cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18434/m32145
title: Ageing of High Strength p-Aramid Fibers Used in Body Armor

description: To improve the reliability and design of armor, it is imperative to understand the failure modes and the degradation rates of the materials used in armor. Despite the best efforts of manufacturers, some vulnerability of armor materials to ageing due to hydrolytic or oxidative environments is expected and may result in the degradation of material properties such as tensile strength. In this work, p-aramid yarns from two manufacturers were exposed to environmental conditions of various fixed temperature and humidity combinations. The maximum temperature and humidity condition was 70 °C and 76 % RH. Tensile tests were performed on specimens extracted at several different timepoints over the course of at least one year to determine the change in ultimate tensile strength and failure strain as a function of time, temperature, and humidity. These materials were found to be generally resistant to degradation at most conditions, showing changes of less than 10 % only at the highest temperature and humidity conditions.This data set contains failure load and failure strain values for three different aramid yarns, exposed to various conditions. It also includes Fourier Transform Infrared (FTIR) spectroscopy spectra for two of the aramids.
DOI: 10.5281/zenodo.3518423
title: jaume-piera/Taller_OAW_2019: Release1.0 OAW

description: No description provided.
DOI: 10.5281/zenodo.3518424
title: jaume-piera/Taller_OAW_2019: Release1.0 OAW

description: No description provided.
DOI: 10.7910/dvn/tprp0f/k19khv
title: Boston Crime Stats - geocoded violent crime - lat-long only.tab

description: :unav
DOI: 10.5281/zenodo.3518433
title: dicapisi/Taller_OAW_2019: Taller_OAW_2019

description: Liberacion de codigo y datos de deforestacion en Corpochivor
DOI: 10.5281/zenodo.3518434
title: dicapisi/Taller_OAW_2019: Taller_OAW_2019

description: Liberacion de codigo y datos de deforestacion en Corpochivor
DOI: 10.6073/pasta/1adbcd358d2285691ff7d13d816d7d99
title: Krummholz island soil C and N data for East of Tvan, 1994.

description: Previous work has shown that passage of Engelmann spruce (Picea engelmannii) and subalpine fir (Abies lasiocarpa) tree islands across tundra lowers the soil carbon and nitrogen storage capacity of the top 15cm of soil (A horizon) (Pauker and Seastedt 1996). This data forms part of a study which seeks to further examine how changes in carbon storage capacity are associated with changes in soil physical properties. Soils were collected in 1994. We sought to determine whether there were changes in natural abundance of C and N isotopes associated with previously reported changes in C and N content. Any such changes in natural abundance of C and N isotopes may indicate differences in inputs to the ecosystem or differences in ecosystem processes that may account for the differences in N and C content that have been observed. In addition to measuring ratios of natural isotopes of C and N, we also measured C and N contents to provide a comparison with previous studies. To convert % N and C values to kgs of N and C per m2, we used bulk density values, averaged across sampling dates that had been previously measured for these sites. Metadata for these data are available at: https://portal.lternet.edu/nis/mapbrowse?scope=knb-lter-nwt&identifier=131 When N and C soil contents were considered on a percentage basis, levels at windward sites were significantly lower than those of undisturbed tundra, but levels directly beneath krummholz were not significantly different from tundra soils. Bulk density was significantly lower under krummholz than other sites. Thus when N and C amounts were expressed on a volume (Kg/m2, to a depth of 15cm), they were significantly lower in both krummholz and windward sites than in undisturbed tundra. D13C values of soils under krummholz were significantly less negative under krummholz than from windward or undisturbed tundra sites. A less negative d13C value is indicative of less discrimination and may be attributable to either vegetation differences or differences in decomposition rates. Metadata for D13C signatures of krummholz and tundra vegetation were also examined and are available at: https://portal.lternet.edu/nis/mapbrowse?scope=knb-lter-nwt&identifier=134
DOI: 10.6073/pasta/ff606f863b711d4d9ce5d279f2fa3b56
title: Florida Bay Productivity Data, Everglades National Park (FCE), South Florida from September 2000 to Present

description: Productivity data collected during visits to TS/Ph 7a, TS/Ph8, TS/Ph9, TS/Ph10 and TS/Ph11. Graphic representation of seagrass status and trends monitoring data and other related information can be located at http://serc.fiu.edu/seagrass/!CDreport/DataHome.htm
DOI: 10.5066/p9j2dhu5
title: Geospatial Dataset of Wells and Attributes in the New England Groundwater Level Network, 2017 (ver. 1.1, December 2019)

description: A dataset of well information and geospatial data was developed for 426 U.S. Geological Survey (USGS) observation wells in Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont. An extensive list of attributes is included about each well, its location, and water-level history to provide the public and water-resources community with comprehensive information on the USGS well network in New England and data available from these sites. These data may be useful for evaluating groundwater conditions and variability across the region. The well list and site attributes, which were extracted from USGS National Water Information System (NWIS), represent all of the active wells in the New England network up to the end of 2017, and an additional 45 wells that were inactive (discontinued or replaced by a nearby well) at that time. Inactive wells were included in the database because they (1) contain periods of water-level record that may be useful for groundwater assessments, (2) may become active again at some point, or (3) are being monitored by another agency (most discontinued New Hampshire wells are still being monitored and the data are available in the National Groundwater Monitoring Network (https://cida.usgs.gov/ngwmn/index.jsp). The wells in this database have been sites of water-level data collection (periodic levels and/or continuous levels) for an average of 31 years. Water-level records go back to 1913. The groundwater-level statistics included in the dataset represent hydrologic conditions for the period of record for inactive wells, or through the end of water year 2017 (September 30, 2017) for active wells. Geographic Information Systems (GIS) data layers were compiled from various sources and dates ranging from 2003 to 2018. These GIS data were used to calculate attributes related to topographic setting, climate, land cover, soil, and geology giving hydrologic and environmental context to each well. In total, the data include 90 attributes for each well. In addition to site number and station name, attributes were developed for site information (15 attributes); groundwater-level statistics through water year 2017 (16 attributes); well-construction information (9 attributes); topographic setting (11 attributes); climate (2 attributes); land use and cover (17 attributes); soils (4 attributes); and geology (14 attributes). Basic well and site information includes well location, period of record, well-construction details, continuous versus intermittent data collection, and ground altitudes. Attributes that may influence groundwater levels include: well depth, location of open or screened interval, aquifer type, surficial and bedrock geology, topographic position, flow distance to surface water, land use and cover near the well, soil texture and drainage, precipitation, and air temperature.
DOI: 10.5523/bris.2c9by25rfxdvl20vgd6ix72vdd
title: Twin-beam sub-shot-noise raster-scanning microscope

description: Open access data relating to the publication J. Sabines-Chesterking et al. "Twin-beam sub-shot-noise raster-scanning microscope " Optics Express 27, 30810-30818 (2019) / arXiv-quant-ph:1906.05331 (2019)
DOI: 10.25739/zxp6-g188
title: LiDar Point Clouds of Hybrid Maize

description: This dataset contains raw and processed LiDar point clouds from hybrid maize trails grown in Aurora, NY in summer 2018. It accompanies the publication 'In-field whole plant maize architecture characterized by Latent Space Phenotyping' (Gage et al., 2019, The Plant Phenome Journal). Scripts for generation and analysis of the data can be found at the URL listed in the 'analysis_tool' field.
DOI: 10.5281/zenodo.3518499
title: Multiomics Analysis of the mdx/mTR Mouse Model of Duchenne Muscular Dystrophy

description: This dataset contains RNA sequencing, proteomics, metabolomics, lipidomics, primer sequence, and Ingenuity Pathway Analysis data for a study evaluating differences between the transcriptome, proteome, metabolome, and lipidome of lower limb muscles of mdx/mTR (Jackson Labs strain 023535) and wild-type C57BL/6J (Jackson Labs strain 000664) mice. Updated from previous submission, DOI: 10.5281/zenodo.3370799.
DOI: 10.5519/qd.zsgjvure
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1000 records)
DOI: 10.21227/v3b0-4e83
title: Twitter Sentiment Analysis

description: Each database (*.db) contain three columns. First column: data and time of tweet, second column: tweet (text), third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live. Only the first two columns of the databases are the outputs of the model. The last column, viz. sentiment score, is not from the model deployed at sentiment.live. The LSTM model is still in development. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of text blob has been appended as the last column. The sentiment scores of our model will be made available publicly after the project leads to a targetted summit.New databases will be released every two weeks.------status------databases: 2,tweets count: 3 million-plus
DOI: 10.17632/zr8xptp3d3.1
title: Data for: Experimental and numerical analysis on the surge instability of the vortex flow produced large vapor cavity in u-shape notch spool valve

description: (1)Figures (2)Data that associated with the Figures (3)Videos that associated with the Figures (4)3D CFD case model and one associtaed data of the 8000th time step
DOI: 10.21227/2xmn-4e41
title: Twitter Sentiment Analysis

description: Each database (*.db) contain three columns. First column: data and time of tweet, second column: tweet (text), third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live. Only the first two columns of the databases are the outputs of the model. The last column, viz. sentiment score, is not from the model deployed at sentiment.live. The LSTM model is still in development. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of text blob has been appended as the last column. The sentiment scores of our model will be made available publicly after the project leads to a targetted summit.New databases will be released every two weeks.------status------databases: 2,tweets count: 3 million-plus
DOI: 10.21227/1dzf-t886
title: Twitter Sentiment Analysis

description: Each database (*.db) contain three columns. First column: date and time of the tweet, second column: tweet, third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live. Only the first two columns of the databases are the outputs of the model. The last column, viz. sentiment score, is not from the model deployed at sentiment.live. The LSTM model is still in development. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob has been appended as the last column. The sentiment scores of our model will be made available publicly after the project leads to a targetted summit.New databases will be released every two weeks.------current status------databases: 2,tweets count: 3 million-plus
DOI: 10.21227/k48m-t850
title: Twitter Sentiment Analysis

description: Each database (*.db) contain three columns. First column: date and time of the tweet, second column: tweet, third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live. Only the first two columns of the databases are the outputs of the model. The last column, viz. sentiment score, is not from the model deployed at sentiment.live. The LSTM model is still in development. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob has been appended as the last column. The sentiment scores of our model will be made available publicly after the project leads to a targetted summit.New databases will be released every two weeks.####current status###databases: 2,tweets count: 3 million-plus
DOI: 10.7910/dvn/eoc6oc/dw54fz
title: Dataset 1. Jeehp_16_29_raw data_Final Inputted Data.tab

description: :unav
DOI: 10.17632/ff6xtfzvyr.2
title: Data for: Question-Answering Dialogue System for Emergency Operations

description: The dialog records of Ask Diana
DOI: 10.17632/cxccgdt5jf.1
title: Data for: A Specific Combination of P Wave Duration and Morphology Accurately Predicts the Presence of Left Atrial Low Voltage Area in Patients with Atrial Fibrillation

description: all subjects clinical data used in this study
DOI: 10.21227/zryc-y237
title: Twitter Sentiment Analysis

description: Each database (*.db) contain three columns. First column: date and time of the tweet, second column: tweet, third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live. Only the first two columns of the databases are the outputs of the model. The last column, viz. sentiment score, is not from the model deployed at sentiment.live. The LSTM model is still in development. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob has been appended as the last column. The sentiment scores of our model will be made available publicly after the project leads to a targetted summit.New databases will be released every two weeks.####current status###databases: 2,tweets count: 3 million-plus The deep learning model is currently live at https://sentiment.live. (screenshot is embedded below)
DOI: 10.5281/zenodo.3514468
title: The analysis of differentially expressed genes relevant to glycolysis in hypoxia-treated gastric cancer cells

description: RNA sequencing was conducted to identify hypoxia-altered genes in GC cells. A total of 13,469 genes were included, with 430 genes upregulated and 476 genes downregulated. Gene Ontology (GO) analysis indicated that hypoxia-regulated genes mainly focused in glycolysis and canonical glycolysis pathways. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that hypoxia-regulated genes were significantly correlated with glycolysis pathway.
DOI: 10.5281/zenodo.3514469
title: The analysis of differentially expressed genes relevant to glycolysis in hypoxia-treated gastric cancer cells

description: RNA sequencing was conducted to identify hypoxia-altered genes in GC cells. A total of 13,469 genes were included, with 430 genes upregulated and 476 genes downregulated. Gene Ontology (GO) analysis indicated that hypoxia-regulated genes mainly focused in glycolysis and canonical glycolysis pathways. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that hypoxia-regulated genes were significantly correlated with glycolysis pathway.
DOI: 10.7910/dvn/n9xp0y/x8zhur
title: Australia_Laws_WithOverseas.tab

description: :unav
DOI: 10.21227/3575-yd70
title: 300-Dimensional Word Embeddings for Nepali Language

description: This pre-trained Word2Vec model has 300-dimensional vectors for more than 0.5 million Nepali words and phrases. A separate Nepali language text corpus was created using the news contents freely available in the public domain. The text corpus contained more than 100 million running words.Word2Vec model details: Embeddings Dimension: 300, Architecture: Continuous - BOW, Training algorithm: Negative sampling = 15, Context (window) size: 10, Token minimum count: 2, Encoded in UTF-8.
DOI: 10.21227/vev8-ex97
title: Twitter Sentiment Analysis

description: Each database (*.db) contain three columns. First column: date and time of the tweet, second column: tweet, third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live. Only the first two columns of the databases are the outputs of the model. The last column, viz. sentiment score, is not from the model deployed at sentiment.live. The LSTM model is still in development. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob has been appended as the last column. The sentiment scores of our model will be made available publicly after the project leads to a targetted summit.New databases will be released every two weeks.####current status###databases: 2,tweets count: 3 million-plus The deep learning model is currently live at https://sentiment.live.
DOI: 10.7910/dvn/n9xp0y/1cxl1m
title: NFS_replication_2019-10-23.R

description: :unav
DOI: 10.7910/dvn/n9xp0y/2wp5sb
title: ELA19Date.tab

description: :unav
DOI: 10.7910/dvn/n9xp0y/4ptwag
title: FamilyProject_replication_2019-10-23.R

description: :unav
DOI: 10.7910/dvn/n9xp0y/5lobta
title: ReadMe.txt

description: :unav
DOI: 10.7910/dvn/n9xp0y/6ztfl1
title: ELA20Date.tab

description: :unav
DOI: 10.7910/dvn/n9xp0y/batsst
title: Pill_replication_2019-09-19.R

description: :unav
DOI: 10.7910/dvn/n9xp0y/jnp3cl
title: ELA18Date.tab

description: :unav
DOI: 10.7910/dvn/n9xp0y/s7itci
title: MaleFemaleCombine_replication_2019-10-23.R

description: :unav
DOI: 10.7910/dvn/n9xp0y/t4hl8c
title: Abortion19Date.tab

description: :unav
DOI: 10.5281/zenodo.3518785
title: Mowgli: DBMS Performance & Scalability Evaluation Data Sets

description: These data sets contain the performance and scalability evaluation data created by the Mowgli framework for Apache Cassandra and Couchbase, operated on a private Openstack and the Amazon EC2 cloud.
DOI: 10.5281/zenodo.3518786
title: Mowgli: DBMS Performance & Scalability Evaluation Data Sets

description: These data sets contain the performance and scalability evaluation data created by the Mowgli framework for Apache Cassandra and Couchbase, operated on a private Openstack and the Amazon EC2 cloud.
DOI: 10.7910/dvn/ktxhew/xzodee
title: va_2015.zip

description: Statewide shapefile for Virginia.
DOI: 10.17632/f7yw6b7jp5.1
title: Data for: Marine litter on the Northern island of the Novaya Zemlya archipelago
DOI: 10.17632/tfh3f27ty7.1
title: Data for: Paleoenvironmental and paleoclimatic variations around Lake Van (Eastern Turkey) recorded by sedimentary source specific biomarkers 250- 130 ka (MIS7 and MIS6)
DOI: 10.7910/dvn/vnjab1/3pdbdu
title: va_2017.zip

description: Statewide results for Virginia.
DOI: 10.7910/dvn/b3ntzr/11atzv
title: documentation.txt

description: Data and sourcing information.
DOI: 10.7910/dvn/b3ntzr/ekcr0s
title: ks_2012.zip

description: Statewide results for Kansas.
DOI: 10.7910/dvn/eoc6oc/cvuwt9
title: Supple 2. Abstract recording.mp3

description: :unav
DOI: 10.5281/zenodo.3518509
title: Reduction of Structural Damage from the Thermal Expansion of Concrete Using Multifunctional Materials

description: Corresponding data set for Tran-SET Project No. 18STTAM01. Abstract of the final report is stated below for reference: "This study leveraged past successes in the analysis and design of shape memory alloy (SMA) components to address the issue of thermal expansion in concrete structures. Since the SMA used in the current work is relatively cheaper than other common SMAs (less than $50/lb compared to NiTi which is $200/lb due to difficulties in processing), it is anticipated that the findings of the study could be implemented in real infrastructures made of concrete, asphalt concrete, and other complex large infrastructure. Low-cost Fe-SMAs and other multifunctional materials can be considered as a replacement for components made of steel (e.g., in reinforced or plain jointed concrete pavements) to control distresses resulting from thermal expansion during seasonal/daily temperature change. This study conducted a series of finite element (FE) case studies of various configurations of concrete (blocks, slabs, and beams) with embedded, pre-strained SMA rods. This included developing new models to investigate temperature induced deflection in concrete slabs to analyze their curling behavior. It also included investigating the optimal position of the SMA rod and required rod radius. It is hoped that the results of this work could help to design smarter civil infrastructure incorporating multifunctional materials into established civil engineering materials."
description: Tran-SET Project No. 18STTAM01
DOI: 10.5281/zenodo.3518510
title: Reduction of Structural Damage from the Thermal Expansion of Concrete Using Multifunctional Materials

description: Corresponding data set for Tran-SET Project No. 18STTAM01. Abstract of the final report is stated below for reference: "This study leveraged past successes in the analysis and design of shape memory alloy (SMA) components to address the issue of thermal expansion in concrete structures. Since the SMA used in the current work is relatively cheaper than other common SMAs (less than $50/lb compared to NiTi which is $200/lb due to difficulties in processing), it is anticipated that the findings of the study could be implemented in real infrastructures made of concrete, asphalt concrete, and other complex large infrastructure. Low-cost Fe-SMAs and other multifunctional materials can be considered as a replacement for components made of steel (e.g., in reinforced or plain jointed concrete pavements) to control distresses resulting from thermal expansion during seasonal/daily temperature change. This study conducted a series of finite element (FE) case studies of various configurations of concrete (blocks, slabs, and beams) with embedded, pre-strained SMA rods. This included developing new models to investigate temperature induced deflection in concrete slabs to analyze their curling behavior. It also included investigating the optimal position of the SMA rod and required rod radius. It is hoped that the results of this work could help to design smarter civil infrastructure incorporating multifunctional materials into established civil engineering materials."
description: Tran-SET Project No. 18STTAM01