cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5285/d7773cb976d64b1c900a518773428df6
title: ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction – viewable snow (SCFV) from AATSR (2002 – 2012), version 1.0

description: This dataset contains Daily Snow Cover Fraction of viewable snow from AATSR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at 0.01° grid size (about 1 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. The SCFV time series provides daily products for the period 2002 – 2012. The SCFV product is based on Advanced Along-Track Scanning Radiometer (AATSR) data aboard the Envisat satellite. The retrieval method of the snow_cci SCFV product from AATSR data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include adaptation of the retrieval method for mapping in forested areas the SCFV. Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFV product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable. The SCFV product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology. The Norwegian Computing Center (Norsk Regnesentral, NR) is responsible for the SCFV product development and generation from AATSR data. The Remote Sensing Research Group of the University of Bern supported the development. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation. There are a few days without any AATSR acquisitions in the years 2002, 2003, 2004, 2006, 2008, 2010 and 2012.
DOI: 10.5285/70061acca284432ca31fd8a5cbd604d0
title: ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction – viewable snow (SCFV) from ATSR-2 (1995 – 2003), version 1.0

description: This dataset contains Daily Snow Cover Fraction of viewable snow from ATSR-2, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at 0.01° grid size (about 1 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. The SCFV time series provides daily products for the period 1995 – 2003. The SCFV product is based on Along-Track Scanning Radiometer 2 (ATSR-2) data aboard the ERS-2 satellite. The retrieval method of the snow_cci SCFV product from ATSR-2 data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include adaptation of the retrieval method for mapping in forested areas the SCFV. Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFV product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable. The SCFV product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology. The Norwegian Computing Center (Norsk Regnesentral, NR) is responsible for the SCFV product development and generation from ATSR-2 data. The Remote Sensing Research Group of the University of Bern supported the development. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation. There are a few days without any ATSR-2 acquisitions in the years 1995, 1996, 1999, 2000, 2001, 2002 and 2003.
DOI: 10.34740/kaggle/dsv/6269758
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.8224373
title: Bern3D model output data from idealized co2 increase-decrease simulations to investigate reversibility in the Earth system

description: The data described below is output from the Bern3D intermediate complexity model and idealized CO2 increase-decrease simulations to investigate reversibilty and hysteresis for different maximum co2 forcings. <br> The data are provided as .csv and .nc files<br> The first row in the .csv files contains the header, which describes the variable. The naming convention is as follows: c#k#_VARIABLE c# indicates the maximum co2 as times pre-industrial (c2 to c5)<br> k# indicates the equilibrium climate sensitivity of the respective simulation in degrees C (k2 to k5)<br> and VARIABLE indicates the value of the respective variable, which are:<br> co2: change in atmospheric co2 concentration in [ppm]<br> amoc: change in maximum of the Atlantic meridional overturning circulation in [Sv]<br> ohc: change in ocean heat content in [10^24 J]<br> seaice: sea-ice area remaining as fraction of the pre-industrial cover<br> Om_arag: fraction of water with Omega_arag &gt; 3 in the upper 175 m<br> o2_thermo: change in thermocline (200-600 m) oxygen concentration in [mmol m^-3]<br> for each variable a separate file exists where the variable and co2 are provided. <br> Spatial data to create the maps of hysteresis on a grid-cell basis are provided for the two scenarios as .nc files. The naming is as follows: c#k#_hyst_o2thermo.nc where c# corresponds again to maximum co2 as times pre-industrial and k# to the equilibrium climate sensitivity. The .nc files contain the coordinate (latitude, longitude) centers (lat_t, lon_t) and edges (lat_u, lon_u) as well as the hysteresis area (hystA_o2thermo) in [mmol m^-3]. <br> The files can be readily importet in python, for example, by:<br> import pandas as pd<br> import xarray as xr<br> <br> # for the .csv files<br> df = pd.read_csv('path+filename', sep=',', header=0, index_col=None)<br> <br> # for the .nc files<br> ds = xr.open_dataset('path+filename') <br> For additional information or in case of questions please contact:<br> Aurich Jeltsch-Thömmes<br> aurich.jeltsch-thoemmes@unibe.ch
DOI: 10.5281/zenodo.8224372
title: Bern3D model output data from idealized co2 increase-decrease simulations to investigate reversibility in the Earth system

description: The data described below is output from the Bern3D intermediate complexity model and idealized CO2 increase-decrease simulations to investigate reversibilty and hysteresis for different maximum co2 forcings. <br> The data are provided as .csv and .nc files<br> The first row in the .csv files contains the header, which describes the variable. The naming convention is as follows: c#k#_VARIABLE c# indicates the maximum co2 as times pre-industrial (c2 to c5)<br> k# indicates the equilibrium climate sensitivity of the respective simulation in degrees C (k2 to k5)<br> and VARIABLE indicates the value of the respective variable, which are:<br> co2: change in atmospheric co2 concentration in [ppm]<br> amoc: change in maximum of the Atlantic meridional overturning circulation in [Sv]<br> ohc: change in ocean heat content in [10^24 J]<br> seaice: sea-ice area remaining as fraction of the pre-industrial cover<br> Om_arag: fraction of water with Omega_arag &gt; 3 in the upper 175 m<br> o2_thermo: change in thermocline (200-600 m) oxygen concentration in [mmol m^-3]<br> for each variable a separate file exists where the variable and co2 are provided. <br> Spatial data to create the maps of hysteresis on a grid-cell basis are provided for the two scenarios as .nc files. The naming is as follows: c#k#_hyst_o2thermo.nc where c# corresponds again to maximum co2 as times pre-industrial and k# to the equilibrium climate sensitivity. The .nc files contain the coordinate (latitude, longitude) centers (lat_t, lon_t) and edges (lat_u, lon_u) as well as the hysteresis area (hystA_o2thermo) in [mmol m^-3]. <br> The files can be readily importet in python, for example, by:<br> import pandas as pd<br> import xarray as xr<br> <br> # for the .csv files<br> df = pd.read_csv('path+filename', sep=',', header=0, index_col=None)<br> <br> # for the .nc files<br> ds = xr.open_dataset('path+filename') <br> For additional information or in case of questions please contact:<br> Aurich Jeltsch-Thömmes<br> aurich.jeltsch-thoemmes@unibe.ch
DOI: 10.17617/4.d9
title: Ion-specific binding of cations to the carboxylate and of anions to the amide of alanylalanine

description: Krevert, C. S., Gunkel, L., Haease, C., & Hunger, J. (2022). Ion-specific binding of cations to the carboxylate and of anions to the amide of alanylalanine. Communications Chemistry, 5(1): 173. doi:10.1038/s42004-022-00789-y.
DOI: 10.17638/datacat.liverpool.ac.uk/2411
title: Qualitative study of irish teacher perceptions of their role supporting student mental health
DOI: 10.6084/m9.figshare.23905050
title: Supplementary code, raw data and figure plots from "Organization and dynamics of the cortical complexes controlling insulin secretion in β-cells"

description: Supplementary code, raw data and figure plots from the paper:<br><i>"Organization and dynamics of the cortical complexes controlling insulin secretion in β-cells"</i><br>Ivar Noordstra, Cyntha M. van den Berg, Fransje W. J. Boot, Eugene A. Katrukha, Ka Lou Yu, Roderick P. Tas, Sybren Portegies, Bastiaan J. Viergever, Esther de Graaff, Casper C. Hoogenraad, Eelco J. P. de Koning, Françoise Carlotti, Lukas C. Kapitein, Anna Akhmanova <br><br>
DOI: 10.6084/m9.figshare.23905050.v1
title: Supplementary code, raw data and figure plots from "Organization and dynamics of the cortical complexes controlling insulin secretion in β-cells"

description: Supplementary code, raw data and figure plots from the paper:<br><i>"Organization and dynamics of the cortical complexes controlling insulin secretion in β-cells"</i><br>Ivar Noordstra, Cyntha M. van den Berg, Fransje W. J. Boot, Eugene A. Katrukha, Ka Lou Yu, Roderick P. Tas, Sybren Portegies, Bastiaan J. Viergever, Esther de Graaff, Casper C. Hoogenraad, Eelco J. P. de Koning, Françoise Carlotti, Lukas C. Kapitein, Anna Akhmanova <br><br>
DOI: 10.5517/ccdc.csd.cc1nfmb1
title: CCDC 1532433: Experimental Crystal Structure Determination

description: Related Article: Ulrich Flörke, Martin Bernard, Gerald Henkel|2017|CSD Communication|||
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: 10.5517/ccdc.csd.cc1nfmd3
title: CCDC 1532435: Experimental Crystal Structure Determination

description: Related Article: Ulrich Flörke, Martin Bernard, Gerald Henkel|2017|CSD Communication|||
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: 10.5517/ccdc.csd.cc1p939d
title: CCDC 1557883: Experimental Crystal Structure Determination

description: Related Article: Ulrich Flörke, Alexander Oppermann, Gerald Henkel|2017|CSD Communication|||
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: 10.6084/m9.figshare.23905476.v1
title: experimental and numerical date.xlsx

description: The date is about the permeability of structured porous media with microfluidic model
DOI: 10.6084/m9.figshare.23905476.v2
title: experimental and numerical date.xlsx

description: Date for " permeability of structured porous media with microfluidic model "
DOI: 10.5517/ccdc.csd.cc1qblqc
title: CCDC 1589113: Experimental Crystal Structure Determination

description: Related Article: Brian A. Chalmers, Phillip S. Nejman, Alice V. Llewellyn, Adrian M. Felaar, Ben L. Griffiths, Eden I. Portman, Emma-Jane L. Gordon, Kenny J. H. Fan, J. Derek Woollins, M. Bühl, Olga L. Malkina, David B. Cordes, Alexandra M. Z. Slawin, Petr Kilian|2018|Inorg.Chem.|57|3387|doi:10.1021/acs.inorgchem.8b00162
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: 10.5517/ccdc.csd.cc2bslqg
title: CCDC 2168596: Experimental Crystal Structure Determination

description: Related Article: Gita Matulevičiūtė, Neringa Kleizienė, Greta Račkauskienė, Vytas Martynaitis, Aurimas Bieliauskas, Urtė Šachlevičiūtė, Rokas Jankauskas, Martynas R. Bartkus, Frank A. Sløk, Algirdas Šačkus|2023|RSC Advances|13|21378|doi:10.1039/D3RA03060A
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: 10.6084/m9.figshare.23905476
title: experimental and numerical date.xlsx

description: The date is about the permeability of structured porous media with microfluidic model
DOI: 10.5517/ccdc.csd.cc2ffxb4
title: CCDC 2247696: Experimental Crystal Structure Determination

description: Related Article: Naba Kr Mandal, Priyanka Arya, Neera Raghav, Shubhamoy Chowdhury, Jnan Prakash Naskar|2023|New J.Chem.|47|14551|doi:10.1039/D3NJ02148C
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: 10.17638/datacat.liverpool.ac.uk/2413
title: Spectroscopic data for "Unravelling the Roles of Integral Polypeptides in Excitation Energy Transfer of Photosynthetic RC-LH1 Supercomplexes".

description: zip file containing steady-state and time-resolved transient UV/vis absorption spectra for Unravelling the Roles of Integral Polypeptides in Excitation Energy Transfer of Photosynthetic RC-LH1 Supercomplexes. The transient absorption data are organised as separate files for each titular complex.
DOI: 10.6084/m9.figshare.23869971
title: Biomass

description: This is biomass data collected in experimental thin layer placement plots in Coggeshall Marsh, Prudence Island, RI.<br>These data and related items of information have not been formally disseminated by NOAA, and do not represent any agency determination, view, or policy.
DOI: 10.5519/qd.05wl0p1y
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (22 records)
DOI: 10.17617/4.da
title: Ion-pairing equilibria and kinetics of dimethyl phosphate: A model for counter-ion binding to the phosphate backbone of nucleic acids

description: Kutus, B., Wagner, K., Wagner, M., & Hunger, J. (2022). Ion-pairing equilibria and kinetics of dimethyl phosphate: A model for counter-ion binding to the phosphate backbone of nucleic acids. Journal of Molecular Liquids, 363: 119868. doi:10.1016/j.molliq.2022.119868.
DOI: 10.5517/ccdc.csd.cc208jys
title: CCDC 1855255: Experimental Crystal Structure Determination

description: Related Article: Yawei Zhao, Junyu Shen, Ze Yu, Maowei Hu, Chong Liu, Jiandong Fan, Hongxian Han, Anders Hagfeldt, Mei Wang, Licheng Sun|2019|J.Mater.Chem.A|7|12808|doi:10.1039/C9TA02735A
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: 10.6084/m9.figshare.23905617
title: <b>Supplementary Material – </b><b>Genome size in Argentinean species of </b><i><b>Passiflora</b></i><b> genus: cytological and phenotypical correlates</b>

description: <b>Table S1 – </b><i><b>Passiflora</b></i><b> species in the Institute of Floriculture (IF) collection.</b><b>Table S2 – </b><b>Estimation of 1Cx genome size, mean flower diameter, mean leaf area and mean internodal length with standard deviation for genotypes and species of </b><i><b>Passiflora</b></i><b>.</b><b>This Supplementary Material is part of the Original Paper published at Rodriguesia - DOI:</b> <b>http://dx.doi.org/10.1590/2175-7860202374046</b>
DOI: 10.6084/m9.figshare.23905617.v1
title: <b>Supplementary Material – </b><b>Genome size in Argentinean species of </b><i><b>Passiflora</b></i><b> genus: cytological and phenotypical correlates</b>

description: <b>Table S1 – </b><i><b>Passiflora</b></i><b> species in the Institute of Floriculture (IF) collection.</b><b>Table S2 – </b><b>Estimation of 1Cx genome size, mean flower diameter, mean leaf area and mean internodal length with standard deviation for genotypes and species of </b><i><b>Passiflora</b></i><b>.</b><b>This Supplementary Material is part of the Original Paper published at Rodriguesia - DOI:</b> <b>http://dx.doi.org/10.1590/2175-7860202374046</b>
DOI: 10.5281/zenodo.8220468
title: Mapping past land cover on Poitiers in 1993 at Very High Resolution using GEOBIA approach and open data

description: This dataset contains a land cover map of Poitiers in 1993 over an area of 225km². The land cover map was achieved using aerial images of the French National Geographic Institute (IGN) and Landsat-5 TM images combined with remote sensing methods. Geographic Object-Based Image Analysis (GEOBIA) and Random Forest classifications produced a reliable land cover map at a 1m of spatial resolution. Orthophotos produced as well as training and validating polygons to achieve the classifications were added into this dataset. As land cover changes is crucial to land management, this map will help to understand changes from 1993 to now for urban, agricultural issues but also their impact on ecological processes. Data will be easily used in GIS applications for any users. This work is part of the thesis of Elie Morin which was funded by la région Nouvelle-Aquitaine and Grand Poitiers Communauté urbaine, among others.
DOI: 10.5281/zenodo.8220467
title: Mapping past land cover on Poitiers in 1993 at Very High Resolution using GEOBIA approach and open data

description: This dataset contains a land cover map of Poitiers in 1993 over an area of 225km². The land cover map was achieved using aerial images of the French National Geographic Institute (IGN) and Landsat-5 TM images combined with remote sensing methods. Geographic Object-Based Image Analysis (GEOBIA) and Random Forest classifications produced a reliable land cover map at a 1m of spatial resolution. Orthophotos produced as well as training and validating polygons to achieve the classifications were added into this dataset. As land cover changes is crucial to land management, this map will help to understand changes from 1993 to now for urban, agricultural issues but also their impact on ecological processes. Data will be easily used in GIS applications for any users. This work is part of the thesis of Elie Morin which was funded by la région Nouvelle-Aquitaine and Grand Poitiers Communauté urbaine, among others.
DOI: 10.6084/m9.figshare.23791527.v1
title: Supplementary Material for: Large Ascites in a Cirrhotic Patient Reveal an Isolated and Late Metastasis of Ductal Breast Cancer: Case Study

description: Breast cancer is the most prevalent cancer in women worldwide, and its prevalence has increased since the introduction of screening programs. Most cases are discovered at an early stage; however, despite effective treatment, some cases progress to metastasis. The most common breast cancer metastatic locations are the bone, liver, and lungs. Ascites malignant due to peritoneal involvement is a rare manifestation of metastatic breast cancer. After eight years of well-controlled breast cancer, we report a 54-year-old woman who presents with malignant ascites and is known to have cirrhosis of the liver.
DOI: 10.6084/m9.figshare.23866962.v17
title: Untitled Item

description: Regridding Global CMIP6 GCMs dataset(0.5 degree)Historical dataset: 7 CMIP6 GCMs (1850-2014, ACCESS-CM2 to GFDL-ESM4)Future dataset: 7 CMIP6 GCMs and 4 SSP scenarios (2015-2100)Composition of dataset: Average temperature, Maximum temperature, Minimum temperature, Windspeed, Relative humidity, Solar radiationIf you are using our codes and dataset, please use the following references:Song, Y.H., Chung, E.S., Shahid, S., Kim, Y.J., Kim, D.K. 2023. Development of global monthly dataset of CMIP6 climate variables for estimating evapotranspiration. Scientific Data.<br>