cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/gdw2-1373
title: PRECIS HAR

description: PRECIS HAR represents a RGB-D dataset for human activity recognition, captured with the 3D camera Orbbec Astra Pro. It consists of 16 different activities (stand up, sit down, sit still, read, write, cheer up, walk, throw paper, drink from a bottle, drink from a mug, move hands in front of the body, move hands close to the body, raise one hand up, raise one leg up, fall from bed, and faint), performed by 50 subjects. There are 800 videos in total, having both RGB and depth recordings.The dataset does not contain other people than the video subject that appear as environmental noise. Besides, the background does not change. However, its difficulties are:Small camera occlusions: from time to time, we added small occlusions to the RGB stream (we move the mouse from a part of the screen to the other), that simulate the presence of external, uncontrolled environment factors; for example, in a real scenario, a fly could pass in front of the camera.No stage directions: while filming the dataset, we did not instruct participants on how to perform each action. We simply told them the name of the action. Thus, they all interpreted them slightly differently, closer to the natural way in which a person would behave.Variable video length: because we did not impose constraints on how the participants had to perform each action, dataset videos have different lengths.Different lighting conditions: the source of light is variable, sometimes having scenes with pale light and other times with sun shining directly on scene parts. These lighting variations are however only visible in the RGB stream. The depth stream is not affected by environmental changes, as far as the light is concerned. We thus want to emphasize the utility of also examining the depth stream, while classifying videos from RGB-D cameras. When citing this dataset, please cite both the dataset and the article that introduces it: Fusion Mechanisms for Human Activity Recognition using Automated Machine Learning. Instructions will be added here once the article is published.
DOI: 10.5281/zenodo.3508506
title: Distinguishing mutations and null alleles from genotyping errors using mother progeny comparisons in Brazilian pine (Araucaria angustifolia)

description: the rate of null alleles, mutations and genotyping errors in microsatellite loci, using Araucaria angustifolia, a threatened species, as a case study. We estimated the rates of the different types of genotyping deviations using mother-progeny genotype comparison from 50 seed-trees and their respective progeny (seeds). A total of 2336 A. angustifolia samples were genotyped, and we found that the rate of null alleles was 0.045. From the 1972 mother-progeny comparisons, the overall genotype deviation rate was 1.58%, consisting of 145 inconsistences (mutations), 339 null alleles and 210 genotyping errors. In terms of seed numbers, 128 (6.5%) showed inconsistencies in at least one locus, 118 (6.0%) null alleles, and 321 (16.3%) genotyping errors. This is the first study to describe the inconsistences (mutations) between mother-progeny genotypes for A. angustifolia, and the outcome makes it clear that an understanding of these genotyping deviations must be considered in assessing the accuracy of inferences made based on population genetics analyses.
DOI: 10.14469/hpc/6224
title: Kekulene, b3lyp+GD3BJ/Def2-TZVPP

description: Gaussian Calculation
DOI: 10.7910/dvn/wjq8fd/22z2bq
title: Feeding-Trial-Data July 2019.tab

description: :unav
DOI: 10.14469/hpc/6225
title: Kekulene, wB97XD/Def2-TZVPP

description: Gaussian Calculation
DOI: 10.7910/dvn/bmxvqw/yasjmo
title: UT_Cambodia_LSIL_on-farm_leucaena_biomass.tab

description: :unav
DOI: 10.21227/kbzt-yg77
title: Hotel review dataset

description: Chinese Hotel Review Dataset
DOI: 10.7910/dvn/fhccwy/9to7vp
title: presidential_elections_dataset.tab

description: Dataset (Stata format)
DOI: 10.7910/dvn/fhccwy/sdszfr
title: Codebook.pdf

description: Codebook
DOI: 10.7910/dvn/fhccwy/u8zr0j
title: license.txt

description: License
DOI: 10.5281/zenodo.3508543
title: weecology/portalPredictions 2019-10-18

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.3508544
title: weecology/forecasts 2019-10-18

description: Archived forecasts for future analysis
DOI: 10.21227/9yqx-yr91
title: Hotel review dataset

description: Chinese Hotel Review Dataset
DOI: 10.25345/c5t080
title: MassIVE MSV000084473 - Tapirira_guinensis_Chroloform_extract_positive_mode
title: chroroform extract of Tapirira guianensis. The samples was recolected in Tanimboca near Leticia, Amazonas. positive Mode
DOI: 10.15787/vtt1/yrts1z
title: Vehicle Occupants and Driver Behavior: A Novel Data Approach to Assessing Speeding (02-009)

description: Project Description: The question of whether driver behavior, and speeding in particular, differs based on vehicle occupancy requires the use of large amounts of data—some of which may be difficult to accurately obtain. Traditional methods of obtaining information on driver behavior either lack passenger information altogether (i.e., insurance companies using telematics) or rely on rough estimates of passenger age and gender obtained from blurred photos (i.e., naturalistic driving studies like State Highway Research Program 2 (SHRP 2)). This research project represents a novel, data-driven approach to this topic. Household travel survey demographic information and global positioning system (GPS) traces were linked to HERE network speed limit to study the impact of vehicle occupancy on speeding. Survey responses from 11 study areas were cleaned, merged, and ultimately used in developing binomial logistic regression models. Of particular interest were the driver groups of teenagers, adults driving with children, and older drivers. The models suggest that drivers speed less when there is a passenger in the vehicle, especially adults with a child passenger. These data represent the trip summary model inputs for determining the influence that passengers may have on the following driver groups’ speeding behavior: younger drivers (16-years-old to 24-years-old), older drivers (65+ years old), and all drivers 16-years-old to 55-years-old with a child passenger (0-years-old to 15-years-old). The original trip data came from the Texas Department of Transportation Travel Survey Program’s household travel surveys from eleven urban areas around the state. Each trip was matched with its corresponding GPS trace data to determine the travel route. The route was then assigned posted speed limits from the HERE streets network. Each was trip were was coded as a speeding trip if at least 20% of the trip free-flow (any traveling speed that ranged from 5 miles per hour below the posted speed limit or greater) duration was comprised of driving speeds greater than the posted speed limit. All trips were included in the data set irrespective of travel speed. Driver and passenger demographic characteristics were gathered from the household travel surveys. Data Scope: Trip summaries in tabular format (three Excel CSV files): younger_driver_data_public.csv older_driver_data_public.csv all_drivers_data_public.csv 11 cities across Texas: Abilene Bryan-College Station Corpus Christi El Paso Houston Midland-Odessa San Angelo Sherman-Denison Texarkana Victoria Wichita Falls Trips per driver group: Younger drivers – 825 Older drivers – 2,654 All drivers with child passenger – 6,106 Data Specifcation: Data specification is included in a file attached
DOI: 10.17632/nmpm73txc9.1
title: Data for: Interspecific hybridisation and LTR-retrotransposon mobilisation-related structural variation in plants: a study case

description: Supplementary materials for the article
DOI: 10.15787/vtt1/z5dzaj
title: Assessing Alternative Approaches for Conveying Automated Vehicle ‘Intentions’ (03-082)

description: Project Description: One of the greatest barriers to the entry of highly automated vehicles (HAV) into the market is the lack of user trust in the vehicle. Research has shown that this lack of faith in the system primarily stems from a lack of system transparency while in motion (e.g., the user not being told how the car will react in a certain situation) and not having an effective way to control the vehicle in the event of a system failure. This problem is particularly prevalent in public transit or ridesharing applications, where HAVs are expected to first appear and where the user has less training and control over the vehicle. To improve user trust and perceptions of comfort and safety, this study developed human-machine interface (HMI) systems, focusing on visual and auditory displays, to better relay automated vehicle “intentions” and the perceived driving environment to the user. These HMI systems were then implemented into a HAV developed at the Virginia Tech Transportation Institute (VTTI) and tested with volunteer participants on the Smart Roads. Data Scope: The dataset includes participant responses to surveys (open-ended feedback, Likert-scale, yes/no answers) distributed during each testing session. Different surveys were distributed before the testing session began (“pre-session questionnaire”), after each trial (“post-trial questionnaire”), and after the surprise event (“post-surprise questionnaire”). Data Specification: See attached data dictionary pdf.
DOI: 10.21227/r46e-6s97
title: PRECIS HAR

description: PRECIS HAR represents a RGB-D dataset for human activity recognition, captured with the 3D camera Orbbec Astra Pro. It consists of 16 different activities (stand up, sit down, sit still, read, write, cheer up, walk, throw paper, drink from a bottle, drink from a mug, move hands in front of the body, move hands close to the body, raise one hand up, raise one leg up, fall from bed, and faint), performed by 50 subjects. There are 800 videos in total, having both RGB and depth recordings.The dataset does not contain other people than the video subject that appear as environmental noise. Besides, the background does not change. However, its difficulties are:Small camera occlusions: from time to time, we added small occlusions to the RGB stream (we move the mouse from a part of the screen to the other), that simulate the presence of external, uncontrolled environment factors; for example, in a real scenario, a fly could pass in front of the camera.No stage directions: while filming the dataset, we did not instruct participants on how to perform each action. We simply told them the name of the action. Thus, they all interpreted them slightly differently, closer to the natural way in which a person would behave.Variable video length: because we did not impose constraints on how the participants had to perform each action, dataset videos have different lengths.Different lighting conditions: the source of light is variable, sometimes having scenes with pale light and other times with sun shining directly on scene parts. These lighting variations are however only visible in the RGB stream. The depth stream is not affected by environmental changes, as far as the light is concerned. We thus want to emphasize the utility of also examining the depth stream, while classifying videos from RGB-D cameras. When citing this dataset, please cite both the dataset and the article that introduces it: Fusion Mechanisms for Human Activity Recognition using Automated Machine Learning. Instructions will be added here once the article is published.
DOI: 10.21227/5nez-qv82
title: PRECIS HAR

description: PRECIS HAR represents a RGB-D dataset for human activity recognition, captured with the 3D camera Orbbec Astra Pro. It consists of 16 different activities (stand up, sit down, sit still, read, write, cheer up, walk, throw paper, drink from a bottle, drink from a mug, move hands in front of the body, move hands close to the body, raise one hand up, raise one leg up, fall from bed, and faint), performed by 50 subjects. There are 800 videos in total, having both RGB and depth recordings.The dataset does not contain other people than the video subject that appear as environmental noise. Besides, the background does not change. However, its difficulties are:Small camera occlusions: from time to time, we added small occlusions to the RGB stream (we move the mouse from a part of the screen to the other), that simulate the presence of external, uncontrolled environment factors; for example, in a real scenario, a fly could pass in front of the camera.No stage directions: while filming the dataset, we did not instruct participants on how to perform each action. We simply told them the name of the action. Thus, they all interpreted them slightly differently, closer to the natural way in which a person would behave.Variable video length: because we did not impose constraints on how the participants had to perform each action, dataset videos have different lengths.Different lighting conditions: the source of light is variable, sometimes having scenes with pale light and other times with sun shining directly on scene parts. These lighting variations are however only visible in the RGB stream. The depth stream is not affected by environmental changes, as far as the light is concerned. We thus want to emphasize the utility of also examining the depth stream, while classifying videos from RGB-D cameras. When citing this dataset, please cite both the dataset and the article that introduces it: Fusion Mechanisms for Human Activity Recognition using Automated Machine Learning. Instructions will be added here once the article is published.
DOI: 10.7910/dvn/cdovxz/b5awmd
title: UT_Cambodia_LSIL_Biomass Data UBB.tab

description: :unav
DOI: 10.17632/y6nfpykjv3.1
title: Data for: School gentrifying community in the making in China: its formation mechanisms and socio-spatial consequences

description: This is the questionnaire data used in our paper. Check it out if necessary.
DOI: 10.5281/zenodo.3508656
title: Group diagrams for representing trajectories

description: The provided data is a subset of the data set <em>LifeTrack Geese IGRAS MPIO ICARUS</em> maintained by movebank.org. <strong>Please contact Andrea Kölzsch (akoelzsch@orn.mpg.de) before using the data</strong> in order to obtain essential information on how to correcty handle the data. The provided code to compute group diagrams based on equal-time distance or Fréchet distance is at the stage of prototyping in order to support the findings of the corresponding paper "Group diagrams for representing trajectories" by Maike Buchin, Bernhard Kilgus, and Andrea Kölzsch. For questions about using the code, remarks and suggestions please contact Bernhard Kilgus (bernhard.kilgus@rub.de).
DOI: 10.5281/zenodo.3508657
title: Group diagrams for representing trajectories

description: A subset of the data from the LifeTrack Geese IGRAS MPIO ICARUS maintained by movebank.org. <strong>Please contact Andrea Kölzsch (akoelzsch@orn.mpg.de) before using the data</strong> in order to obtain essential information on how to handle the data.
DOI: 10.7910/dvn/mapgnu/8qh5sx
title: README.txt

description: :unav
DOI: 10.3886/e115002v1
title: Immigration Enforcement and Student Achievement in the Wake of Secure Communities

description: Over the past decade, U.S. immigration enforcement policies have increasingly targeted unauthorized immigrants residing in the U.S. interior, many of whom are the parents of U.S.-citizen children. Heightened immigration enforcement may affect student achievement through stress, income effects, or student mobility. I use one immigration enforcement policy, Secure Communities, to examine this relationship. I use the staggered activation of Secure Communities across counties to measure its relationship with average achievement for Hispanic students, as well as non-Hispanic black and white students. I find that the activation of Secure Communities was associated with decreases in average achievement for Hispanic students in English Language Arts (ELA), as well as black students in ELA and math. Similarly, I find that increases in removals are associated with decreases in achievement for Hispanic and black students. I note that the timing of rollout is potentially correlated with other county trends affecting results.
DOI: 10.3886/e115002
title: Immigration Enforcement and Student Achievement in the Wake of Secure Communities

description: Over the past decade, U.S. immigration enforcement policies have increasingly targeted unauthorized immigrants residing in the U.S. interior, many of whom are the parents of U.S.-citizen children. Heightened immigration enforcement may affect student achievement through stress, income effects, or student mobility. I use one immigration enforcement policy, Secure Communities, to examine this relationship. I use the staggered activation of Secure Communities across counties to measure its relationship with average achievement for Hispanic students, as well as non-Hispanic black and white students. I find that the activation of Secure Communities was associated with decreases in average achievement for Hispanic students in English Language Arts (ELA), as well as black students in ELA and math. Similarly, I find that increases in removals are associated with decreases in achievement for Hispanic and black students. I note that the timing of rollout is potentially correlated with other county trends affecting results.
DOI: 10.16907/7bc20828-fda6-432e-b4b2-a0f1ff293f2f
title: Just a test

description: sdg
DOI: 10.25345/c5p95q
title: MassIVE MSV000084474 - Rayo et al Immunoediting Role for Major Vault Protein in Apoptotic Signaling Induced by N-Acyl Homoserine Lactones
title: In order to find investigate the immunomodulatory effects of the bacterial signaling molecule N-(3-Oxododecanoyl)-L-homoserine lactone, a synthetic analogue was prepared in order to identify potential mammalian receptors. SILAC-labeled normal human bronchial epithelial (NHBE) cells were used. Additionally, results of a labeling experiment with the probe on pure recombinant human major vault protein (the target resulting from the above experiment) are included.
DOI: 10.16907/50db5a4b-a4ca-49cd-a49f-46876b93d67b
title: Just a test
DOI: 10.16907/709d2153-5554-4e01-8455-d1d2c4410c2b
title: Just a test

description: Testdescription
DOI: 10.25612/837.prqe7j7b6mxw
title: Businesses categorized as Social Services in the Houston-Galveston Area Council Region - 2017
title: Businesses categorized as Social Services - 2017

description: This dataset is an extract of the Infogroup Businesses 2017 dataset and contains entries that are categorized as Social Services (NAICS codes 8322, 8331, 8351, 8361, and 8399) within the Houston-Galveston Area Council (13-county) region.
DOI: 10.17632/kjhxgdd37b.1
title: Data for: Drivers of farmers’ willingness to adopt extensive farming practices in a globally important bird area

description: Dataset, estimation codes, questionnaire, translated wording of the descriptions of agri-environmental schemes
DOI: 10.5281/zenodo.3508780
title: Group diagrams for representing trajectories

description: The provided data is a subset of the data set <em>LifeTrack Geese IGRAS MPIO ICARUS</em> maintained by movebank.org. <strong>Please contact Andrea Kölzsch (akoelzsch@orn.mpg.de) before using the data</strong> in order to obtain essential information on how to correcty handle the data. The provided code to compute group diagrams based on equal-time distance or Fréchet distance is at the stage of prototyping in order to support the findings of the corresponding paper "Group diagrams for representing trajectories" by Maike Buchin, Bernhard Kilgus, and Andrea Kölzsch. For questions about using the code, remarks and suggestions please contact Bernhard Kilgus (bernhard.kilgus@rub.de).
DOI: 10.5519/qd.yturxyhy
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.3508811
title: Elevated Atopic Comorbidity in Patients with Food Protein-Induced Enterocolitis

description: Dataset used for manuscript
DOI: 10.5281/zenodo.3508812
title: Elevated Atopic Comorbidity in Patients with Food Protein-Induced Enterocolitis

description: Dataset used for manuscript
DOI: 10.7910/dvn/w5my3d/kjvvau
title: Dataset_Workshop_Follow up_Dec18-Jan19_36_final_surveys.tab

description: :unav
DOI: 10.5281/zenodo.3509708
title: Data for: Brain structural connectivity predicts brain functional complexity

description: Data used in analyses for "Brain structural connectivity predicts brain functional complexity: DTI derived centrality accounts for variance in fractal properties of fMRI signal"
description: Rows represent regions in Shen 268 atlas
DOI: 10.5281/zenodo.3509709
title: Data for: DTI Derived Centrality Predicts fMRI Complexity as Measured by Fractal Analysis

description: Data used in analyses for "DTI Derived Centrality Predicts fMRI Complexity as Measured by Fractal Analysis"
description: Rows represent regions in Shen 268 atlas
DOI: 10.5281/zenodo.3222772
title: Stellar Density Profiles of Dwarf Spheroidal Galaxies: Posterior Distributions

description: This archive contains samples from the posterior distributions of our 3-plummer, 1-plummer, 3-steeper and 1-steeper fits for 38 dwarf spheroidal galaxies.
description: readme included
DOI: 10.5281/zenodo.3222773
title: Stellar Density Profiles of Dwarf Spheroidal Galaxies: Posterior Distributions

description: This archive contains samples from the posterior distributions of our 3-plummer, 1-plummer, 3-steeper and 1-steeper fits for 38 dwarf spheroidal galaxies.
description: readme included
DOI: 10.17632/tfx9bnf86z.1
title: Data for: Adherence to non-opioid multimodal analgesia (NOMA) protocol is associated with a shorter length of stay after thoracic surgery

description: Study objective: To review the outcomes of non-opioid multimodal analgesia (NOMA) in patients after thoracic surgery. Design: Retrospective one-year cohort. Setting: Single hospital, level one trauma center. Patients. 29 ASA 2-4 patients after thoracotomy (8), open lung biopsy or video-assisted thoracic surgery (VATS) operated by a single surgeon. Interventions: NOMA protocol included intravenous acetaminophen alternating with ketorolac every 3 hours (each every 6 hours, later converted to oral acetaminophen and ibuprofen) with a low dose (0.05mg/kg/h) IV ketamine infusion up to chest tube removal. Opioids were not routinely prescribed from postoperative day one. Measurements: NOMA adherence versus breach (need for &gt;2 opioid rescue doses/24h) rate and it’s association with the perioperative variables and length of stay was explored from the chart review. Main Results: The median (IQR) postoperative opioid requirement was 3 (0-6) doses per hospital stay. 7/29 patients (CI 10-44%) required &gt;2 opioid rescue doses/24 h. Need for opioid rescue was associated with lower BMI and using hydromorphone as oppose to morphine in the recovery room. 3/29 patients were prescribed opioids on discharge. No patients required opioids on follow-up and 63% were pain-free. Median length of stay was 3 (2-5.25) days in NOMA compliant and 9 (6-11) days in opioid rescue groups (P=0.002). In subgroup analysis length of stay reduction was limited to non-thoracotomy patients.
DOI: 10.5281/zenodo.3509977
title: 2019JC015322

description: Those uploaded files are required data for manuscript 2019JC015322.
DOI: 10.5281/zenodo.3509976
title: 2019JC015322

description: Those uploaded files are required data for manuscript 2019JC015322.