cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/f3bae8adcf1ff3d6b993881be9818a16
title: Jornada Basin LTER: Wireless meteorological station at NPP M-WELL site: Daily average soil volumetric water content data: 2013 - ongoing

description: Daily average of 30-minute Volumetric Water Content at NPP M-WELL met station. Collection of soil volumetric water content data at Jornada LTER NPP sites, New Mexico, supports the environmental monitoring objectives of the Jornada LTER monitoring program that look at plant-soil water dynamics. Volumetric water content, bulk electrical conductivity, soil temperature, and bulk dielectric permittivity are measured and calculated at an automated meteorological station installed at Jornada LTER NPP M-WELL site. The daily average at 3 soil depths is calculated from measurements made every 30 minutes at 10 cm, 20 cm, and 30 cm. This is an ONGOING dataset.
DOI: 10.17632/pff4wtbwcd.1
title: Safety of IL17/23 Inhibitors in Patients with Psoriasis or Other Immune-mediated Diseases: a Systematic Review and Meta-Analysis

description: Supplemental figures and tables for 'Safety of IL17/23 Inhibitors in Patients with Psoriasis or Other Immune-mediated Diseases: a Systematic Review and Meta-Analysis'.
DOI: 10.17632/wjpbtgdyzm.1
title: A multi-sensory dataset for the activities of daily living

description: The dataset contains multiple instances of 9 Activities of Daily Living (ADL)-related actions namely Walk, Sit Down, Stand Up, Open Door, Close Door, Pour Water, Drink Glass, Brush Teeth and Clean Table. Each of the 10 volunteers performed each activity at least 14 times, with the notable exception of the walking activity that has been performed 40 times, in different sequences and alternating the used hand. For each volunteer, the dataset contains 7 CSV files, i.e., one file for each of the 6 IMU sensors worn by the volunteer on different body parts, as described in Figure 1, namely left lower arm (lla.csv), left upper arm, (lua.csv), right lower arm (rla.csv), right upper arm (rua.csv) and right thigh (rt.csv). Each file contains the overall sequence recorded during the experiment. The first column contains a label "qags" indicating the type of recorded data (quaternions, acceleration, angular velocity). The next column is the time-stamp in milliseconds elapsed from 00:00:0.000 AM (with a 30 milliseconds sampling time). The next four columns are the quaternions (with a resolution of 0.0001 ). Following them, we have three columns with the accelerations along the x, y and z axes (with a 0.1 mG resolution). The last three columns refer to the angular velocity about the x, y and z axes (with a 0.01 dps resolution). The last CSV file (annotation.csv) contains the data labelling. The first two columns of this file contain the time in the format hh.mm.ss.000 (current day time) and in milliseconds elapsed from 00:00:0.000 AM. All the remaining columns are organised as couples where the first element represents the scope of the labelling and the second indicates whether the labelled activity is starting or ending. In the annotation file, there are four different labelling scopes. • “BothArms”: all instances of each activity are labelled independently of which arm has been used; • “RightArm”: are labelled the activity instances using only the right arm, or in case they belong to the Walk, Sit Down or the Stand Up activities; • “LeftArm”: are labelled the activity instances using only the left arm, or in case they belong to the Walk, Sit Down or the Stand Up activities; • “Locomotion”: in this scope are labelled only the instances of Walk, Sit Down and Stand Up. Finally, the last two columns report a session ID. There are four different sessions characterised by the order in which the activities are performed, and by the used arm (see also Table 3), and whether the session starts or ends. The videos recorded during the experiments have been only used for labelling purposes, and they are not published. Together with the dataset, we provide a MATLAB script named TimeStampExtraction.m that extract from the annotation and the data files, for each volunteer and for each sensor, the time stamp associated with the start and end of each ADL.
DOI: 10.17632/xv9j5nc4kz.1
title: Allosteric/intrinsic transcriptional termination of RNA polymerase II on human snRNA genes
DOI: 10.21227/9623-vb48
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about "corona", "covid-19", "coronavirus" or the variants of "sars-cov-2". Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.Note: I started sharing the IDs of the tweets that contained the exact location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs (above 140+ million tweets) shared in the Coronavirus (COVID-19) Tweets Dataset.If you need the geolocation-based data starting March 20, 2020, then use the Coronavirus (COVID-19) Tweets Dataset and hydrate the IDs while adding the following condition:data = json.loads(data)if data["coordinates"]: longitude, latitude = data["coordinates"]["coordinates"]The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-april29.* will contain tweet ID and sentiment data of the tweets that were tweeted between April 28, 2020, and April 29, 2020.Why are only tweet IDs being shared? It is Twitter's content redistribution policy that restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers to always pull fresh data using their API. Why? Here's my opinion. Maybe, some user might want to delete a particular tweet after a couple of minute(s)/hour(s)/day(s), and if the same tweet has already been pulled and is shared on a public domain, then it might make the user/community vulnerable to many inferences coming out of the shared data.
DOI: 10.25345/c5dd8d
title: MassIVE MSV000085538 - Massive_GIT_in_Formaldehyde_Neg
title: Gastrointestinal tract stored in formaldehyde for 3 days at room temperature. ddMS2 study in C8 with 7.5-minute gradients.
DOI: 10.5281/zenodo.3873300
title: APIS CIDOC CRM serialization

description: This is a trig serialisation of the enriched version of the Austrian Biographical Dictionary. The data was enriched during the Austrian Prosopographical Information System (APIS) project. It contains data on ~19.000 persons who had an impact on Austrian soil and died between 1815 and 1955. This version of the APIS data uses CIDOC CRM and is less complete than the also available JSON version of the data (using a custom data model). Please refer to Matthias Schlögl for any inquiries.
DOI: 10.5281/zenodo.3873986
title: Soil Carbon Stock Change Due to Afforestation in Japan by Paired-Sampling Method in an Equivalent Mass Basis

description: The data set for the paper submitted to Global Biogeochemical Cycles.
DOI: 10.5281/zenodo.3873987
title: Soil Carbon Stock Change Due to Afforestation in Japan by Paired-Sampling Method in an Equivalent Mass Basis

description: The data set for the paper submitted to Global Biogeochemical Cycles.
DOI: 10.6073/pasta/875a45810e2b49e508e8cdb68e19a3ba
title: Jornada Basin LTER: Wireless meteorological station at NPP P-COLL site: 1-hour summary data: 2013 - ongoing

description: Hourly summary data at NPP P-COLL met station. Average/maximum/minimum air temperature; average relative humidity and wind direction; average/maximum wind speed. These are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at Jornada LTER NPP P-COLL site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset.
DOI: 10.17632/d3pb9kj56d.1
title: Medicaid State Financial Condition

description: This database includes the averages (2006, 2010, 2014, 2016 and 2017) of state financial condition in the United States, Medicaid spending per enrollee and a number of control variables in an analysis of the effects of Medicaid spending on state financial condition. The SPSS database is included here. The hypothesis was that the effects were significant and possibly positive. I found an offsetting effect on revenues and expenditures which nulled the overall effect on state financial condition.
DOI: 10.17632/dcpst33wc7.2
title: Data for Rosenzopf et al. Mapping the human praxis network: an investigation of white matter disconnection in limb apraxia

description: Data for Rosenzopf et al. - Mapping the human praxis network: an investigation of white matter disconnection in limb apraxia (working title) Data include topographies, analysis scripts, and full results of the SVR-FA-mapping analysis The archive can be extracted with 7zip freeware (https://www.7-zip.de) Topographies can be viewed with MRIcron freeware (https://www.nitrc.org/projects/mricron)
DOI: 10.17632/g2xh9k5yzy.1
title: iVR

description: iVR is a progress monitoring tool in near real-time using Digital Twin. it integrate extended reality with 3D laser scanners to exchange data between construction jobsite and construction office. the gap between office and site is reduced by utilizing the power of visual programming in tranferring and visualizing data in high quality and fast rate. the Virtual reality, Augmented reality and BIM technologies are used to make the virtual reality as close as possible to construction job site for Quality Assurance.
DOI: 10.17632/kvwknkdpfz.2
title: Global Scientific Research on SARS-CoV-2 (COVID-19).

description: For analyzing data in the VOS viewer, we used ISI, PubMed, and Scopus as a database. Data were obtained from the Web of Science (WOS), PubMed, and Scopus on March 02, 2020, and updated on March 10. to analyzed in VOS viewer software. The keywords were divided into three clusters: Generally, the lesser the distance between two key-terms, the greater the number of co-occurrences of the terms. The larger circle represents the more frequent keyword
DOI: 10.17632/r9d66gnvtm.2
title: Extreme_Precip_Event_Database

description: Extreme events statistics from IMERG over CONUS
DOI: 10.21227/mmke-dv03
title: MIT DriveSeg (Manual) Dataset

description: Semantic scene segmentation has primarily been ad- dressed by forming representations of single images both with supervised and unsupervised methods. The problem of semantic segmentation in dynamic scenes has begun to recently receive attention with video object segmentation approaches. What is not known is how much extra informa- tion the temporal dynamics of the visual scene carries that is complimentary to the information available in the indi- vidual frames of the video. We provide the MIT DriveSeg dataset, which is a large-scale driving scene segmen- tation dataset, densely annotated for every pixel and every one of 5,000 video frames. The purpose of this dataset is to allow for exploration of the value of temporal dynamics information for full scene segmentation in dynamic, real- world operating environments.
DOI: 10.21227/ze7x-y204
title: MIT DriveSeg (Manual) Dataset

description: Semantic scene segmentation has primarily been ad- dressed by forming representations of single images both with supervised and unsupervised methods. The problem of semantic segmentation in dynamic scenes has begun to recently receive attention with video object segmentation approaches. What is not known is how much extra informa- tion the temporal dynamics of the visual scene carries that is complimentary to the information available in the indi- vidual frames of the video. We provide the MIT DriveSeg dataset, which is a large-scale driving scene segmen- tation dataset, densely annotated for every pixel and every one of 5,000 video frames. The purpose of this dataset is to allow for exploration of the value of temporal dynamics information for full scene segmentation in dynamic, real- world operating environments.
DOI: 10.17632/3bpv5v6yxm.2
title: Data for manuscript: Enthalpy based model for prediction of microstructure evolution during binary alloy solidification
DOI: 10.17632/9jmn5cr3r6.1
title: THE EFFECT OF TAX REFORM ON VALUE OF TAX STRATEGIES

description: Using a US panel data set from 1982 to 1992, I analyze how the 1986 Tax Reform Act (TRA1986) influenced the value of pursuing a sustainable tax strategy. I use the Compustat database to construct a panel data set for USA firms over the period 1982-1992. I only include firms with complete data for the entire study period. I then divide the sample into pre-reform (1982-1986) and post-reform (1988-1992) periods. I follow the accounting literature (e.g. Mayberry et al. 2015; Neuman et al. 2013; McGuire et al. 2014) that uses effective tax rates (ETRs) to measure tax avoidance. Lower ETRs imply that a firm engages in tax avoidance. Further, I identify a firm as having a sustainable tax strategy if the coefficient of variation of annual ETRs over a period of five years is low. Results indicate a negative association between tax avoidance behavior and the application of sustainable tax strategies, suggesting that firms that engage in higher tax avoidance behavior are not able to sustain their tax positions over time. The findings also imply that the TRA1986 diminishes the benefits of sustainable tax strategies in tax avoidance behavior.
DOI: 10.17632/btbddwdnfz.1
title: Raw data: Characterization of natural latex biomembranes

description: Raw data used in the article "Physical and chemical properties of natural latex biomembranes associated with phototherapy designed for healing diabetic foot ulcers". We performed physical and chemical tests to evaluate the membranes: Tensile Strength Test, Thermogravimetric Analysis, Wettability Assay and Fourier-transform infrared spectroscopy.
DOI: 10.17632/r9d66gnvtm.3
title: Extreme_Precip_Event_Database

description: Extreme events statistics from IMERG over CONUS
DOI: 10.17632/tmrs92j7pv.7
title: COVID-19: Time Series Datasets India versus World

description: This dataset consists of COVID-19 time series data of India since 24th March 2020. The data set is for all the States and Union Territories of India and is divided into five parts, including i) Confirmed cases; ii) Death Count; iii) Recovered Cases; iv) Temperature of that place; and v) Percentage humidity in the region. The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020. The end user can contact the corresponding author (Rohit Salgotra : nicresearchgroup@gmail.com) for more details. . [Dataset is updated Thrice a Week]
DOI: 10.21227/czxc-hg76
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about "corona", "covid-19", "coronavirus" or the variants of "sars-cov-2". Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).Note: I started sharing the IDs of the tweets that contained the exact location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs (above 150+ million tweets) shared in the Coronavirus (COVID-19) Tweets Dataset.If you need the geolocation-based data starting March 20, 2020, then use the Coronavirus (COVID-19) Tweets Dataset and hydrate the IDs while adding the following condition:data = json.loads(data)if data["coordinates"]: longitude, latitude = data["coordinates"]["coordinates"]The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-april29.* will contain tweet ID and sentiment data of the tweets that were tweeted between April 28, 2020, and April 29, 2020.Why are only tweet IDs being shared? It is Twitter's content redistribution policy that restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers to always pull fresh data using their API. Why? Here's my opinion. Maybe, some user might want to delete a particular tweet after a couple of minute(s)/hour(s)/day(s), and if the same tweet has already been pulled and is shared on a public domain, then it might make the user/community vulnerable to many inferences coming out of the shared data.
DOI: 10.21227/ef6f-5z46
title: MIT DriveSeg (Manual) Dataset

description: Semantic scene segmentation has primarily been ad- dressed by forming representations of single images both with supervised and unsupervised methods. The problem of semantic segmentation in dynamic scenes has begun to recently receive attention with video object segmentation approaches. What is not known is how much extra informa- tion the temporal dynamics of the visual scene carries that is complimentary to the information available in the indi- vidual frames of the video. We provide the MIT DriveSeg dataset, which is a large-scale driving scene segmen- tation dataset, densely annotated for every pixel and every one of 5,000 video frames. The purpose of this dataset is to allow for exploration of the value of temporal dynamics information for full scene segmentation in dynamic, real- world operating environments.
DOI: 10.5281/zenodo.3873309
title: APIS CIDOC CRM serialization

description: This is a trig serialisation of the enriched version of the Austrian Biographical Dictionary. The data was enriched during the Austrian Prosopographical Information System (APIS) project. It contains data on ~19.000 persons who had an impact on Austrian soil and died between 1815 and 1955. This version of the APIS data uses CIDOC CRM and is less complete than the also available JSON version of the data (using a custom data model). Please refer to Matthias Schlögl for any inquiries.
DOI: 10.5519/qd.i25cxul1
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (37 records)
DOI: 10.17632/96bngfw6vt.1
title: Processed data: Characterization of natural latex biomembranes

description: Processed data used in the article "Physical and chemical properties of natural latex biomembranes associated with phototherapy designed for healing diabetic foot ulcers". We performed physical and chemical tests to evaluate the membranes: Tensile Strength Test, Thermogravimetric Analysis, Wettability Assay and Fourier-transform infrared spectroscopy.
DOI: 10.17632/bcnv3n43wg.1
title: Classification of Microorganisms of Sukhna and Dhanas Lakes
DOI: 10.17632/gwhtjvfnw7.3
title: Dataset for studing the relationship between gastronomy and internationalization

description: The dataset contains raw data from 154 foreign tourists visiting the city of Seville. The data were collected by means of a structured questionnaire in December 2017. Related research article: Juan Manuel Berbel-Pineda, Beatriz Palacios-Florencio, Luna Santos-Roldán, and José M. Ramírez Hurtado, Relation of Country-of-Origin Effect, Culture, and Type of Product with the Consumer’s Shopping Intention: An Analysis for Small- and Medium-Sized Enterprises, Complexity. Article ID 8571530 (2018). https://doi.org/10.1155/2018/8571530
DOI: 10.17632/zz8zzrdsj7.1
title: Project Gatekeeper: An entrance control system embedded radiation detection capability for security applications.

description: Threat assessments continue to conclude that terrorist groups and individuals as well as those wanting to cause harm to society have the ambition and increasing means to acquire unconventional weapons such as improvised nuclear explosive devices and radiological disposal devices. Such assessments are given credence by public statements of intent by such groups/persons, by reports of attempts to acquire radioactive material, and by law enforcement actions which have interdicted, apprehended or prevented attempts to acquire such material. As a mechanism through which to identify radioactive materials being transported on an individual’s person, this work sought to develop a detection system than is of lower-cost, reduced form-factor and more covert than existing infrastructure, while maintaining adequate sensitivity and being retrofittable into an industry standard and widely utilised Gunnebo Speed Gate system. The system developed comprised an array of six off-set Geiger-Muller detectors positioned around the gate, alongside a single scintillator detector for spectroscopy – triggered by the systems inbuilt existing IR proximity sensor. This configuration served to not only reduce the cost for such a system but also allowed for source localisation and identification to be performed. Utilising the current setup, it was possible to detect a 1 µSv/hr source carried into the Speed Gate on all of instances, alongside locating and spectrally analyzing the material.
DOI: 10.6073/pasta/87cb364013fa1b281b00cc296301615b
title: Jornada Basin LTER: Wireless meteorological station at NPP P-COLL site: Daily summary data: 2013 - ongoing

description: Daily summary data at NPP P-COLL met station. Average/maximum/minimum air temperature; average/maximum relative humidity and wind speed and average wind direction. These are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at Jornada LTER NPP P-COLL site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset.
DOI: 10.6073/pasta/de9888330c840a91d085d5844dcc8372
title: Jornada Basin LTER: Wireless meteorological station at NPP P-COLL site: 30-minute summary data: 2013 - ongoing

description: 30-minute summary data at NPP P-COLL met station. Average air temperature, relative humidity, wind speed and wind direction are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at Jornada LTER NPP P-COLL site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset.
DOI: 10.17632/gwhtjvfnw7.1
title: Dataset_Tasting

description: The dataset contains raw data from 154 foreign tourists visiting the city of Seville. The data were collected by means of a structured questionnaire in December 2017. Related research article: Juan Manuel Berbel-Pineda, Beatriz Palacios-Florencio, Luna Santos-Roldán, and José M. Ramírez Hurtado, Relation of Country-of-Origin Effect, Culture, and Type of Product with the Consumer’s Shopping Intention: An Analysis for Small- and Medium-Sized Enterprises, Complexity. Article ID 8571530 (2018). https://doi.org/10.1155/2018/8571530