. This process took a month and required the development and deployment of a web application. <strong>2 NER Dataset (Named Entity Recognition)</strong> For the entity recognition model training, a sample of the filtered tweets resulting from the previous classification phase was taken. 1,340 tweets were extracted, where 800 are from “unofficial” users, almost 60% of the sample. These tweets were user reports on traffic incident occurred in Bogota from October 2018 to July 2019, including other tweets that contained some location references such as reports on the state of road infrastructure; some tweets from the years 2016 and 2017 were also included. Although these posts were not related to accidents per se, they were selected because they contained location information. The purpose was to train a model that would recognize these entities, because a classifier of accident-related tweets was previously created. Additionally, the dataset was split, reserving 1,072 tweets for training and 268 for evaluation. This dataset was manually labeled using the IOB (Inside-outside- beginning) format. The labeling tool called Brat Annotation Tools was used for this task. The labels defined are Location, which refers to the location of the report; and Time, which refers to the time or date of the incident. Accordingly, 5 labels were generated: B-loc, I-loc, B-time, I-time and O. The O label refers to Others. <strong>3 Traffic accident Twitter geolocation</strong> A dataset with 26362 traffic accident tweets with the coordinates of the incident and the date of publication.
DOI: 10.5281/zenodo.5548474
title: Dataset of traffic accidents reported on Twitter Bogotá Colombia

description: <strong>1 Classification Dataset</strong> This dataset for the classification model contains 3,804 tweets, where 1,902 are related to traffic accident reports (TA, positive class) and 1,902 are unrelated (NTA, negative class). For training the tweet classification model, a collaborative labeling strategy was designed. Here, 30 people labeled data according to the instructions given. Each participant had to evaluate a tweet to manually classify it into one of three categories defined as: traffic accident related, unrelated and don´t know/no response. Each tweet was evaluated by 3 participants. The correct label was selected by voting; the 3 people must agree on the selected label, otherwise the tweet was excluded from training. This process took a month and required the development and deployment of a web application. <strong>2 NER Dataset (Named Entity Recognition)</strong> For the entity recognition model training, a sample of the filtered tweets resulting from the previous classification phase was taken. 1,340 tweets were extracted, where 800 are from “unofficial” users, almost 60% of the sample. These tweets were user reports on traffic incident occurred in Bogota from October 2018 to July 2019, including other tweets that contained some location references such as reports on the state of road infrastructure; some tweets from the years 2016 and 2017 were also included. Although these posts were not related to accidents per se, they were selected because they contained location information. The purpose was to train a model that would recognize these entities, because a classifier of accident-related tweets was previously created. Additionally, the dataset was split, reserving 1,072 tweets for training and 268 for evaluation. This dataset was manually labeled using the IOB (Inside-outside- beginning) format. The labeling tool called Brat Annotation Tools was used for this task. The labels defined are Location, which refers to the location of the report; and Time, which refers to the time or date of the incident. Accordingly, 5 labels were generated: B-loc, I-loc, B-time, I-time and O. The O label refers to Others. <strong>3 Traffic accident Twitter geolocation</strong> A dataset with 26362 traffic accident tweets with the coordinates of the incident and the date of publication.
DOI: 10.18739/a2hq3s06m
title: The climate envelope of Alaska’s northern treelines: implications for controlling factors and future treeline advance. Mapping seasonal climate variables 2019 - 2021.

description: Understanding the key mechanisms that control northern treelines is important to accurately predict biome shifts and terrestrial feedbacks to climate. At a global scale, it has long been observed that elevational and latitudinal treelines occur at similar mean growing season air temperature (GSAT) isotherms, inspiring the growth limitation hypothesis (GLH) that cold GSAT limits aboveground growth of treeline trees, with mean treeline GSAT ~6-7 degrees celsius (°C). Treelines with mean GSAT warmer than 6-7 °C may indicate other limiting factors. Many treelines globally are not advancing despite warming, and other climate variables are rarely considered at broad scales. Our goals were to test whether current boreal treelines in northern Alaska correspond with the GLH isotherm, determine which environmental factors are most predictive of treeline presence, and to identify areas beyond the current treeline where advance is most likely. We digitized ~12,400 kilometers (km) of treelines (greater than 26K (26,000) points) and computed seasonal climate variables across northern Alaska. We then built a generalized additive model predicting treeline presence to identify key factors determining treeline. Two metrics of mean GSAT at Alaska’s northern treelines were consistently warmer than the 6-7 °C isotherm (means of 8.5 °C and 9.3 °C), indicating that direct physiological limitation from low GSAT is unlikely to explain the position of treelines in northern Alaska. Our final model included cumulative growing degree-days, near-surface (≤ 1 meters (m)) permafrost probability, and growing season total precipitation, which together may represent the importance of soil temperature. Our results indicate that mean GSAT may not be the primary driver of treeline in northern Alaska or that its effect is mediated by other more proximate, and possibly non-climatic, controls. Our model predicts treeline potential in several areas beyond current treelines, pointing to possible routes of treeline advance if unconstrained by non-climatic factors.
DOI: 10.5281/zenodo.6347221
title: Data sets used in "Neural network processing of holographic images"

description: Included are the training, validation, and testing data sets for synthetic holograms (netCDF), the HOLODEC data set containing the RF07 examples (netCDF), and the two splits of manually labeled HOLODEC image tiles (numpy arrays). The source code for using the data sets can be found at https://github.com/NCAR/holodec-ml
DOI: 10.18739/a2cz3261c
title: The climate envelope of Alaska’s northern treelines: implications for controlling factors and future treeline advance. Primary data and analyses 2019 - 2021

description: Understanding the key mechanisms that control northern treelines is important to accurately predict biome shifts and terrestrial feedbacks to climate. At a global scale, it has long been observed that elevational and latitudinal treelines occur at similar mean growing season air temperature (GSAT) isotherms, inspiring the growth limitation hypothesis (GLH) that cold GSAT limits aboveground growth of treeline trees, with mean treeline GSAT ~6-7 degrees celsius (°C). Treelines with mean GSAT warmer than 6-7 °C may indicate other limiting factors. Many treelines globally are not advancing despite warming, and other climate variables are rarely considered at broad scales. Our goals were to test whether current boreal treelines in northern Alaska correspond with the GLH isotherm, determine which environmental factors are most predictive of treeline presence, and to identify areas beyond the current treeline where advance is most likely. We digitized ~12,400 kilometers (km) of treelines (greater than 26K points) and computed seasonal climate variables across northern Alaska. We then built a generalized additive model predicting treeline presence to identify key factors determining treeline. Two metrics of mean GSAT at Alaska’s northern treelines were consistently warmer than the 6-7 °C isotherm (means of 8.5 °C and 9.3 °C), indicating that direct physiological limitation from low GSAT is unlikely to explain the position of treelines in northern Alaska. Our final model included cumulative growing degree-days, near-surface (≤ 1 meters (m)) permafrost probability, and growing season total precipitation, which together may represent the importance of soil temperature. Our results indicate that mean GSAT may not be the primary driver of treeline in northern Alaska or that its effect is mediated by other more proximate, and possibly non-climatic, controls. Our model predicts treeline potential in several areas beyond current treelines, pointing to possible routes of treeline advance if unconstrained by non-climatic factors.
DOI: 10.7284/111735
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise BH10-13 using a Sea-Bird SBE-911+ instrument system onboard the platform RV Blue Heron

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/111736
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise BH10-14 using a Sea-Bird SBE-911+ instrument system onboard the platform RV Blue Heron

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/111749
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise BH11-09 using a Sea-Bird SBE-911+ instrument system onboard the platform RV Blue Heron

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/111758
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise BH11-24 using a Sea-Bird SBE-911+ instrument system onboard the platform RV Blue Heron

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/111762
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise BH12-02 using a Sea-Bird SBE-911+ instrument system onboard the platform RV Blue Heron

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/109966
title: GNSS (Global Navigation Satellite System) data collected during research cruise BH12-02 using a Northstar 941X instrument system onboard the platform RV Blue Heron

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/111782
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise BH12-30 using a Sea-Bird SBE-911+ instrument system onboard the platform RV Blue Heron

description: Original (unprocessed) data collected during a Field_expedition