cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5066/p9cp2nud
title: Inundation observations and inundation model predictions for vernal pools of the northeastern United States

description: This data release includes data-processing scripts, data products, and associated metadata for a study to model the hydrology of several hundred vernal pools (i.e., seasonal pools or ephemeral wetlands) across the northeastern United States. More information on this study is available from the project website. This data release consists of several components: (1) an input dataset and associated metadata document (&quot;pool_inundation_observations_and_climate_and_landscape_data&quot;); (2) an annotated R script which processes the input dataset, performs inundation modeling, and generates model predictions (&quot;annotated_R_script_for_pool_inundation_modeling.R&quot;); and (3) a model prediction dataset and associated metadata document (&quot;pool_inundation_predictions&quot;) for the model predictions generated by the annotated R script. The inundation modeling procedures are explained in detail in the metadata documents and annotated R script and are described briefly here. The input dataset includes approximately 3,000 field observations of inundation from approximately 450 vernal pools located across the northeastern United States. Observations of inundated depth, length, and width for pools were collected between May and July, from 2004 through 2016. From these observations, four binary inundation metrics were constructed. The H1 metric classified pools as inundated if any amount of water was observed, i.e. inundated depth and area greater than 0. The H2, H3, and H4 metrics classified pools as inundated if they had inundated depth greater than or equal to 5cm and area greater than or equal to 5m2, depth greater than or equal to 10cm and area greater than or equal to 15m2, and depth greater than or equal to 15cm and area greater than or equal to 25m2, respectively. For each inundation metric (H1 through H4), boosted regression tree models were constructed using the inundation metric as the response variable and pool attributes, weather and climate variables, and landscape characteristics as explanatory variables. These models were then used to generate inundation predictions for the H1 through H4 metrics at several seasonal time points and under a variety of weather and climate scenarios.
DOI: 10.15129/11baf0c3-eeca-41e7-9939-a31964062a0e
title: A 2015 Scottish SAM for UKCCSRC project

description: This is a 2015 Social Accounting Matrix (SAM) with the industrial sectors aggregated so that it enables a specific industrial decarbonisation project funded by the UK Carbon Capture and Storage Research Centre (UKCCSRC). The main body of this SAM has been created using the 2015 Scottish Input Output tables published by the Scottish Government as well as other government publications such as the Government Expenditure & Revenue Scotland (GERS) 2014/15 and 2015/16. This SAM was used to calibrate the AMOSENVI Scottish CGE model in order to study the economy-wide impacts of introducing industrial decarbonisation solutions that require costly capital investments.
DOI: 10.18716/nmrshiftdb2/77178
title: NMR data for InChI=1S/C5H10O/c1-2-3-5-4-6-5/h5H,2-4H2,1H3
DOI: 10.21227/qdn9-da49
title: Corona Virus (COVID-19) Geolocation-based Sentiment Data

description: This dataset is being upgraded. Check back tomorrow.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". In order to protect the privacy of users, 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: 10.18716/nmrshiftdb2/77191
title: NMR data for InChI=1S/C7H14O/c1-7(2,3)4-6-5-8-6/h6H,4-5H2,1-3H3
DOI: 10.5285/a2104adc-e98f-6789-e053-6c86abc0d557
title: Abundance of microbial phytoplankton through the water column during the AMT24 (JR20140922/JR303) cruise in September-November 2014.

description: This dataset contains depth profiles of the mean abundance of cyanobacteria and small (&lt;20 um) phytoplankton reported in cells per millilitre. Samples were measured using flow cytometry during the Atlantic Meridional Transect (AMT) cruise AMT24 (JR20140922/JR303). The samples were collected between September and November 2014 from 200 m to the surface, using a Seabird CTD system with a rosette of 24 Niskin bottles of 20 L volume. Samples were collected from the predawn and noon CTDs and were stored in a refrigerator and analysed within 3 hours of collection. In total 814 discrete samples were analysed and quantified using a Becton Dickinson FACSort flow cytometer which characterised and enumerated <em>Prochlorococcus</em> sp. and <em>Synechococcus</em> sp. (cyanobacteria) and pico- and eukaryotic phytoplankton, based on their light scattering and autofluorescence properties. Data were analysed onboard the ship during the cruise.
DOI: 10.18716/nmrshiftdb2/77182
title: NMR data for InChI=1S/C6H12O/c1-5(2)3-6-4-7-6/h5-6H,3-4H2,1-2H3
DOI: 10.18716/nmrshiftdb2/77192
title: NMR data for (4R,5R)-4,5-bis(iodomethyl)-1,3-dioxolane
DOI: 10.34725/dvn/s4oz49/u6q4qd
title: Disclaimer.pdf

description: Disclaimer
DOI: 10.5281/zenodo.3765167
title: Free-Breathing Self-Gated 4D-Lung MRI using wave-CAIPI

description: MRI raw data set of a volunteer examination for the project "Free-Breathing Self-Gated 4D-Lung MRI using wave-CAIPI". The upload includes the measured k-space for 8 different breathing phases, the respective image reconstructions and the coil sensitivity maps required for the Conjugate Gradient SENSE reconstruction. C++ source code for image reconstruction can be downloaded at https://github.com/expRad/4d_lung.
DOI: 10.5281/zenodo.3774043
title: Sample Attributes

description: This file contains the following: Sample Name, Sample Title, Bioproject_accession, Organism, Host, Isolation_source, Collection_date, Geo_loc_name, Lat_lon, Ref_biomaterial, rel_to_oxygen, samp_collect_device, samp_mat_process, samp_size, source_material_id, description, sequencing_replicate, concentration_after_ampl (ng/ul), ng_sequenced, read_count
DOI: 10.17632/hj6bn5dc87.8
title: Data for: New hydroperoxylated and 20,24-epoxylated dammarane triterpenes from the rot roots of Panax notoginseng

description: The raw 1D and 2D NMR data of new compounds 1-7
DOI: 10.18716/nmrshiftdb2/77176
title: NMR data for InChI=1S/C3H6O2/c4-1-3-2-5-3/h3-4H,1-2H2
DOI: 10.18716/nmrshiftdb2/77177
title: NMR data for InChI=1S/C6H12O/c1-2-3-4-6-5-7-6/h6H,2-5H2,1H3
DOI: 10.18716/nmrshiftdb2/77186
title: NMR data for InChI=1S/C3H4O2/c4-1-3-2-5-3/h1,3H,2H2
DOI: 10.18716/nmrshiftdb2/77188
title: NMR data for InChI=1S/C8H14O2/c1-3-9-4-2-7(1)5-8-6-10-8/h7-8H,1-6H2
DOI: 10.18716/nmrshiftdb2/77189
title: NMR data for InChI=1S/C6H10O3/c1(5-3-8-5)7-2-6-4-9-6/h5-6H,1-4H2
DOI: 10.5281/zenodo.3706831
title: Plant invasion has limited impact on soil microbial alpha-diversity : a meta-analysis

description: Plant invasion has proved to be a significant driver of ecosystem change, and with increased probability of invasion due to globalization, agricultural practices and other anthropogenic causes, it is crucial to understand its impact across multiple trophic levels. With the strong linkages between above and belowground processes, the response of soil microorganisms to plant invasion is the next logical step in developing our conceptual understanding of this complex system. In our study, we utilized a meta-analytical approach to better understand the impacts of plant invasion on soil microbial diversity. We synthesized 70 independent studies with 23 unique invaders across multiple ecosystem types to search for generalizable trends in soil microbial a-diversity following invasion. When possible, soil nutrient metrics were also collected in an attempt to understand the contribution of nutrient status shifts on microbial a-diversity. Our results show plant invasion to have highly heterogenous and limited impacts on microbial a-diversity. When taken together, our study indicates soil microbial a-diversity to remain constant following invasion, contrary to the aboveground counter parts. As our results suggest a decoupling in patterns of below and aboveground diversity, future work is needed to examine the drivers of microbial diversity patterns following invasion.
DOI: 10.5281/zenodo.3774392
title: Chap 4 - Appendix 1 - Specimens Overview

description: Overview of the specimens of <em>Pieris napi</em> used in Chapter 4 "<strong>Rear window: population genetics using museum specimens of <em>Pieris napi</em> (Lepidoptera)</strong>"
DOI: 10.5281/zenodo.3774393
title: Chap 4 - Appendix 1 - Specimens Overview

description: Overview of the specimens of <em>Pieris napi</em> used in Chapter 4 "<strong>Rear window: population genetics using museum specimens of <em>Pieris napi</em> (Lepidoptera)</strong>"
DOI: 10.18716/nmrshiftdb2/77183
title: NMR data for InChI=1S/C8H14O2/c1-2-4-9-7(3-1)5-8-6-10-8/h7-8H,1-6H2
DOI: 10.34725/dvn/ybbib3
title: Ag Biodiversity Endline Survey Nutrition Data in Uganda

description: Ag Biodiversity Endline Survey Nutrition Data in Uganda
DOI: 10.34725/dvn/ybbib3/hi4775
title: AgBioNutritionEndlineUganda.7z

description: Ag Biodiversity Endline Survey Nutrition Data in Uganda
DOI: 10.34725/dvn/ybbib3/utqx4h
title: Disclaimer.pdf

description: Disclaimer
DOI: 10.5281/zenodo.3773785
title: IO Islamic 3435. Ta'rîkh-i-'âlamârâi-'abbâsî, A Complete Copy of Iskandar Munshî's History of Shâh 'Abbâs the Great

description: IO Islamic 3435. Ta’rîkh-i-‘âlamârâi-‘abbâsî, A Complete Copy of Iskandar Munshî’s History of Shâh ‘Abbâs the Great
DOI: 10.5281/zenodo.3773786
title: IO Islamic 3435. Ta'rîkh-i-'âlamârâi-'abbâsî, A Complete Copy of Iskandar Munshî's History of Shâh 'Abbâs the Great

description: IO Islamic 3435. Ta’rîkh-i-‘âlamârâi-‘abbâsî, A Complete Copy of Iskandar Munshî’s History of Shâh ‘Abbâs the Great
DOI: 10.21227/k1jd-wc22
title: Corona Virus (COVID-19) Geolocation-based Sentiment Data

description: This dataset is being upgraded. Check back tomorrow.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 the 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/aret-v856
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: 10.34725/dvn/0i2ras/dvwmab
title: AgBioNutritionEndlineEthiopia.7z

description: Ag Biodiversity Endline Survey Nutrition Data in Ethiopia
DOI: 10.34725/dvn/0i2ras/vbnhlj
title: Disclaimer.pdf

description: Disclaimer
DOI: 10.14469/hpc/7147
title: TMS o3lyp/Def-2 TZVPP scrf=(cpcm,solvent=toluene) NMR Reference

description: Gaussian Calculation
DOI: 10.21227/y2a2-ns16
title: Corona Virus (COVID-19) Geolocation-based Sentiment Data

description: This dataset is being upgraded. Check back tomorrow.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: 10.25919/5ea7f07685b9f
title: Canberra HDR Sky Images 2015/12 - December 2015

description: High dynamic range (HDR) 360 degree sky colour images taken every ten seconds (resolution 648 x 486 pixels) and every minute (resolution 2592 x 1944 pixels) from two cameras in Canberra.
DOI: 10.34725/dvn/0i2ras
title: Ag Biodiversity Endline Survey Nutrition Data in Ethiopia

description: Ag Biodiversity Endline Survey Nutrition Data in Ethiopia
DOI: 10.3886/e119156
title: Self-efficacy and fatigue among health care workers during COVID-19 outbreak A moderated mediation model of posttraumatic stress disorder symptoms and negative coping

description: Health care workers (HCWs) fighting Coronavirus Disease 2019 (COVID-19) are not immune to psychological problems. The project aimed to explore the posttraumatic stress disorder (PTSD) symptoms, fatigue, anxiety of health care workers and its influential factors regarding self-efficacy and coping. Furthermore, the project anticipated to shed light on the potential interventions and elighten the health care services during the pandemic. <br>
DOI: 10.15161/oar.it/23616
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.5281/zenodo.3774404
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: 10.15468/9hurwj
title: Three new goatfishes of the genus Upeneus (Mullidae) from the Indo-Pacific, with a redescription of colour patterns in U. margarethae

description: This dataset contains the digitized treatments in Plazi based on the original journal article Uiblein, Franz, Gledhill, Daniel C., Pavlov, Dimitri A., Hoang, Tuan Anh, Shaheen, Shaker (2019): Three new goatfishes of the genus Upeneus (Mullidae) from the Indo-Pacific, with a redescription of colour patterns in U. margarethae. Zootaxa 4683 (2): 151-196, DOI: https://doi.org/10.11646/zootaxa.4683.2.1
DOI: 10.21227/hh8q-en43
title: Aerial Lidar Dataset of an Indoor Stockpile Warehouse

description: Dataset of rosbags collected during autonomous drone flight inside a warehouse of stockpiles. PCD files created using reconstruction method proposed by article.Data still being move to IEEE-dataport.
DOI: 10.3886/e119159
title: Effects of Region, Epidemic Stage, and Demographic Characteristics on Sleep Quality and Mental Disturbances among Health Care Workers during COVID-19 Outbreak

description: The objective is to investigate the prevalence of sleep quality and mental disturbances of medical staff and identify the effects of region, epidemic stage, and demographic characteristics during the COVID-19 outbreak in China.<br>Self-administered questionnaire were sent to health care workers (HCWs) in China from 30 Jan to 2 March, 2020. The Pittsburgh Sleep Quality Index, the Patient Health Questionare-9, the Generalized Anxiety Disorder-7 and the Impact of Event Scale were used to assess sleep quality, depression symptoms, anxiety symptoms and Post-traumatic stress disorder (PTSD) of HCWs, respectively. The influencing factors of psychological and sleep disturbances were identified by univariate analysis and multiple regression. Research found that HCWs had poorer sleep quality on stage 2 and 3 of the outbreak. HCWs in Hubei had poorer sleep quality but lighter depression condition. gender, age, occupation and status of having children were associated with sleep and mental health. Mental health programs should be considered for HCWs especially those with specific characteristics.
DOI: 10.3886/e119159v1
title: Effects of Region, Epidemic Stage, and Demographic Characteristics on Sleep Quality and Mental Disturbances among Health Care Workers during COVID-19 Outbreak

description: The objective is to investigate the prevalence of sleep quality and mental disturbances of medical staff and identify the effects of region, epidemic stage, and demographic characteristics during the COVID-19 outbreak in China.<br>Self-administered questionnaire were sent to health care workers (HCWs) in China from 30 Jan to 2 March, 2020. The Pittsburgh Sleep Quality Index, the Patient Health Questionare-9, the Generalized Anxiety Disorder-7 and the Impact of Event Scale were used to assess sleep quality, depression symptoms, anxiety symptoms and Post-traumatic stress disorder (PTSD) of HCWs, respectively. The influencing factors of psychological and sleep disturbances were identified by univariate analysis and multiple regression. Research found that HCWs had poorer sleep quality on stage 2 and 3 of the outbreak. HCWs in Hubei had poorer sleep quality but lighter depression condition. gender, age, occupation and status of having children were associated with sleep and mental health. Mental health programs should be considered for HCWs especially those with specific characteristics.
DOI: 10.3886/e119162
title: UnintendedEffectsObservations

description: Read Me, Stata do file, and Data Access file for "The Unintended Effects of Policy-Assigned Teacher Observations: Examining the Validity of Observation Scores" published in AERA Open.
DOI: 10.3886/e119162v1
title: UnintendedEffectsObservations

description: Read Me, Stata do file, and Data Access file for "The Unintended Effects of Policy-Assigned Teacher Observations: Examining the Validity of Observation Scores" published in AERA Open.
DOI: 10.5281/zenodo.3773751
title: Dynamic Percussion Dataset

description: This dataset was recorded as a part of my Bachelor's thesis work for Tampere University. It is a collection of samples from different drums and percussion, recorded in an anechoic room. More details on the structure, labels and recording process can be found in [1]. In the thesis, this dataset was visualized in the 2-dimensional space with a method (described in [1]) that utilizes OpenL3 deep audio embeddings [2] and t-SNE dimensionality reduction algorithm [3]. Since the embeddings can take a significant time to compute without a sufficient CUDA environment installed, they are provided with the dataset (in a later version). <strong>Audio format:</strong><br> Data type: wav<br> Sampling rate: 44,1 kHz<br> Bit-depth: 24<br> Channels: 1 <em>Added in version 1.1.0:</em> OpenL3 pre-calculated embeddings have been added. Several different models were used to calculate the embeddings. Each file corresponds to a single pickle file containing the embeddings. The pickle files contain a metadata dictionary which has information about the original sample and the OpenL3 model used to calculate that particular embedding series. NOTE: The reference [1] will be added when the thesis has been published.
description: {"references": ["[2] Jason Cramer et al. (2019). Look, Listen and Learn More: Design Choices for Deep Audio Embeddings", "[3] Van der Maaten et al. (2008). t-Distributed Stochastic Neighbor Embedding."]}
DOI: 10.5281/zenodo.3773752
title: Dynamic Percussion Dataset

description: <strong>Note</strong>: latest version (1.1.0) here This dataset was recorded as a part of my Bachelor's thesis work for Tampere University. It is a collection of samples from different drums and percussion, recorded in an anechoic room. More details on the structure, labels and recording process can be found in [1]. In the thesis, this dataset was visualized in the 2-dimensional space with a method (described in [1]) that utilizes OpenL3 deep audio embeddings [2] and t-SNE dimensionality reduction algorithm [3]. Since the embeddings can take a significant time to compute without a sufficient CUDA environment installed, they are provided with the dataset (in a later version). <strong>Audio format:</strong><br> Data type: wav<br> Sampling rate: 44,1 kHz<br> Bit-depth: 24<br> Channels: 1 NOTE: The reference [1] will be added when the thesis has been published.
description: {"references": ["Jason Cramer et al. (2019). Look, Listen and Learn More: Design Choices for Deep Audio Embeddings", "Van der Maaten et al. (2008). t-Distributed Stochastic Neighbor Embedding."]}
DOI: 10.5281/zenodo.3774717
title: MU Density GUI e2e data

description: MU Density GUI e2e data
DOI: 10.5281/zenodo.3774021
title: Excessive Daytime Sleepiness

description: Data collected from university students in health courses at a private institution of higher education located in the state of Mato Grosso, in the Midwest region of Brazil, which assessed the prevalence of excessive daytime sleepiness, chronotype and social factors.
DOI: 10.5281/zenodo.3774022
title: Excessive Daytime Sleepiness

description: Data collected from university students in health courses at a private institution of higher education located in the state of Mato Grosso, in the Midwest region of Brazil, which assessed the prevalence of excessive daytime sleepiness, chronotype and social factors.
DOI: 10.11577/1616244
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.