cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28675166.v1
title: yxin_camarosa.syri.vcf

description: This Variant Call Format (VCF) file contains genome-wide structural variants identified between two strawberry (<i>Fragaria </i>× <i>ananassa</i>) cultivars, 'Yuexin' and 'Camarosa', using SyRI v1.6.337. The comparison was performed with 'Yuexin' as the reference genome.'Yuexin'was selected from the crossing of ‘0362’（‘Camarosa’×‘Akihime’）× ‘Sachinoka’, thus has the closely genetic background with ‘Camarosa’. The VCF file documents sequence variations including SNPs, insertions, deletions, and other structural variants between these two commercially important strawberry cultivars.
DOI: 10.3535/d3z-ws1-7d6
title: Brachiopoda Duméril, 1805

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/p9w-h7e-6b2
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.6084/m9.figshare.12084645.v1
title: Thermal biology and growth of bison (Bison bison) along the Great Plains: testing four theories of endotherm body size: dataset

description: This is the supporting dataset for: Martin and Barboza 2020-Thermal biology and growth of bison (Bison bison) along the Great Plains: testing four theories of endotherm body size<br>Abstract-Body size of bison (<i>Bison bison</i>) declines with rising global temperature across the fossil record and rising annual temperatures across the Great Plains, but what are the underlying drivers? Body size depends on growth, which depends on maximizing net energy and nutrient flows for the production of tissues at seasonal scales across the range of the species. We measured thermoregulation costs of body surface temperature (°C) and heat exchanges (W and W•m<sup>˗2</sup>) of 350 adult and 345 adolescent <i>Bison</i> from 19 herds in summer and winter along the Great Plains from Saskatchewan (52 °N) to Texas (30 °N). At the smallest scale, daily body surface temperature increased with solar radiation and decreased with relative humidity and wind speed, which is consistent with Kooijman’s dynamic energy budget theory. Total surface heat transfer (W) increased with body mass (kg) at an exponent of 0.63 ± 0.03, which is consistent Schmidt-Nielsen’s principal of surface area to volume ratios (<i>b</i>=0.67). On an annual scale, growth (kg•y<sup>-1</sup>) of adolescent <i>Bison</i> decreased with increasing total surface heat transfer (W) during summer, which supports Speakman and Król’s heat dissipation limit theory<i>.</i> On the largest scale, heat flux was weakly related to latitude in summer and winter for adolescent <i>Bison</i>, which provides support for Bergmann’s rule and suggests a role for local primary production along the Great Plains. Cooler summers are more optimal for <i>Bison</i> growth because of reduced heat loads during the growing season. Rising temperatures are likely to constrain body size and productivity of <i>Bison</i> and other large endotherms in North America.
DOI: 10.6084/m9.figshare.28028492.v1
title: <b>Out-of-pocket expenditure and financial risks associated with treatment of chronic kidney disease in Ethiopia: A prospective cohort costing analysis</b>

description: <b>Out-of-pocket expenditure and financial risks associated with treatment of chronic kidney disease in Ethiopia: A prospective cohort costing analysis</b>
DOI: 10.3535/g0h-y9e-e17
title: Animalia

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/h6f-pgy-v71
title: Conochitina aff. iklaensis Nestor, 1980

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.6084/m9.figshare.13426880.v3
title: Ash-throated_Flycatcher_species_counts

description: Species data for the Ash-throated Flycatcher from the Oregon 2020 project: Robinson, WD, Hallman, TA, Curtis JR (2020) Benchmarking the avian diversity of Oregon in an era of rapid change. Northwestern Naturalist 101(3): In Press.<br>Data associated with "A comparison of remotely sensed environmental predictors for avian distributions" (2022).<br>
DOI: 10.6084/m9.figshare.8217209.v1
title: Data dictionary

description: Meta-data and data dictionary for associated raw data file.
DOI: 10.17632/vpb7vfrdn8
title: Hourly NO2 concentrations from surface stations

description: This dataset is for hourly NO2 concentrations from surface stations: Marginal Tietê - Ponte Remédios and Guarulhos- Pimentas, in the metropolitan region of São Paulo and at the station Porta San Felice, located in the urban area of the city of Bologna.

The data for the stations located in São Paulo were extracted from the platform of the Environmental Company of the State of São Paulo (CETESB, 2022) and from the Agenzia Prevenzione Ambiente Energia- Emilia Romagna (ARPAE, 2022) for the station located in Bologna.


ARPAE - Agenzia Prevenzione Ambiente Energia emilia romagna, 2022. Available at: Estrazione dati dalla rete regionale di qualità dell&#39;aria. https://dati.arpae.it/dataset/qualita-dell-aria-rete- dihttps://dati.arpae.it/dataset/qualita-dell-aria-rete-di-monitoraggio/resource/70da49ad-46d6-4945-
90ee-4149016a40bf/monitoraggio/resource/70da49ad-46d6-4945-90ee-4149016a40bf./ (Accessed: October 13, 2022).


CETESB. QUALAR - AIR QUALITY INFORMATION SYSTEM.
https://qualar.cetesb.sp.gov.br/qualar/relConfig. caoEstacao.do?method=gerarRelatorio/ (Accessed on: October 13, 2022.)
DOI: 10.3535/9yy-yyb-x23
title: Brachiopoda Duméril, 1805

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/d1t-as8-2xh
title: Brachiopoda Duméril, 1805

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/lyr-79x-7kc
title: †Eoplectodonta Kozlowski, 1929

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.6084/m9.figshare.26528110.v2
title: File_S9_SpeciesTrees

description: This dataset include all species trees made by Asteroid and Concatenation (ortholog selection included in EukPhylo), for the 500 conserved OG in EukPhylo study, at each step of the Contamination Loop; with Alignment, Trees, Colored Trees, and PDFs.
DOI: 10.6084/m9.figshare.27130026.v1
title: Dataset on the Effects of Mercury, Lead, and Copper Stress on Green Bean (Phaseolus vulgaris) Germination and Seedling Growth

description: This dataset contains experimental data on the effects of heavy metal stress (mercury, lead, and copper) on the germination and seedling growth of green beans (Phaseolus vulgaris). The study investigates the impact of varying concentrations of these heavy metals on key growth parameters such as germination potential, germination rate, root and shoot length, fresh and dry weight, and the accumulation of soluble carbohydrates and proteins. The findings reveal that mercury has the most pronounced inhibitory effect on both germination and seedling growth, while lead stress at certain concentrations may promote seedling dry weight, indicating potential adaptive responses. This dataset provides valuable insights into the physiological adaptation mechanisms of green beans under heavy metal stress, contributing to broader research on plant resilience and environmental pollution mitigation
DOI: 10.6084/m9.figshare.c.3694420_d1.v1
title: Additional file 1: Table S1. of Comparative expression profile of microRNAs in Anopheles anthropophagus midgut after blood-feeding and Plasmodium infection

description: Known miRNAs in Anopheles anthropophagus midgut. (XLSX 14 kb)
DOI: 10.3535/k2f-rpf-dff
title: Brachiopoda Duméril, 1805

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/xwr-ys5-y4j
title: †Triplesia Hall, 1859

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.6084/m9.figshare.15152475.v2
title: 16S rRNA sequences

description: 16S rRNA sequences generated from shotgun metagenome data. The PhyloFlash file contain contigs assembled via the program Spades that was wrapped by the program Phyloflash. The files "All..." and "Five..." are 16S rRNA sequences assembled by MegaHit and were binned to one of the 16 bins by MaxBin2. The "Five_16SrRNA_from_bins.fa" file has 16S rRNA sequences that were correctly binned based on the bins and the 16S sequence taxonomic identity.
DOI: 10.6084/m9.figshare.28400843.v3
title: CTO-DO Raw Data

description: This data is collected by a Seabird SBE-37 Conductivity, Temperature, Dissolved-Oxygen instrument attached to the JASCO ALTO ocean bottom lander.The AEON network concentrates all observation assets in the Gulf of Maine and totals 5 observation nodes. Node locations are determined based on an in-depth analysis of current regional assets and collaborations, bathymetry and associated sound propagation models, location historical and predicted oceanographic features, and ecosystem relevance.
DOI: 10.3535/rwv-ftg-62s
title: Minerals

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/se4-fng-jzc
title: Brachiopoda Duméril, 1805

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.6084/m9.figshare.1369526.v1
title: MicroRNAs expression profile of C4-2B cells with/without metformin treatment

description: MicroRNA array was performed with total RNA from C4-2B cells (n=3) treated with or without metformin (final concentration=5 mM) for 48h.
DOI: 10.17632/w3wjpd3zf4
title: Treatment of keloids with a single dose of low energy superficial X-ray radiation to prevent recurrence after surgical excision: an in vitro and in vivo study

description: Supplementary information and supplementary figures for the original article published in the Journal of American Academy of Dermatology in the year 2020.
DOI: 10.3535/eb8-0sp-520
title: Calcite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/7bm-0ab-msb
title: Specimen from Tallinn University of Technology

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/awp-09n-mj7
title: Brachiopoda Duméril, 1805

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.6084/m9.figshare.20038056.v1
title: "I'M NOT ASKING WHAT I CAN'T GIVE": SIMMETRIC MASCULINITY IN VIRTUAL HOMOEROTICISM

description: Abstract The article is about the representations of the male homoerotic practices among users of two virtual environments: the UOL chat rooms and the social network Manhunt. We can perceive a tension between a model based on the heterosexual matrix, that aligns top to male and bottom to female and another that seeks to affirm masculinity, regardless of sexual position, trying to erase the effeminacy of whom holds the bottom position in sex. However, interviews with some of the users and personal experiences, as well as postings of the participants in which they debate the homoerotic online market, demonstrate how the classifications game is situational and oscillating: my point is that speech transit on the network and in "real" life does not allow us to plaster the categories; the performativities unfolding in the virtual world are always incomplete and open to breaks and displacements.
DOI: 10.6084/m9.figshare.28112039.v1
title: Snails activity preliminary study

description: The presented data are the results of experiments that were used to write the article titled "Winter activity and dormancy of snails: freezing and food shortage avoidance strategy facing snow cover shortage."
DOI: 10.17632/6bj6y4f9p8
title: LINF_110017700

description: Eukaryotic translation termination factor 3; Leishmania infantum
DOI: 10.3535/a9b-3f5-hyk
title: Conochitina mamilla

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/lxg-lhg-s9z
title: Specimen from Tallinn University of Technology

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.57783/1eft-t162
title: NHAMCS

description: Katie Nash, kan2123@cumc.columbia.edu

description: NHAMCS
DOI: 10.57783/v5df-d977
title: MSCH Electronic Medical Records

description: Katie Nash, kan2123@cumc.columbia.edu

description: MSCH Electronic Medical Records
DOI: 10.57783/2dgj-7644
title: Society of Thoracic Surgeons Adult Cardiac Surgery Database

description: Paul Kurlansky, pk2245@cumc.columbia.edu


description: Society of Thoracic Surgeons Adult Cardiac Surgery Database
DOI: 10.57783/8qk7-2305
title: UNOS Database

description: Paul Kurlansky, pk2245@cumc.columbia.edu

description: UNOS Database
DOI: 10.57783/yssf-gc86
title: Aortic Center Database

description: Paul Kurlansky, pk2245@cumc.columbia.edu

description: Aortic Center Database
DOI: 10.57761/12wf-3c87
title: Bidding and Tendering Data of China 中国各地区招投标数据

description: If you have questions or concerns, please contact Zhaohui Xue at zhxue@stanford.edu.
description: The Bidding and Tendering Data of China was released on the Chinese Open Data Platform (CnOpenData). The dataset integrates bidding information from more than 100 bidding websites. It also includes a large amount of bidding and tendering information from unofficial platforms. Variables include unit, agency, winning bidder, process status, process status annotation, creation date, tender opening date, industry type, project number, project title, province, budget, etc.
description: The raw data were wrangled for inclusion in Data Farm. For more information, please see[ CnOpenData GitLab.](<https://code.stanford.edu/sul-cidr/cnopendata>)
DOI: 10.57761/8nay-jn59
title: Bidding and Tendering Data of China 中国各地区招投标数据

description: If you have questions or concerns, please contact Zhaohui Xue at zhxue@stanford.edu.
description: The Bidding and Tendering Data of China was released on the Chinese Open Data Platform (CnOpenData). The dataset integrates bidding information from more than 100 bidding websites. It also includes a large amount of bidding and bidding information from unofficial platforms. Variables include unit, agency, winning bidder, process status, process status annotation, creation date, tender opening date, industry type, project number, project title, province, budget, etc.
DOI: 10.57761/t87y-6b63
title: Bidding and Tendering Data of China 中国各地区招投标数据

description: If you have questions or concerns, please contact Zhaohui Xue at zhxue@stanford.edu.
description: The Bidding and Tendering Data of China was released on the Chinese Open Data Platform (CnOpenData). The dataset integrates bidding information from more than 100 bidding websites. It also includes a large amount of bidding and tendering information from unofficial platforms. Variables include unit, agency, winning bidder, process status, process status annotation, creation date, tender opening date, industry type, project number, project title, province, budget, etc.
description: The raw data were wrangled for inclusion in Data Farm. For more information, please see[ CnOpenData GitLab.](<https://code.stanford.edu/sul-cidr/cnopendata>)
DOI: 10.57761/1rbe-8334
title: Subsidy Map
DOI: 10.57761/50sy-re92
title: Many Babies 1
DOI: 10.57761/9w6t-ez46
title: Many Babies 1
DOI: 10.57761/q6k5-pw88
title: Many Babies 1
DOI: 10.57783/6vef-9m57
title: L2 Uniform Voter Data
DOI: 10.60603/d3301g
title: L2 National Voter File

description: graham straus gpstraus@g.ucla.edu
description: This table is a stacked national voter file where each row has a voter in an election. The state column has two-letter state abbreviations and the LALVOTERID tracks voters within a state. This table is constructed from dozens of voter files we receive from L2 over time, timestamped 2014 - 2021. 

We record full turnout history using the most recent version of the voter file we have for a voter. However, we record the voter demographics (address, party information, commercial data) using the voterfile most proximate to but following the election. For instance, a voter who appears in the Illinois voter file from elections in 2016 to 2020 will have rows for each election. L2_filename and file_date contain information about the file we use to source demographics. 


For example: 

voter id state. elec_date elec_type. voted file_date address
LALX il 11/03/2020 G Y 2021-03-05 202 ...
LALX il 11/06/2018 G Y 2019-05-14 202 ...
LALX il 11/08/2016 G Y 2017-03-17 101 ...
description: The columns of the voter files are as follows:

**state** \- 2 letter state code

**LALVOTERID** \- L2 voter id

**elec\_date** \- date of election

**elec\_type **\- P for primary or G for general

**voted **\- Y for voted, null for no vote

**L2\_filename **\- the file from which demographics are drawn

**file\_date** \- the date parsed from the L2\_filename



**Voters\_\*** & **Ethnic\_Description** & **Parties\_Description** \- voter x election demographic information



**Residence\_Addresses\_\*** \- voter's registered address information



**Precinct, City, County, Congressional, State House and State Senate Districts **\- voter's geographies



**CommercialData **\- commercial data that L2 purhcases and merges on to the state supplied voting information
DOI: 10.60603/d3pp4s
title: L2 National Voter File

description: graham straus gpstraus@g.ucla.edu
description: This table is a stacked national voter file where each row has a voter in an election. The state column has two-letter state abbreviations and the LALVOTERID tracks voters within a state. This table is constructed from dozens of voter files we receive from L2 over time, timestamped 2014 - 2021. 

We record full turnout history using the most recent version of the voter file we have for a voter. However, we record the voter demographics (address, party information, commercial data) using the voterfile most proximate to but following the election. For instance, a voter who remains registered in Illinois and appears in the Illinois voter file from 2016 to 2020 will have 6 rows to their name: primaries and generals in 2016, 2018, and 2020. If the voter moved houses in 2018 and reregistered, our strategy picks up on that. 

L2_filename and file_date contain information about the file we use to source demographics. 


For example: 

voter id state. elec_date elec_type. voted file_date address
LALX il 11/03/2020 G Y 2021-03-05 202 ...
LALX il 11/06/2018 G Y 2019-05-14 202 ...
LALX il 11/08/2016 G Y 2017-03-17 101 ...
description: The columns of the voter files are as follows:

**state** \- 2 letter state code

**LALVOTERID** \- L2 voter id

**elec\_date** \- date of election

**elec\_type **\- P for primary or G for general

**voted **\- Y for voted, null for no vote

**L2\_filename **\- the file from which demographics are drawn

**file\_date** \- the date parsed from the L2\_filename



**Voters\_\*** & **Ethnic\_Description** & **Parties\_Description** \- voter x election demographic information



**Residence\_Addresses\_\*** \- voter's registered address information



**Precinct, City, County, Congressional, State House and State Senate Districts **\- voter's geographies



**CommercialData **\- commercial data that L2 purhcases and merges on to the state supplied voting information
DOI: 10.60603/d3z590
title: L2 National Voter File

description: graham straus gpstraus@g.ucla.edu
description: This table is a stacked national voter file where each row has a voter in an election. The state column has two-letter state abbreviations and the LALVOTERID tracks voters within a state. This table is constructed from dozens of voter files we receive from L2 over time, timestamped 2014 - 2021. 

We record full turnout history using the most recent version of the voter file we have for a voter. However, we record the voter demographics (address, party information, commercial data) using the voterfile most proximate to but following the election. For instance, a voter who appears in the Illinois voter file from elections in 2016 to 2020 will have rows for each election. L2_filename and file_date contain information about the file we use to source demographics. 


For example: 

voter id state. elec_date elec_type. voted file_date address
LALX il 11/03/2020 G Y 2021-03-05 202 ...
LALX il 11/06/2018 G Y 2019-05-14 202 ...
LALX il 11/08/2016 G Y 2017-03-17 101 ...
description: The columns of the voter files are as follows:

**state** \- 2 letter state code

**LALVOTERID** \- L2 voter id

**elec\_date** \- date of election

**elec\_type **\- P for primary or G for general

**voted **\- Y for voted, null for no vote

**L2\_filename **\- the file from which demographics are drawn

**file\_date** \- the date parsed from the L2\_filename



**Voters\_\*** & **Ethnic\_Description** & **Parties\_Description** \- voter x election demographic information



**Residence\_Addresses\_\*** \- voter's registered address information



**Precinct, City, County, Congressional, State House and State Senate Districts **\- voter's geographies



**CommercialData **\- commercial data that L2 purhcases and merges on to the state supplied voting information
DOI: 10.57761/4ck2-9n69
title: Numberbank
DOI: 10.57761/5z1b-2r03
title: Numberbank
DOI: 10.57761/ajnr-7773
title: Numberbank
DOI: 10.57761/jg82-1m64
title: Numberbank
DOI: 10.60603/d3k01t
title: L2 National Voter File

description: graham straus gpstraus@g.ucla.edu
description: This table is a stacked national voter file where each row has a voter in an election. The state column has two-letter state abbreviations and the LALVOTERID tracks voters within a state. This table is constructed from dozens of voter files we receive from L2 over time, timestamped 2014 - 2021. 

We record full turnout history using the most recent version of the voter file we have for a voter. However, we record the voter demographics (address, party information, commercial data) using the voterfile most proximate to but following the election. For instance, a voter who remains registered in Illinois and appears in the Illinois voter file from 2016 to 2020 will have 6 rows to their name: primaries and generals in 2016, 2018, and 2020. If the voter moved houses in 2018 and reregistered, our strategy picks up on that. 

L2_filename and file_date contain information about the file we use to source demographics. 


For example: 

voter id state. elec_date elec_type. voted file_date address
LALX il 11/03/2020 G Y 2021-03-05 202 ...
LALX il 11/06/2018 G Y 2019-05-14 202 ...
LALX il 11/08/2016 G Y 2017-03-17 101 ...
description: The columns of the voter files are as follows:

**state** \- 2 letter state code

**LALVOTERID** \- L2 voter id

**elec\_date** \- date of election

**elec\_type **\- P for primary or G for general

**voted **\- Y for voted, null for no vote

**L2\_filename **\- the file from which demographics are drawn

**file\_date** \- the date parsed from the L2\_filename



**Voters\_\*** & **Ethnic\_Description** & **Parties\_Description** \- voter x election demographic information



**Residence\_Addresses\_\*** \- voter's registered address information



**Precinct, City, County, Congressional, State House and State Senate Districts **\- voter's geographies



**CommercialData **\- commercial data that L2 purhcases and merges on to the state supplied voting information