cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8422952
title: Electrification shares of trucks

description: XX_Tno',' daily truck number for XX scenario XX_TnoF',' Fast chargers number for XX scenario XX_TnoS', slow chargers number for XX scenario<br>
DOI: 10.5281/zenodo.8432881
title: Electrification shares of trucks

description: XX_Tno',' daily truck number for XX scenario XX_TnoF',' daily truck number for XX scenario using fast chargers XX_TnoS', daily truck number for XX scenario using slow chargers XX_FC, Fast chargers number for XX scenario XX_SC, Slow chargers number for XX scenario
DOI: 10.6084/m9.figshare.25057739
title: Spasic: PBMC and Bone Marrow Immunoprofiling data files - spectral flow cytometry data used for development of three immunoprofiling panels in blood and bone marrow.

description: Data files for development of spectral flow cytometry pipelines for immunoprofiling of PBMCs and bone marrow cells. There are two panels for use on peripheral blood mononuclear cells (PBMCs) titled: 'TB' and 'MND'. There is a third panel for use on bone marrow cells. Data are segmented by cell type (PBMC or bone marrow), and then panel used on those samples. Both sample data as well as reference controls used are included in each subfolder. These files were used in our manuscript titled "Spectral Flow Cytometry Methods and Pipelines for Comprehensive Immunoprofiling of Human Peripheral Blood and Bone Marrow."
DOI: 10.34740/kaggle/dsv/7455597
title: Video Games Playing Reason
DOI: 10.13012/b2idb-2128477_v1
title: Sequencing data for migration rate selection experiments (0.2% agar, 1mM sugar)

description: BAM files for evolved strains from migration rate selection experiments conducted in low viscosity (0.2% w/v) agar plates containing M63 minimal medium with 1mM of mannose, melibiose, N-acetylglucosamine or galactose
DOI: 10.5066/p91uoarw
title: USGS North American Packrat Midden Database, Version 5.0

description: This data release contains the data tables for the USGS North American Packrat Midden Database (version 5.0).  This version of the Midden Database contains data for 3,331 packrat midden samples obtained from published sources (journal articles, book chapters, theses, dissertations, government and private industry reports, conference proceedings) as well as unpublished data contributed by researchers.  Compared to the previous version of the Midden Database (i.e., ver. 4), this version of the database (ver. 5.0) has been expanded to include more precise midden-sample site location data, calibrated midden-sample age data, and plant functional type (PFT) assignments for the taxa in each midden sample.  In addition, World Wildlife Fund ecoregion and major habitat type (MHT) assignments (Ricketts and others, 1999) and modern climate and bioclimate data (New and others, 2002; Davis and others, 2017) are provided for each midden-sample site location.  References  Davis, T.W., Prentice, I.C., Stocker, B.D., Thomas, R.T., Whitley, R.J., Wang, H., Evans, B.J., Gallego-Sala, A.V., Sykes, M.T., and Cramer, W., 2017, Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture.  Geoscientific Model Development, v. 10, p. 689-708, https://doi.org/10.5194/gmd-10-689-2017.  New, M., Lister, D., Hulme, M., and Makin, I., 2002, A high-resolution data set of surface climate over global land areas: Climate Research, v. 21, p. 1?25, http://www.cru.uea.ac.uk/cru/data/hrg.  Ricketts, T.H., Dinerstein, E., Olson, D.M., Loucks, C.J., Eichbaum, W., DellaSala, D., Kavanagh, K., Hedao, P., Hurley, P.T., Carney, K.M., Abell, R., and Walters, S., 1999, Terrestrial ecoregions of North America?A conservation assessment: Washington, D.C., Island Press, 485 p.
DOI: 10.5281/zenodo.10571557
title: Supporting Dataset (Tables S1-S7, Figure S1) for "Heavy-mineral grain counting: Counting techniques, error estimation, and the number of grains to be counted"

description: This Dataset comprises Tables S1-S7 for the article "Heavy-mineral grain counting: Counting techniques, error estimation, and the number of grains to be counted" authored by Jan Schönig and submitted to Journal of Geophysical Research: Earth Surface.

Figure S1: Error of different counting methods in comparison with theory and without considering the finite population correction

Table S1: Heavy-mineral dataset from semi-automated Raman analysis

Table S2: Summary of heavy-mineral composition for individual samples in percent

Table S3: Computed ribbon compositions for individual samples and ribbon sizes given in numbers of counts

Table S4: Relative errors at 95 % quantile for consecutive ribbon counting simulations

Table S5: Relative errors at 95 % quantile for maximum distance ribbon counting simulations

Table S6: Relative errors at 95 % quantile for cluster counting simulations

Table S7: Numerical solution for determining the number of counts required to discriminate the content of two mineral species in an aliquot
DOI: 10.5281/zenodo.8116933
title: Supporting Dataset (Tables S1-S7, Figure S1) for "Heavy-mineral grain counting: Counting techniques, error estimation, and the number of grains to be counted"

description: This Dataset comprises Tables S1-S7 for the article "Heavy-mineral grain counting: Counting techniques, error estimation, and the number of grains to be counted" authored by Jan Schönig and submitted to Journal of Geophysical Research: Earth Surface.

Figure S1: Error of different counting methods in comparison with theory and without considering the finite population correction

Table S1: Heavy-mineral dataset from semi-automated Raman analysis

Table S2: Summary of heavy-mineral composition for individual samples in percent

Table S3: Computed ribbon compositions for individual samples and ribbon sizes given in numbers of counts

Table S4: Relative errors at 95 % quantile for consecutive ribbon counting simulations

Table S5: Relative errors at 95 % quantile for maximum distance ribbon counting simulations

Table S6: Relative errors at 95 % quantile for cluster counting simulations

Table S7: Numerical solution for determining the number of counts required to discriminate the content of two mineral species in an aliquot
DOI: 10.5281/zenodo.8116934
title: Supporting Dataset (Tables S1-S6) for "Cluster Counting: A practical approach for bias reduction in heavy-mineral grain counting"

description: This Dataset comprises Tables S1-S6 for the article <strong>"Cluster Counting: A practical approach for bias reduction in heavy-mineral grain counting"</strong> authored by Jan Schönig and submitted to J<em>ournal of Geophysical Research: Earth Surface</em>. Table S1: Heavy-mineral dataset from semi-automated Raman analysis Table S2: Summary of heavy-mineral composition for individual samples in percent Table S3: Computed ribbon compositions for individual samples and ribbon sizes given in numbers of counts Table S4: Relative errors at 95 % quantile for consecutive ribbon counting simulations Table S5: Relative errors at 95 % quantile for maximum distance ribbon counting simulations Table S6: Relative errors at 95 % quantile for cluster counting simulations
DOI: 10.6084/m9.figshare.25078127
title: Untitled Item

description: The aim of this study was to examine the predictive role of parental and child socio-demographic factors, as well as giftedness factors (learning, motivation, creativity and leadership), on emotional and behavioural disorders,. A total of 182 parents completed the demographic questionnaire, the Renzulli-Hartman Scale and the CBCL questionnaire. In addition to giftedness the parental age, the fathers’ education level and chronic illnesses are significant predictors of emotional and behavioural disorders. Giftedness has a greater predictive power for internalization symptoms than for externalization symptoms, in line with previous studies.
DOI: 10.5281/zenodo.10397820
title: Hydrogen-Bonded Organic Frameworks Based on Endless-Stacked Amides for Iodine  Capture and Detection

description: Raw data of
DOI: 10.5281/zenodo.8377965
title: Hydrogen-Bonded Organic Frameworks Based on Endless-Stacked Amides for Iodine  Capture and Detection

description: Raw data of
DOI: 10.5281/zenodo.8377966
title: Hydrogen-Bonded Organic Frameworks Based on Endless-Stacked Amides for Iodine Capture and Detection

description: Raw data
DOI: 10.34740/kaggle/dsv/7488104
title: Balochi Language to English Translation Dataset
DOI: 10.25919/f8bj-pp06
title: Parkes observations for project P595 semester 2023OCTS_08

description: We propose to continue the PULSE@Parkes project in which high school students from Australia and around the World use the Parkes radio telescope in a remote observing model to observe and analyse pulsar data. The data from some PULSE@Parkes observations are used to support the space missions, other observations supplement the P456 Pulsar Timing Array project and the remainder were chosen in order to make a detailed analysis of pulsar timing irregularities and intermittency.
DOI: 10.6075/j02v2g98
title: Virtual Talking Circle Study Data

description: Collected using Qualtrics software. Entered, coded and analyzed using SPSS version 27 software.
description: De-identified data from virtual talking circle survey corresponds to associated publication, Student Perceptions: How Virtual Student-led Talking Circles Promote Engagement, Social Connectedness, and Academic Benefit.
DOI: 10.25919/c3h8-3711
title: Parkes observations for project P960 semester 2023OCTS_08

description: We aim to carry out receiver pointing and characterisation tests of Parkes receivers. Typically, these tests are carried out after the receiver package is installed, but time does not always allow for proper characterisation. Receivers for consideration include UWL, MARS and 13MM packages. In addition to cross-referencing C007 for flux calibration via pointing, we aim to conduct gain-elevation and system temperature experiments using the Moon, to ensure users of the Parkes Observatory have the most accurate information available for calibration purposes.
DOI: 10.5281/zenodo.10562872
title: Dataset for Study Long-Term Changes in Urban Bird Populations: Correspondence of the Trends between Urban and Rural Areas

description: Datasets used for a study "Long-term changes in urban bird populations: correspondence of the trends between urban and rural areas".

In the code (deposited in Zenodo at https://doi.org/10.5281/zenodo.10573789) follow the instructions and use these datasets where indicated. Find the detailed dataset information below.

1.       “Bird_spec_dt.csv” is used for calculating long term population trends for urban and rural environments.

Legend: 

Type (rural/urban) refers to the type of environment of the point.

POID refers to the unique number for individual points.

Transect refers to the number of transect in JPSP program.

Point refers to number of the point within transect in JPSP program.

Year refers to the year of count of the individual species on individual point.

Lat and Lon refer to geographical position of the point.

Species refers to the scientific name of the bird.

Euring refers to a unique code for the species in Euring database. 

Count refers to the maximum count of a species at a specific point in a given year.

2.       “Bird_spec_trends.csv” is used to test relationship of the trends in rural and urban environments.

Legend:

species refers to the species scientific name.

Trend (_R/_U) refers to the species trends slope.

Trend (_R/_U)_SE refers to the standard error of the trends.

z.ratio (_R/_U) refers to the z ratio of the trends.

p.value (_R/_U) refers to the p-value of the trends.

asymp.LCL (_R/_U) refers to the lower confidence interval limit of the trends. 

asymp.UCL (_R/_U) refers to the upper confidence interval limit of the trends. 

3.       “Bird_spec_traits.csv” is used to test relationships of species traits and their trends in urban/rural environments.

Legend:

Spec refers to the scientific species name abbreviations.

Full_name refers to the common species name.

Type [Urban (Urb)/rural (Rur)] refers to environment from which the trend was obtained.

SE_trend refers to the standard errors of the trends.

Hab_position refers to the habitat niche position which is defined as the position of each species along the gradient from forest interior (value = 1) to open treeless landscape (value = 7) and obtained from Hanzelka et al. (2019).

Wet refers to the species association with wetland habitats (WetN – not associated, WetY – associated) obtained from Honzelka et al. (2019).

BM refers to the body mass, which is mean body weight of the species in grams obtained from Hořák and Storchová (2018).
DOI: 10.5281/zenodo.10562873
title: Dataset for Study Long-Term Changes in Urban Bird Populations: Correspondence of the Trends between Urban and Rural Areas

description: Datasets used for a study "Long-term changes in urban bird populations: correspondence of the trends between urban and rural areas".

In the code (deposited in Zenodo at https://doi.org/10.5281/zenodo.10573789) follow the instructions and use these datasets where indicated. Find the detailed dataset information below.

1.       “Bird_spec_dt.csv” is used for calculating long term population trends for urban and rural environments.

Legend: 

Type (rural/urban) refers to the type of environment of the point.

POID refers to the unique number for individual points.

Transect refers to the number of transect in JPSP program.

Point refers to number of the point within transect in JPSP program.

Year refers to the year of count of the individual species on individual point.

Lat and Lon refer to geographical position of the point.

Species refers to the scientific name of the bird.

Euring refers to a unique code for the species in Euring database. 

Count refers to the maximum count of a species at a specific point in a given year.

2.       “Bird_spec_trends.csv” is used to test relationship of the trends in rural and urban environments.

Legend:

species refers to the species scientific name.

Trend (_R/_U) refers to the species trends slope.

Trend (_R/_U)_SE refers to the standard error of the trends.

z.ratio (_R/_U) refers to the z ratio of the trends.

p.value (_R/_U) refers to the p-value of the trends.

asymp.LCL (_R/_U) refers to the lower confidence interval limit of the trends. 

asymp.UCL (_R/_U) refers to the upper confidence interval limit of the trends. 

3.       “Bird_spec_traits.csv” is used to test relationships of species traits and their trends in urban/rural environments.

Legend:

Spec refers to the scientific species name abbreviations.

Full_name refers to the common species name.

Type [Urban (Urb)/rural (Rur)] refers to environment from which the trend was obtained.

SE_trend refers to the standard errors of the trends.

Hab_position refers to the habitat niche position which is defined as the position of each species along the gradient from forest interior (value = 1) to open treeless landscape (value = 7) and obtained from Hanzelka et al. (2019).

Wet refers to the species association with wetland habitats (WetN – not associated, WetY – associated) obtained from Honzelka et al. (2019).

BM refers to the body mass, which is mean body weight of the species in grams obtained from Hořák and Storchová (2018).
DOI: 10.34740/kaggle/dsv/7488191
title: Forex Exchange Rates Since 2004 (Updated Daily)
DOI: 10.6084/m9.figshare.25050464.v4
title: swat

description: This is a test dataset.
DOI: 10.13012/b2idb-1917934_v1
title: Trained models for multi-task multi-dataset learning for text classification in tweets

description: Trained models for multi-task multi-dataset learning for text classification in tweets.
Classification tasks include sentiment prediction, abusive content, sarcasm, and veridictality.

Models were trained using: https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_classification.py
See https://github.com/socialmediaie/SocialMediaIE and https://socialmediaie.github.io for details.

If you are using this data, please also cite the related article:
Shubhanshu Mishra. 2019. Multi-dataset-multi-task Neural Sequence Tagging for Information Extraction from Tweets. In Proceedings of the 30th ACM Conference on Hypertext and Social Media (HT '19). ACM, New York, NY, USA, 283-284. DOI: https://doi.org/10.1145/3342220.3344929
DOI: 10.13012/b2idb-0934773_v1
title: Trained models for multi-task multi-dataset learning for sequence prediction in tweets

description: Trained models for multi-task multi-dataset learning for sequence tagging in tweets.
Sequence tagging tasks include POS, NER, Chunking, and SuperSenseTagging.

Models were trained using: https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_experiment.py
See https://github.com/socialmediaie/SocialMediaIE and https://socialmediaie.github.io for details.

If you are using this data, please also cite the related article: 
Shubhanshu Mishra. 2019. Multi-dataset-multi-task Neural Sequence Tagging for Information Extraction from Tweets. In Proceedings of the 30th ACM Conference on Hypertext and Social Media (HT '19). ACM, New York, NY, USA, 283-284. DOI: https://doi.org/10.1145/3342220.3344929
DOI: 10.6084/m9.figshare.25080668
title: A1 10X PL FL （4）_1_001.png

description: 研究数据
DOI: 10.6084/m9.figshare.25080668.v1
title: A1 10X PL FL （4）_1_001.png

description: 研究数据
DOI: 10.60955/fszr-5q79
title: CP-NET Clinical Database Platform: Genomic Architecture of CP (Whole Genome Sequencing)
DOI: 10.5066/p14umyjm
title: Amplicon sequencing of pollen foraged by Bombus affinis for compositional analysis

description: This study generated genetic 'barcode' data using high-throughput sequencing to characterize pollen collected by the endangered rusty-patched bumblebee, Bombus affinis. Pollen samples were associated with a single Bombus affinis colony within a structure in Red Wing, Minnesota, USA, as previously described by Boone et al. (2022).

This data release consists of two tab-delimited files:
1) sample.metadata.txt includes sample identifiers and accessions assigned by the National Center for Biotechnology Information (NCBI), the authoritative repository for publicly funded genetic data in the United States. These accessions can be used individually to obtain raw sequencing data or sample information at www.ncbi.nih.nlm.gov. Alternatively, the BioProject accession PRJNA1045705 can be searched to retrieve the full set of data and sample accessions listed in the file. Entity and attribute metadata are provided for this file.
2) taxon.counts.txt includes the inferred counts, or number of sequences, attributable to each identified taxon in each sample. Taxa are in rows and sequencing libraries are in columns. Taxa are listed by scientific name and the taxonomic rank of that scientific name. A numeric taxonomic identifier used by NCBI for each taxon is also provided, as the taxonomic identifier is unique in the NCBI databases whereas scientific names may not be. Entity and attribute data are not provided for this file due to its size and repetitive content.
DOI: 10.34740/kaggle/dsv/7488233
title: Top Spotify Songs in 73 Countries (Daily Updated)
DOI: 10.5517/ccdc.csd.cc2fbsk4
title: CCDC 2244696: Experimental Crystal Structure Determination

description: Related Article: Hang Peng, Qin Liu, Yan-Zi Lu, Shu-Jing Yang, Jun-Chao Qi, Xiao-Gang Chen, Wei-Qiang Liao|2023|Chem.Commun.|59|10295|doi:10.1039/D3CC02798H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6073/pasta/065ae8d1797bd0a646c46936ed8d4585
title: PBB02 Belowground Plot Experiment: Biomass and nutrient content of Rhizomes

description: To address the potential interactive effects of fire, aboveground biomass removal, and nutrient amendments on above- and belowground responses, a long-term field experiment was initiated in 1986 as part of the Konza Prairie Long-Term Ecological Research (LTER) program. The general goals of this experiment are: 1) to document both short- and long-term responses of plants and soils to fire, aboveground biomass removal (a surrogate for grazing in these small plots), and nutrient amendments (additions of N and/or P); and 2) to provide a better understanding of the mechanisms underlying tallgrass prairie responses to fire, aboveground biomass removal and nutrient enrichment. Standing crops of live and dead rhizomes (0.1 sq. m2 * 20cm deep samples) are taken in late summer periodically from 64 belowground plots. N and P content are determined on live and dead rhizomes. N and P for forb rhizomes are available for some plots in some years.
DOI: 10.5281/zenodo.10528900
title: Temporal host–symbiont dynamics in community contexts: Impact of host fitness and vertical transmission efficiency on symbiosis prevalence

description: Symbiotic associations are pivotal in plant ecology and evolution, with outcomes depending on partners' traits and environmental context. Here, we compiled a dataset from a three-year study focusing on an annual plant species that forms symbiosis with a vertically transmitted fungal endophyte. The study was conducted in stands situated in two different vegetation communities: HMM, characterized by productivity and low-stress conditions, and HP, exhibiting less productivity and high-stress conditions.
DOI: 10.6073/pasta/3f94769bbaa4303d9e56f1b4e73507c1
title: PBB03 Belowground Plot Experiment: Belowground plot experiment: biomass and nutrient content of Roots

description: To address the potential interactive effects of fire, aboveground biomass removal, and nutrient amendments on above- and belowground responses, a long-term field experiment was initiated in 1986 as part of the Konza Prairie Long-Term Ecological Research (LTER) program. The general goals of this experiment are: 1) to document both short- and long-term responses of plants and soils to fire, aboveground biomass removal (a surrogate for grazing in these small plots), and nutrient amendments (additions of N and/or P); and 2) to provide a better understanding of the mechanisms underlying tallgrass prairie responses to fire, aboveground biomass removal and nutrient enrichment. Standing crops of live and dead grass roots(0.1 sq. m2  x 20cm deep samples) are taken in late summer periodically from 64 belowground plots. N and P content are determined on live and dead grass roots. N and  P concentrations for forb roots are available for some plots in some years.
DOI: 10.5281/zenodo.10528899
title: Temporal host–symbiont dynamics in community contexts: Impact of host fitness and vertical transmission efficiency on symbiosis prevalence

description: Symbiotic associations are pivotal in plant ecology and evolution, with outcomes depending on partners' traits and environmental context. Here, we compiled a dataset regarding a three-year study focusing on an annual plant species that forms symbiosis with a vertically transmitted fungal endophyte. The study was conducted in stands situated in two different vegetation communities: HMM, characterized by productivity and low-stress conditions, and HP, exhibiting less productivity and high-stress conditions.
DOI: 10.5281/zenodo.10529744
title: Temporal host–symbiont dynamics in community contexts: Impact of host fitness and vertical transmission efficiency on symbiosis prevalence

description: Symbiotic associations are pivotal in plant ecology and evolution, with outcomes depending on partners' traits and environmental context. Here, we compiled a dataset regarding a three-year study focusing on an annual plant species that forms symbiosis with a vertically transmitted fungal endophyte. The study was conducted in stands situated in two different vegetation communities: HMM, characterized by productivity and low-stress conditions, and HP, exhibiting less productivity and high-stress conditions.