cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.11577/1616245
title: Ptychographic X-ray Speckle Tracking with Multi Layer Laue Lens Systems

description: This data-set consists of 121 projection images (or in-line holograms) of a diatom sample collected from the Antarctic sea floor. These images are highly magnified views of a small sub-region of the diatom.
DOI: 10.11577/1616246
title: Ptychographic X-ray Speckle Tracking with Multi Layer Laue Lens Systems

description: This data-set consists of 400 projection images (or in-line holograms) of a gold Siemens star (a typical optical test pattern).
DOI: 10.5281/zenodo.3773820
title: GGCMI Phase 2 masks and growing season input data

description: Growing season data for crops as supplied to modelers in the GGCMI Phase 2 experiment (Franke et al. 2020). Other than for wheat, which is split in spring wheat and winter wheat in Phase 2, the growing season input data is the same as in Phase 1 (Elliott et al. 2015). A boolean mask on what regions can be excluded from the simulations, modeling all crops and irrigation systems everywhere otherwise. References: Franke J, Müller C, Elliott J, Ruane AC, Jagermeyr J, Balkovic J, Ciais P, Dury M, Falloon P, Folberth C, Francois L, Hank T, Hoffmann M, Izaurralde RC, Jacquemin I, Jones C, Khabarov N, Koch M, Li M, Liu W, Olin S, Phillips M, Pugh TAM, Reddy A, Wang X, Williams K, Zabel F, and Moyer E. 2020, The GGCMI Phase II experiment: global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0), Geosci. Model Dev. Discuss., 2019, 1-30, doi: 10.5194/gmd-2019-237 Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015.
description: {"references": ["Franke J, M\u00fcller C, Elliott J, Ruane AC, Jagermeyr J, Balkovic J, Ciais P, Dury M, Falloon P, Folberth C, Francois L, Hank T, Hoffmann M, Izaurralde RC, Jacquemin I, Jones C, Khabarov N, Koch M, Li M, Liu W, Olin S, Phillips M, Pugh TAM, Reddy A, Wang X, Williams K, Zabel F, and Moyer E. 2020, The GGCMI Phase II experiment: global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0), Geosci. Model Dev. Discuss., 2019, 1-30, doi: 10.5194/gmd-2019-237", "Elliott J, M\u00fcller C, Deryng D, Chryssanthacopoulos J, Boote KJ, B\u00fcchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015."]}
DOI: 10.21227/70c2-3952
title: Corona Virus (COVID-19) Geolocation-based Sentiment Data

description: This dataset contains the geolocation-based sentiment analysis data. The data is generated 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 data sharing 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 with an exact location that contained any of the previously mentioned keywords.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.If you need the geolocation-based data starting March 20, 2020, then use "Corona Virus (COVID-19) Tweets Dataset" and hydrate the IDs, and add the following condition:if data["coordinates"] is true; then use longitude, latitude = data["coordinates"]["coordinates"]
DOI: 10.21227/f1tx-3080
title: Corona Virus (COVID-19) Geolocation-based Sentiment Data

description: This dataset contains the geolocation-based sentiment analysis data. The data is generated 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 data sharing 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 with an exact location that contained any of the previously mentioned keywords.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.If you need the geolocation-based data starting March 20, 2020, then use "Corona Virus (COVID-19) Tweets Dataset" and hydrate the IDs, adding the following condition:if data["coordinates"] is true; then use longitude, latitude = data["coordinates"]["coordinates"]
DOI: 10.26186/5ea8f68f24657
title: National Geochemical Survey of Australia: A Continental-Scale Pb Isotope Map of the Australian Regolith - Additional Dataset

description: The element Pb (lead) is a particularly useful tracer as it is both a toxin of environmental concern and often associated with mineralisation. Isotopic analyses as opposed to simple Pb concentration data provide an important advantage as they can allow discrimination between different sources of Pb. Lead isotope analyses have been conducted on catchment outlet sediment (similar to floodplain) samples obtained from Geoscience Australia’s National Geochemical Survey of Australia (NGSA; http://www.ga.gov.au/ngsa) regolith archive. The selected samples were Top Outlet Sediments (TOS) taken from 0 to 10 cm depth and sieved to &lt;2 mm. Analyses were performed by Candan Desem as part of her PhD research at the University of Melbourne using a Nu Instruments Attom Sector Field-Inductively Coupled Plasma-Mass Spectrometer (SF-ICP-MS). Analyses were undertaken on sequentially extracted samples, first after an ammonium acetate (AmAc) leach and then after a subsequent aqua regia (AR) digestion on all samples from the Northern Territory and those within the Exploring for the Future (EFTF) focus area into western Queensland. For the remaining States of Australia, only the samples obtained after the AR digestion step were presented to the SF-ICP-MS (the dried AmAc leachates were preserved for future analyses). These data allow the construction of the first continental-scale Pb isotope regolith map of Australia and can be used to understand contributions of Pb from underlying bedrock (including Pb-rich mineralisation), wind-blown dust and possibly from anthropogenic sources (industrial, transport, agriculture, residential, waste handling).
DOI: 10.21227/2r96-vq23
title: UP - Fall Detection

description: [17-APR-2020: WE ARE STILL UPLOADING THE DATASET, PLEASE WAIT UNTIL IT IS COMPLETED] -The dataset comprises a set of 11 different actions performed by 17 subjects that is created for multimodal fall detection. Five types of falls and six daily activities were considered in the experiment. Data collection comes from five wearable sensors, one brainwave helmet sensor, six infrared sensors around the room and two RGB-cameras. Three attempts per action were recorded. The dataset contains raw signals as well as three windowing-based feature sets. Furthermore, this dataset is associated to a published article. The latter presents main results for fall detection that can be used as baseline.
DOI: 10.21227/38d9-6t17
title: Corona Virus (COVID-19) Geolocation-based Sentiment Data

description: This dataset contains the geolocation-based sentiment analysis data. The data is generated 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 data sharing 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 with an exact location that contained any of the previously mentioned keywords.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.Primary Dataset: Corona Virus (COVID-19) Tweets DatasetIf you need the geolocation-based data starting March 20, 2020, then use the primary dataset and hydrate the IDs, adding the following condition:if data["coordinates"] is true; then use longitude, latitude = data["coordinates"]["coordinates"]
DOI: 10.21227/4wpb-n791
title: Corona Virus (COVID-19) Geolocation-based Sentiment Data

description: This dataset contains the geolocation-based sentiment analysis data. The data is generated 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 data sharing 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 with an exact location that contained any of the previously mentioned keywords.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.Primary Dataset: Corona Virus (COVID-19) Tweets DatasetIf you need the geolocation-based data starting March 20, 2020, then use the primary dataset and hydrate the IDs, adding the following condition:if data["coordinates"] is true; then use longitude, latitude = data["coordinates"]["coordinates"]
DOI: 10.21227/dxrs-j216
title: Corona Virus (COVID-19) Geolocation-based Sentiment Data

description: This dataset contains the geolocation-based sentiment analysis data. The data is generated 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 data sharing 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 with an exact location that contained any of the previously mentioned keywords.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.Primary Dataset: http://dx.doi.org/10.21227/781w-ef42If you need the geolocation-based data starting March 20, 2020, then use the primary dataset and hydrate the IDs, adding the following condition:if data["coordinates"] is true; then use longitude, latitude = data["coordinates"]["coordinates"]
DOI: 10.21227/fj4m-s066
title: Corona Virus (COVID-19) Geolocation-based Sentiment Data

description: This dataset contains the geolocation-based sentiment analysis data. The data is generated 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 data sharing 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 with an exact location that contained any of the previously mentioned keywords.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.If you're looking for COVID-19 tweets dataset: http://dx.doi.org/10.21227/781w-ef42If you need the geolocation-based data starting March 20, 2020, then use the primary dataset and hydrate the IDs, adding the following condition:if data["coordinates"] is true; then use longitude, latitude = data["coordinates"]["coordinates"]
DOI: 10.21227/mr9c-0947
title: Corona Virus (COVID-19) Geolocation-based Sentiment Data

description: This dataset contains the geolocation-based sentiment analysis data. The data is generated 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 data sharing 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 with an exact location that contained any of the previously mentioned keywords.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.If you're looking for Corona Virus (COVID-19) Tweets Dataset (en): http://dx.doi.org/10.21227/781w-ef42If you need the geolocation-based data starting March 20, 2020, then use the primary dataset and hydrate the IDs, adding the following condition:if data["coordinates"] is true; then use longitude, latitude = data["coordinates"]["coordinates"]
DOI: 10.21227/vgfb-gb50
title: Corona Virus (COVID-19) Geolocation-based Sentiment Data

description: This dataset contains the geolocation-based sentiment analysis data. The data is generated 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 data sharing 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 with an exact location that contained any of the previously mentioned keywords.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.If you're looking for Corona Virus (COVID-19) Tweets Dataset (en): http://dx.doi.org/10.21227/781w-ef42If you need the geolocation-based data starting March 20, 2020, then use the COVID-19 Tweets dataset and hydrate the IDs while adding the following condition:if data["coordinates"] is true; then use longitude, latitude = data["coordinates"]["coordinates"]
DOI: 10.25919/5ea8f94eab532
title: Parkes observations for project P1050 semester 2020APRS_04

description: This is a request for observing time for the initial follow-up of pulsar discoveries from the re-processing of the low-latitude Galactic plane section of the HTRU survey (P630). We have now completed a first pass re-processing of the 60 % of the survey with GPU based coherent acceleration search and template bank search pipelines, and have discovered potential 54 previously unknown pulsars. Interesting science can usually only be derived from a new pulsar after confirmation and a follow-up timing campaign is carried out. One year of initial timing is the minimum timespan required to fully characterise any newly-discovered pulsars, essential for deriving pulsar parameters such as the characteristic age, magnetic field strength, spin-down rate, as well as to detect any unexpected behaviour of the pulsar which might result from emission instabilities. This follow-up and timing project is necessary for following up on any interesting pulsar systems discovered from the HTRU Galactic plane survey. Since all of the pulsars on the observing list here are being followed-up for the first time, they will produce completely new and exciting results. In addition, this timing project will enable a large-scale examination of the Galactic plane pulsar population, exploring the true boundaries of pulsar parameter space.
DOI: 10.15468/vavh6z
title: New data on the genus Uroseius Berlese (Acari: Mesostigmata: Uropodina Trachytidae) with a redescription of U. sorrentinus (Lombardini, 1952)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Moraza, María L. (2019): New data on the genus Uroseius Berlese (Acari: Mesostigmata: Uropodina Trachytidae) with a redescription of U. sorrentinus (Lombardini, 1952). Zootaxa 4717 (1): 7-29, DOI: 10.11646/zootaxa.4717.1.4
DOI: 10.25345/c5cm4n
title: MassIVE MSV000085348 - Personal assessment of the external exposome of seniors in China
title: Personal inhalation exposure to contaminants was evaluated using a novel wearable passive air sampler known as the Fresh Air wristband. This wristband consisted of a silicone band that housed a polytetrafluoroethylene (PTFE) chamber. This PTFE chamber contained four custom fabricated PDMS sorbent bar which was used to passively absorb airborne chemicals. PDMS sorbent bars were custom fabricated and cleaned in a vacuum oven (2 h, 300 C) under of 0.1-0.3 L/min flow of high purity nitrogen (99.99) at Yale prior to use in the China BAPE Study. Cleaned PDMS sorbents were individually placed in microvial inserts and stored in air-tight 2mL amber glass vials with PTFE septa caps. PDMS sorbent bars were shipped to the Chinese Center for Disease Control and Prevention. Immediately prior to deployment, a member of the study team positioned four cleaned PDMS sorbent bars into the PTFE chamber and assembled the Fresh Air wristband. PDMS sorbent bars were secured in place using pre-cleaned neodymium magnets (KJ Magnetic, Plumsteadville, PA, USA). The wristbands were worn by participants for the three-day exposure assessment period. A member of the study team returned the PDMS sorbent bars from the wristband back to the glass storage vials using stainless steel forceps immediately following the sampling period. Samples were stored at -20 C in Beijing at the Chinese Center for Disease Control and Prevention and cold shipped to Yale for analysis. Similar sampling protocols were used to deploy the Fresh Air clip at the stationary air monitoring station operated in Jinan.
DOI: 10.34725/dvn/qxw0iv
title: Clean Merged Ag Biodiversity Farm, Nutrition and Consumption Data

description: Clean Merged Ag Biodiversity Farm, Nutrition and Consumption Data
DOI: 10.34725/dvn/qxw0iv/lnhwqm
title: MergedCleanedUganda_AgBioBaselineFarmNutrition.tab

description: Merged Cleaned Uganda Ag Biodiversity Baseline Farm, Nutrition and Consumption Data
DOI: 10.34725/dvn/qxw0iv/whwaoj
title: MergedCleanedEthiopia_AgBioBaselineFarmNutrition.tab

description: Merged Cleaned Ethiopia Ag Biodiversity Baseline Farm, Nutrition and Consumption Data
DOI: 10.34725/dvn/qxw0iv/yrmtvk
title: Disclaimer.pdf

description: Disclaimer
DOI: 10.6073/pasta/509b4f76c04cacf0eb4ab9a734bea669
title: Global lake area, climate, and population dataset

description: An increasing population in conjunction with a changing climate necessitates a detailed understanding of water abundance at multiple spatial and temporal scales. Remote sensing has provided massive data volumes to track fluctuations in water quantity, yet contextualizing water abundance with other local, regional, and global trends remains challenging by often requiring large computational resources to combine multiple data sources into analytically-friendly formats. To bridge this gap and facilitate future freshwater research opportunities, we harmonized existing global datasets to create the Global Lake area, Climate, and Population (GLCP) dataset. The GLCP is a compilation of lake surface area for 1.42+ million lakes and reservoirs of at least 10 ha in size from 1995 to 2015 with co-located basin-level temperature, precipitation, and population data. The GLCP was created with FAIR (findable, accessible, interoperable, reusable) data principles in mind and retains unique identifiers from parent datasets to expedite interoperability. The GLCP offers critical data for basic and applied investigations of lake surface area, and water quantity, at local, regional, and global scales.
DOI: 10.6092/unibo/amsacta/6310
title: PhD Thesis "Dealing with Uncertainty in Test Assembly". Supplementary material.

description: Supplementary material of the PhD Thesis entitled "Dealing with Uncertainty in Test Assembly" written by Giada Spaccapanico Proietti, PhD student in Statistical Sciences at University of Bologna, cycle XXXII. It contains data and results of the simulation studies and real data applications of Chapter 3 and 4.
DOI: 10.5281/zenodo.3774596
title: The relative influence of catchment and site variables on fish and macroinvertebrate richness in Cerrado biome streams. Supplemental Materials Table SM2

description: Table SM2. Environmental variable descriptions (mean, standard deviation and transformation applied) and<br> relationships with assemblage richness across all 80 sites
DOI: 10.5281/zenodo.3774597
title: The relative influence of catchment and site variables on fish and macroinvertebrate richness in Cerrado biome streams. Supplemental Materials Table SM2

description: Table SM2. Environmental variable descriptions (mean, standard deviation and transformation applied) and<br> relationships with assemblage richness across all 80 sites
DOI: 10.7910/dvn/4srs4x/dg4feq
title: res_reg_comprehensive-1.pdf

description: :unav
DOI: 10.7910/dvn/4srs4x/0xs9hh
title: gdp_growth-1.tab

description: :unav
DOI: 10.7910/dvn/4srs4x/glonj7
title: simulation_dat_inconsistent_1-1.tab

description: :unav
DOI: 10.34725/dvn/hsxr2s
title: R Scripts: Ag Biodiversity Farm Data for Uganda and Ethiopia

description: R Scripts used to clean, transform, wrangle farm data for Ag Biodiversity Project in Uganda and Ethiopia
DOI: 10.34725/dvn/hsxr2s/8omybc
title: Disclaimer.pdf

description: Disclaimer
DOI: 10.34725/dvn/hsxr2s/e8vp7i
title: AgBio Project Ethiopia.Rmd

description: Ag Biodiversity R Scripts for Ethiopia
DOI: 10.34725/dvn/hsxr2s/s8kpzc
title: AgBioProject_Uganda_RScripts.Rmd

description: Ag Biodiversity R Scripts for Uganda
DOI: 10.5281/zenodo.3774607
title: Data Associated with Manuscript Titled "Socio-hydrological System in the Remote Arid Highland of Baja California Sur, Mexico"

description: This is a dataset in support of analyses of water chemistry and social networks described and interpreted in the associated manuscript titled "Socio-hydrological System in the Remote Arid Highland of Baja California Sur, Mexico"
DOI: 10.7910/dvn/4srs4x/airfsb
title: 00_README-1

description: :unav
DOI: 10.7910/dvn/4srs4x/hewh7a
title: simulation_dat_very_vague_1-1.tab

description: :unav
DOI: 10.7910/dvn/4srs4x/hhieyc
title: NyhuisStoetzer_AmbguityEstimates_CHES_2006-18.csv

description: :unav
DOI: 10.34725/dvn/xifzie/8sbj4b
title: FoodTreesProjectFarmProductionDataDocumentation.R

description: R Script for the Food Trees Project Socioeconomic, farm production and food tree species diversity and richness: Preliminary data cleaning, development of indicators and data analysis: Laikipia County, Tharaka-Nithi County and Kitui County