cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28444583.v1
title: SP-LCC — a dataset on the structure and properties of lignin-carbohydrate complexes

description: Database of structure and properties of 95 lignin-carbohydrate complex samples. The data is structured in the following way:

· Sample ID, biorefinery processing conditions, yield, lignin moieties identified from 2D HSQC NMR, and physiochemical properties for all samples are available in the CSV file named “SP-LCC_table.csv”. The file is structured as described in Table 1.

· Intensities of the NMR spectra are contained in the archive “NMR_spectra.zip”, which contains one file for the intensities per sample named “NMRspectrum_{sampleID}.csv”. The corresponding chemical shifts, at which these intensities were measured, are contained in the same archive NMR_spectra.zip in two files per sample, for the x-axis (F1 C axis) “xarr_{sampleID}.csv” and the y-axis (F2 H axis) “yarr_{sampleID}.csv”, where {sampleID} refers to the sample ID of the respective sample. The files are structured as described in Table 2.

· The CSV files containing the NMR data were generated from Bruker files, which we make available, too, in the “NMR_spectra_Bruker.zip” archive. In the archive, every subfolder is named by the sample ID of the respective sample.

· For full transparency, we also include python scripts to parse and plot the NMR spectra both from the Bruker files, with “parse_and_plot_Bruker_NMR_spectrum.py”, and from the .csv files with “plot_NMR_spectrum.py”. The dependencies for their use are described in the file “readme.txt”.

· Chromatograms are contained in the archives “RI_Chromatogram.zip” and “LS_Chromatogram.zip” with one file per sample named “RI_Chromatogram_{sampleID}.csv” the refractive index and “LS_Chromatogram_{sampleID}.csv” for light scattering chromatograms. For parallel measurements, “_2” has been added to the file name. The files are structured as described in Table 2. Additional details of the chromatogram data are described in section 2 of the Supplementary information.

· Thermograms are contained in the archives “TD_Thermogram.zip” and “Tg_Thermogram.zip” with one file per sample named “Tg_Thermogram_{sampleID}.csv” for thermograms obtained from DSC analyses and “TD_Thermogram_{sampleID}.csv” obtained from TGA analyses. For parallel measurements, “_2” has been added to the file name. The files are structured as described in Table 2. Additional details of the thermogram data are described in section 2 of the Supplementary information.
DOI: 10.15468/dl.bwe88p
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hippodonta hungarica (Grunow) Lange-Bertalot, Metzeltin & Witkowski",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((15.4784 -23.4046,15.4933 -24.0715,20.509 -24.0715,20.509 -24.0455,20.0952 -23.5072,19.9968 -23.017,19.7843 -22.5405,19.1988 -21.8785,18.5972 -21.5447,17.8859 -21.3993,17.1626 -21.4805,16.5332 -21.7596,16.1257 -22.0882,15.8234 -22.4662,15.606 -22.8993,15.4784 -23.4046))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045452-250525065834625/datasets/export for details.
DOI: 10.15468/dl.ppz26h
title: Occurrence Download

description: A dataset containing 18886 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rhamnus saxatilis Jacq.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 18886 records from 258 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045454-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28692440
title: Electroluminescence measurement of microscale light-emitting diode wafers using a three-dimensional flexible probe head

description: The data corresponding to figure 1 to 4 in the manuscript.
DOI: 10.6084/m9.figshare.28692440.v1
title: Electroluminescence measurement of microscale light-emitting diode wafers using a three-dimensional flexible probe head

description: The data corresponding to figure 1 to 4 in the manuscript.
DOI: 10.15468/dl.gbtshz
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hippodonta hungarica (Grunow) Lange-Bertalot, Metzeltin & Witkowski",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((25.5395 -28.5786,20.509 -28.5786,20.509 -32.9007,25.5395 -32.9007,25.5395 -28.5786))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045456-250525065834625/datasets/export for details.
DOI: 10.15468/dl.56y9a3
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elegia grandispicata H.P.Linder",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((20.7576 -24.3688,20.529 -24.0715,20.509 -24.0715,20.509 -28.5786,25.5395 -28.5786,25.5395 -24.0715,25.1306 -24.0715,24.9333 -24.4571,23.8962 -24.2467,22.8065 -24.295,22.3719 -24.7207,22.2323 -25.547,22.0387 -25.7188,21.7833 -25.7784,21.5388 -25.7113,21.3553 -25.5415,20.7576 -24.3688))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045455-250525065834625/datasets/export for details.
DOI: 10.6084/m9.figshare.27368070
title: Sea ice records over more than a century at an observatory facing the Okhotsk coast of Hokkaido, Japan

description: <i>Time series of sea ice dates and amounts at Abashiri.</i>

The dataset contains four directories:<br>
<b>raw_data</b>, <b>csv_data</b>, <b>codes</b>, and <b>figs</b>.<br>

<b>raw_data</b> directory contains manually created data in an ascii format:<br>
<b>Si_dates_1892-2025.txt</b>: yearly first and last sea ice dates (FD_sight, FD_shore, LD_sight, and LD_shore) during 1892-2025.<br>
<b>Total_ice_1956-2021.txt</b>: daily total ice amounts during 1956-2021.<br>
<b>Drift_ice_1930-2021.txt</b>: daily drift ice amounts during 1930-2021.<br>
In <b>Total_ice_1956-2021.txt</b> and <b>Drift_ice_1930-2021.txt</b>, six monthly lines with daily values from previous December to May are described for each year; for the daily data, <code>0</code>-<code>9</code> denote the area fraction in quantile, <code>*</code> denotes full ice cover (10), <code>+</code> and <code>-</code> denote 0+ and 10-, respectively.<br>

<b>csv_data</b> directory contains data files in a csv format:<br>
<b>Si_dates_1892-2025.csv</b>, <b>Total_ice_1956-2021.csv</b>, and <b>Drift_ice_1930-2021.csv</b>.<br>
Dates are described in an ISO 8601 format ("YYYY-MM-DD"; "0000-00-00" denotes date not defined; "9999-99-99" denotes missing observation). For total/drift ice amounts, <code>*</code>, <code>-</code>, and <code>+</code> are converted to 10.0, 9.75, and 0.25, respectively.<br>

<b>codes</b> directory contains shared codes:<br>
<b>txt2csv_dates.py</b> converts <b>Si_dates_1892-2025.txt</b> to <b>Si_dates_1982-2025.csv</b>.<br>
<b>txt2csv_ice.py</b> converts ice amount files (.txt to .csv).<br>
<b>plot_fig6a.py</b> and <b>plot_fig7a.py</b> plot ice amount time series (Figs. 6a and 7a) from the csv files.<br>
<b>cumul_dev_si_dates.F90</b> (or <b>cumul_dev_si_dates.py</b>) calculates cumulative deviation from the mean (CDM) values, which are written as <b>cumul_dev_si_dates.dat</b> (<b>cumul_dev_si_dates.csv</b>).<br> <b>cumul_dev_si_dates_plot.gs</b> (<b>plot_cumul_dev_si_dates.py</b>) draws a plot for the CDM time series.<br>

<b>figs</b> directory contains figures.
DOI: 10.6084/m9.figshare.27368070.v1
title: Sea ice records over more than a century at an observatory facing the Okhotsk coast of Hokkaido, Japan

description: <i>Time series of sea ice dates and amounts at Abashiri.</i>

The dataset contains four directories:<br>
<b>raw_data</b>, <b>csv_data</b>, <b>codes</b>, and <b>figs</b>.<br>

<b>raw_data</b> directory contains manually created data in an ascii format:<br>
<b>Si_dates_1892-2025.txt</b>: yearly first and last sea ice dates (FD_sight, FD_shore, LD_sight, and LD_shore) during 1892-2025.<br>
<b>Total_ice_1956-2021.txt</b>: daily total ice amounts during 1956-2021.<br>
<b>Drift_ice_1930-2021.txt</b>: daily drift ice amounts during 1930-2021.<br>
In <b>Total_ice_1956-2021.txt</b> and <b>Drift_ice_1930-2021.txt</b>, six monthly lines with daily values from previous December to May are described for each year; for the daily data, <code>0</code>-<code>9</code> denote the area fraction in quantile, <code>*</code> denotes full ice cover (10), <code>+</code> and <code>-</code> denote 0+ and 10-, respectively.<br>

<b>csv_data</b> directory contains data files in a csv format:<br>
<b>Si_dates_1892-2025.csv</b>, <b>Total_ice_1956-2021.csv</b>, and <b>Drift_ice_1930-2021.csv</b>.<br>
Dates are described in an ISO 8601 format ("YYYY-MM-DD"; "0000-00-00" denotes date not defined; "9999-99-99" denotes missing observation). For total/drift ice amounts, <code>*</code>, <code>-</code>, and <code>+</code> are converted to 10.0, 9.75, and 0.25, respectively.<br>

<b>codes</b> directory contains shared codes:<br>
<b>txt2csv_dates.py</b> converts <b>Si_dates_1892-2025.txt</b> to <b>Si_dates_1982-2025.csv</b>.<br>
<b>txt2csv_ice.py</b> converts ice amount files (.txt to .csv).<br>
<b>plot_fig6a.py</b> and <b>plot_fig7a.py</b> plot ice amount time series (Figs. 6a and 7a) from the csv files.<br>
<b>cumul_dev_si_dates.F90</b> (or <b>cumul_dev_si_dates.py</b>) calculates cumulative deviation from the mean (CDM) values, which are written as <b>cumul_dev_si_dates.dat</b> (<b>cumul_dev_si_dates.csv</b>).<br> <b>cumul_dev_si_dates_plot.gs</b> (<b>plot_cumul_dev_si_dates.py</b>) draws a plot for the CDM time series.<br>

<b>figs</b> directory contains figures.
DOI: 10.6084/m9.figshare.27966036
title: 3.5 million years of Tibetan Plateau vegetation dynamics in response to climate change

description: Biome reconstructions of the Tibetan Plateau for the last 3.5 million years based on fossil pollen data of cores ZB13-C2 and ZB19-C1 from the Zoige Basin.
DOI: 10.6084/m9.figshare.27966036.v1
title: 3.5 million years of Tibetan Plateau vegetation dynamics in response to climate change

description: Biome reconstructions of the Tibetan Plateau for the last 3.5 million years based on fossil pollen data of cores ZB13-C2 and ZB19-C1 from the Zoige Basin.
DOI: 10.15468/dl.ehcsfv
title: Occurrence Download

description: A dataset containing 497 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Russian Federation, Mongolia, China, Korea (Democratic People’s Republic of), Korea, Republic of, Japan)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Gulo gulo (Linnaeus, 1758)"
 ]
}

 The dataset includes 497 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045457-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15654318
title: DC magnetic field measurement of FY-3E satellite

description:  This dataset contains the data files supporting the paper "The Distributions and Temporal Variations of FACs During Substorms and Quiet Periods", these data are in ". csv" format. The contents are as follows:

1. Trajectory of the FY-3E satellite from July 2021 to December 2023.2. IGRF magnetic field at the location of FY-3E satellite.3. 1-second time resulution, three-component magnetic field in the GEO coordinate system of the four magnetometers carried by the FY-3E satellite from July 2021 to December 2023.
DOI: 10.5281/zenodo.15654319
title: DC magnetic field measurement of FY-3E satellite

description:  This dataset contains the data files supporting the paper "The Distributions and Temporal Variations of FACs During Substorms and Quiet Periods", these data are in ". csv" format. The contents are as follows:

1. Trajectory of the FY-3E satellite from July 2021 to December 2023.2. IGRF magnetic field at the location of FY-3E satellite.3. 1-second time resulution, three-component magnetic field in the GEO coordinate system of the four magnetometers carried by the FY-3E satellite from July 2021 to December 2023.
DOI: 10.15468/dl.q298fz
title: Occurrence Download

description: A dataset containing 23306 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rhaponticum coniferum (L.) Greuter",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 23306 records from 219 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045458-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2usfkx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hippodonta hungarica (Grunow) Lange-Bertalot, Metzeltin & Witkowski",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((20.7576 -24.3688,20.529 -24.0715,20.509 -24.0715,20.509 -28.5786,25.5395 -28.5786,25.5395 -24.0715,25.1306 -24.0715,24.9333 -24.4571,23.8962 -24.2467,22.8065 -24.295,22.3719 -24.7207,22.2323 -25.547,22.0387 -25.7188,21.7833 -25.7784,21.5388 -25.7113,21.3553 -25.5415,20.7576 -24.3688))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045460-250525065834625/datasets/export for details.
DOI: 10.15468/dl.ce92mz
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elegia grandispicata H.P.Linder",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((20.529 -24.0715,20.509 -24.0455,20.509 -24.0715,20.529 -24.0715))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045459-250525065834625/datasets/export for details.
DOI: 10.15468/dl.y39br9
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CHN.6.4_1",
 "TaxonKey is Callosciurus erythraeus (Pallas, 1779)"
 ]
}

 The dataset includes 25 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045461-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dbgb5z
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elegia grandispicata H.P.Linder",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((25.5395 -23.2724,25.1306 -24.0715,25.5395 -24.0715,25.5395 -23.2724))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045462-250525065834625/datasets/export for details.
DOI: 10.15468/dl.xvs9yj
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hippodonta hungarica (Grunow) Lange-Bertalot, Metzeltin & Witkowski",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((20.529 -24.0715,20.509 -24.0455,20.509 -24.0715,20.529 -24.0715))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045463-250525065834625/datasets/export for details.
DOI: 10.15468/dl.dvbedj
title: Occurrence Download

description: A dataset containing 26 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elegia grandispicata H.P.Linder",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((20.2855 -34.7533,20.509 -34.5676,20.509 -32.9007,17.7723 -32.9007,17.8156 -33.0786,18.3471 -33.7176,18.3748 -33.8287,18.2181 -34.0492,18.3197 -34.3408,18.4452 -34.429,18.5435 -34.4213,18.5872 -34.3667,18.5363 -34.167,18.7345 -34.1508,18.7216 -34.3637,18.7808 -34.4521,19.0289 -34.4206,19.0831 -34.4835,19.2655 -34.5017,19.2032 -34.6157,19.2394 -34.6926,19.375 -34.6963,19.6091 -34.8524,19.8251 -34.8396,19.9798 -34.9064,20.1081 -34.8731,20.1517 -34.7847,20.2855 -34.7533))"
 ]
}

 The dataset includes 26 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045465-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qwuv78
title: Occurrence Download

description: A dataset containing 13579 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Roemeria hispida Stace",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 13579 records from 371 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045464-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.scv2c5
title: Occurrence Download

description: A dataset containing 10609 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is PlantBank Records"
 ]
}

 The dataset includes 10609 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006787-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x2794m
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pinellia ternata",
 "Country is Canada",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006789-250914085247600/datasets/export for details.
DOI: 10.15468/dl.rzpk2s
title: Occurrence Download

description: A dataset containing 216 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pinellia ternata",
 "Country is United States of America",
 "HasCoordinate is true"
 ]
}

 The dataset includes 216 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006790-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60656/2aa610a20c24408e
title: Custom collection of lidar data from Cabauw between 1 and 31 May 2021
DOI: 10.15468/dl.yjvc9s
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pinellia ternata",
 "Country is Austria",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006788-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25934/pr00011271
title: Understanding the Impact of Molecular Subtypes on the Effectiveness of Kidney Cancer Treatments (Ipilimumab + Nivolumab and Sunitinib)

description: Kidney cancer is a serious disease that can spread to other parts of the body. One common type is called clear cell renal cell carcinoma (ccRCC). When ccRCC spreads, it is called metastatic ccRCC. More than 100,000 of people around the world are affected by this condition every year.

Doctors usually treat metastatic ccRCC using a mix of special medicines that help the immune system fight cancer. These medicines include anti-Programmed cell Death-1 (PD-1)/Programmed cell Death ligand 1(PD-L1) antibodies and anti-Cytotoxic T-lymphocyte Antigen-4(CTLA-4) antibodies, which help “wake up” immune cells so they can attack cancer cells. Other patients get medicines called tyrosine kinase inhibitors (TKIs), which block signals that help cancer grow. However, we don’t yet know which treatment works best for which patient. Right now, there is no test to help doctors choose the best treatment for each person.

In this study, we want to look at how genes are working in cancer cells by using a method called Ribonucleic acid (RNA) sequencing. This helps us understand what is going on inside the cancer. In an earlier study, we found that kidney cancer (renal cancer) can be divided into four different types based on gene activity. Now, we want to see if people with these different types of kidney cancer respond to treatment in different ways.

The requested study compared the efficacy of Ipilimumab (anti-CTLA4 antibody) + Nivolumab (anti-PD-1 antibody) and Sunitinib (TKI). Using this dataset, we could know patients who are suitable for either anti-PD-1 + anti-CTLA4 antibody or TKIs. This research could help doctors choose the right treatment for each patient in the future
DOI: 10.17617/3.5fnj7l
title: Dataset for the publication titled "Interplay between electronic and atomic short-range order in La$_{2-x}$Ca$_{x}$CuO$_{4-\delta}$ - temperature and doping dependence"

description: Experimental data used to generate the figures in the publication titled "Interplay between electronic and atomic short-range order in La$_{2-x}$Ca$_{x}$CuO$_{4-\delta}$ - temperature and doping dependence"