cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10517621
title: Rat Auditory Delayed Discrimination Task Dataset

description: This dataset contains behavioral data from an experiment with rats. This is a summary of the experiment: We developed a computerized protocol to train rats, in high-throughput facilities, to perform an auditory delayed comparison task. All training takes place in three-port operant conditioning chambers, in which ports are arranged side-by-side along one wall, with two speakers placed above the left and right nose ports. A visible light-emitting diode in the centre port signals the availability of each trial. Rat subjects initiate a trial by inserting their nose into the center port, which causes the center light to turn off. Rats must keep their nose in the centre port (fixation period) until an auditory go cue (a 6-kHz pure tone for 200 ms) signals the end of fixation. Only after the go cue can subjects withdraw and orient to one of the side ports to receive a reward of water. During the fixation period two auditory stimuli, sa and sb, separated by a variable delay, are played for 400 ms, with short delay periods of 250 ms inserted before sa and after sb. Rats should judge which out of the two stimuli, sa and sb, had the greater SPL standard deviation. If sa > sb, the correct action is to poke the nose into the right-hand nose port to collect the reward, and if sa < sb, rats should orient to the left-hand nose port. Trial durations are independently varied on a trial-by-trial basis, by varying the delay interval between the two stimuli, which can be as short as 2 s or as long as 12 s. The delay between the two stimuli is varied on a trial-by-trial basis, which makes the task more challenging and requires the rats to maintain their attention and memory over longer periods. Rats progressed through a series of shaping stages before the final version of the delayed comparison task, in which they learned to: associate light in the center poke with the availability of trials; associate special sounds from the side pokes with reward; maintain their nose in the center poke until they hear an auditory go signal; and compare the two sa and sb stimuli.stages descriptions:

 

There are six stages involved in the training process, each of which builds on the previous stage and introduces new aspects of the task. In stage 1, rats learn to associate the lights on either side with the availability of trials and poke into the lightened side poke to get a reward.

In stage 2, rats learn to initiate a trial by poking their nose into the center port and waiting for a go cue sound to signal the end of the fixation period. They can then withdraw and orient to one of the side ports to receive a reward.

In stage 3, the fixation period is gradually increased in duration over successive trials. This is designed to increase the rats ability to maintain their attention and focus during the task.

in the dataset, only the three first stages’ data is included. 

. then a go cue sound plays. that means that rat can
DOI: 10.4121/6b8dd902-607d-4e82-b919-3a14abdee440
title: Data underlying the PhD thesis: Development of inorganic nanoparticles for radionuclide therapy in cancer treatment

description: In this database, research data including the characterization of the products, data from in vitro cell experiments and other radiochemistry experiments is uploaded to support the PhD thesis: Development of inorganic nanoparticles for radionuclide therapy in cancer treatment. The data is classified in chapters (i.e. chapter 2~5) and the name of technology used to obtain the data (e.g. TEM, DLS, etc.).
DOI: 10.4121/6b8dd902-607d-4e82-b919-3a14abdee440.v1
title: Data underlying the PhD thesis: Development of inorganic nanoparticles for radionuclide therapy in cancer treatment

description: In this database, research data including the characterization of the products, data from in vitro cell experiments and other radiochemistry experiments is uploaded to support the PhD thesis: Development of inorganic nanoparticles for radionuclide therapy in cancer treatment. The data is classified in chapters (i.e. chapter 2~5) and the name of technology used to obtain the data (e.g. TEM, DLS, etc.).
DOI: 10.48425/2ffftdgh
title: V_rodents_snaptrapping_individuals_regional

description: Snap trapping data of rodents on the Varanger and Nordkynn peninsula. This dataset has data from the COAT regional study design. Each entry corresponds to a trapped individual. Two related datasets have information on i) site-based abundances and ii) trap status, i.e. missing and closed traps. 

The dataset includes four different types of files and all files are saved as ;-separated txt-files:

* One data file per year (_YEAR.txt)
* One coordinate file with coordinates of all sites (_coordinates.txt)
* One auxiliary file with information about which sites are included in the study design (_aux.txt)
* One readme file with additional information (_readme.pdf)

The dataset has a two-year embargo, older versions of the dataset are publicly available for download.
DOI: 10.5281/zenodo.10517677
title: SANBIF database

description: Database of SANBIF project
DOI: 10.5281/zenodo.10517678
title: SANBIF database

description: Database of SANBIF project
DOI: 10.6084/m9.figshare.25002434
title: Supplementary Material for: Alteration of thyroid hormones in mouse models of Alzheimer's disease and aging

description: Introduction: Aging is characterized by the deterioration of a wide range of functions in tissues and organs, and Alzheimer's disease (AD) is a neurodegenerative disease characterized by cognitive impairment. Hypothyroidism occurs when there is insufficient production of thyroid hormones (THs) by the thyroid. The relationship between hypothyroidism and aging as well as AD is controversial at present.
Methods: We established an animal model of AD (FAD4T) with mutations in the APP and PSEN1 genes, and we performed a thyroid function test and RNA-sequencing (RNA-Seq) of the thyroid from FAD4T and naturally aging mice. We also studied gene perturbation correlation in FAD4T mouse thyroid, bone marrow and brain by futher single-cell RNA sequencing (scRNA-seq) data of bone marrow and brain.
Results: In this study, we found alterations in THs in both AD and aging mice. RNA-seq data showed significant upregulation of T-cell infiltration- and cell proliferation-related genes in the FAD4T mouse thyroid. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that upregulated genes were enriched in functional gene modules of activation of immune cells. Downregulated energy metabolism-related genes were prominent in aging thyroids, which reflected the reduction in THs. GSEA showed a similar enrichment tendency in both mouse thyroids, suggesting their analogous inflammation state. In addition, the regulation of leukocyte activation and migration was a common signature between the thyroid, brain and bone marrow of FAD4T mice. 
Conclusions: Our findings identified immune cell infiltration of the thyroid as the potential underlying mechanism of the alteration of THs in AD and aging.
DOI: 10.5517/ccdc.csd.cc2hfxgb
title: CCDC 2307282: Experimental Crystal Structure Determination

description: Related Article: Saigal, Anam Arif, Md. Musawwer Khan|2024|J.Mol.Struct.|1300|137193|doi:10.1016/j.molstruc.2023.137193
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.cc2h7m1f
title: CCDC 2301224: Experimental Crystal Structure Determination

description: Related Article: Marisol Cervantes-Vásquez, Naytzé Ortiz-Pastrana, Karen Yazmín Gamero-Vega, Alejandra Flores-Ramírez, M. Angeles Paz-Sandoval|2024|J.Organomet.Chem.|1004|122968|doi:10.1016/j.jorganchem.2023.122968
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.cc2h7m2g
title: CCDC 2301225: Experimental Crystal Structure Determination

description: Related Article: Marisol Cervantes-Vásquez, Naytzé Ortiz-Pastrana, Karen Yazmín Gamero-Vega, Alejandra Flores-Ramírez, M. Angeles Paz-Sandoval|2024|J.Organomet.Chem.|1004|122968|doi:10.1016/j.jorganchem.2023.122968
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.cc2h7lt5
title: CCDC 2301217: Experimental Crystal Structure Determination

description: Related Article: Marisol Cervantes-Vásquez, Naytzé Ortiz-Pastrana, Karen Yazmín Gamero-Vega, Alejandra Flores-Ramírez, M. Angeles Paz-Sandoval|2024|J.Organomet.Chem.|1004|122968|doi:10.1016/j.jorganchem.2023.122968
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.cc2hk5mw
title: CCDC 2310418: Experimental Crystal Structure Determination

description: Related Article: Shingo Harada, Shumpei Hirose, Mizuki Takamura, Maika Furutani, Yuna Hayashi, Tetsuhiro Nemoto|2023|J.Am.Chem.Soc.|146|733|doi:10.1021/jacs.3c10596
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.cc2f5gqt
title: CCDC 2239586: Experimental Crystal Structure Determination

description: Related Article: Haichuan Shi, Yuqing Wang, Xinyu Wang, Xiaotian Zhang, Ming Huang, Haijun Yang, Zhenghang Luo|2024|New J.Chem.|48|1212|doi:10.1039/D3NJ04391F
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.cc2f5grv
title: CCDC 2239587: Experimental Crystal Structure Determination

description: Related Article: Haichuan Shi, Yuqing Wang, Xinyu Wang, Xiaotian Zhang, Ming Huang, Haijun Yang, Zhenghang Luo|2024|New J.Chem.|48|1212|doi:10.1039/D3NJ04391F
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.cc2g81h9
title: CCDC 2271850: Experimental Crystal Structure Determination

description: Related Article: Ersad Hossain, Abhijit Hazra, Sourav Datta, Samim Khan, Samit Pramanik, Priyabrata Banerjee, Mohammad Hedayetullah Mir, Subrata Mukhopadhyay|2024|RSC Advances|14|397|doi:10.1039/D3RA06926E
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.cc2h6q5m
title: CCDC 2300360: Experimental Crystal Structure Determination

description: Related Article: Hong-Ru Tan, Xiang Zhou, Tengfei Gong, Hanqi You, Qi Zheng, Sheng-Yin Zhao, Weimin Xuan|2024|RSC Advances|14|364|doi:10.1039/D3RA07120K
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.25000025
title: OMEinfo v1 Data Packet

description: OMEinfo V1 datasetAboutThis dataset represents the version 1 data packet utilised by OMEinfo for the annotation of geographical metadata in a consistent manner. The cloud-optimised GeoTIFF files in the dataset are collated from a variety of sources (see references below) For details on the process for the creation of the OMEinfo data source, see the explanation hereData References<b>Data Source:</b> Fossil Fuel CO<sub>2</sub> emissions data<br><b>Citation: </b>Tomohiro Oda, Shamil Maksyutov (2015), ODIAC Fossil Fuel CO<sub>2</sub> Emissions Dataset (Version name: ODIAC2020b), Center for Global Environmental Research, National Institute for Environmental Studies <br><b>DOI: </b>https://doi.org/10.17595/20170411.001<b>Data Source: </b>Köppen-Geiger Climate Classification<br><b>Citation: </b>Beck, H., Zimmermann, N., McVicar, T. et al. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci Data 5, 180214 (2018)<br><b>DOI: </b>https://doi.org/10.1038/sdata.2018.214<b>Data Source: </b>Population Density<br><b>Citation: </b>Schiavina, Marcello; Freire, Sergio; MacManus, Kytt (2019): GHS population grid multitemporal (1975, 1990, 2000, 2015) R2019A. European Commission, Joint Research Centre (JRC)<br><b>DOI: </b>https://doi.org/10.2905/42E8BE89-54FF-464E-BE7B-BF9E64DA5218)<b>Data Source: </b>Rurality<br><b>Citation: </b>Pesaresi, Martino; Florczyk, Aneta; Schiavina, Marcello; Melchiorri, Michele; Maffenini, Luca (2019): GHS settlement grid, updated and refined REGIO model 2014 in application to GHS-BUILT R2018A and GHS-POP R2019A, multitemporal (1975-1990-2000-2015), R2019A. European Commission, Joint Research Centre (JRC)<br><b>DOI: </b>https://doi.org/10.2905/42E8BE89-54FF-464E-BE7B-BF9E64DA5218)<b>Data Source: </b>Tropospheric NO<sub>2</sub> Emissions data<br><b>Citation: </b>Romahn, Pedergnana, Loyola, Apituley, Sneep and Veefkind (2022): Sentinel-5 Precursor/TROPOMI Level 2 Product User Manual: Cloud Properties <b>Reference: </b>https://sentinel.esa.int/documents/247904/2474726/Sentinel-5P-Level-2-Product-User-Manual-Cloud
DOI: 10.6084/m9.figshare.25000343
title: OMEinfo v2 Data Packet

description: OMEinfo V2 datasetAboutThis dataset represents the version 2 data packet utilised by OMEinfo for the annotation of geographical metadata in a consistent manner. The cloud-optimised GeoTIFF files in the dataset are collated from a variety of sources (see references below)For details on the process for the creation of the OMEinfo data source, see the explanation hereData References<b>Data Source:</b> Fossil Fuel CO<sub>2</sub> emissions data<br><b>Citation: </b>Tomohiro Oda, Shamil Maksyutov (2015), ODIAC Fossil Fuel CO<sub>2</sub> Emissions Dataset (Version name: ODIAC2020b), Center for Global Environmental Research, National Institute for Environmental Studies<br><b>DOI: </b>https://doi.org/10.17595/20170411.001<b>Data Source: </b>Köppen-Geiger Climate Classification<br><b>Citation: </b>Beck, H., Zimmermann, N., McVicar, T. et al. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci Data 5, 180214 (2018)<br><b>DOI: </b>https://doi.org/10.1038/sdata.2018.214<b>Data Source: </b>Population Density<br><b>Citation: </b>Schiavina, Marcello; Freire, Sergio; MacManus, Kytt (2019): GHS population grid multitemporal (1975, 1990, 2000, 2015) R2019A. European Commission, Joint Research Centre (JRC)<br><b>DOI: </b>https://doi.org/10.2905/42E8BE89-54FF-464E-BE7B-BF9E64DA5218)<b>Data Source: </b>Rurality<br><b>Citation: </b>Pesaresi, Martino; Florczyk, Aneta; Schiavina, Marcello; Melchiorri, Michele; Maffenini, Luca (2019): GHS settlement grid, updated and refined REGIO model 2014 in application to GHS-BUILT R2018A and GHS-POP R2019A, multitemporal (1975-1990-2000-2015), R2019A. European Commission, Joint Research Centre (JRC)<br><b>DOI: </b>https://doi.org/10.2905/42E8BE89-54FF-464E-BE7B-BF9E64DA5218)<b>Data Source: </b>Tropospheric NO<sub>2</sub> Emissions data<br><b>Citation: </b>Romahn, Pedergnana, Loyola, Apituley, Sneep and Veefkind (2022): Sentinel-5 Precursor/TROPOMI Level 2 Product User Manual: Cloud Properties <br><b>Reference: </b>https://sentinel.esa.int/documents/247904/2474726/Sentinel-5P-Level-2-Product-User-Manual-Cloud<b>Data Source: </b>Relative Deprivation Index<br><b>Citation: </b>NASA Socioeconomic Data and Applications Center (SEDAC) (2022) <br><b>Reference: </b>https://sedac.ciesin.columbia.edu/data/set/povmap-grdi-v1
DOI: 10.6084/m9.figshare.25000343.v1
title: OMEinfo v2 Data Packet

description: OMEinfo V2 datasetAboutThis dataset represents the version 2 data packet utilised by OMEinfo for the annotation of geographical metadata in a consistent manner. The cloud-optimised GeoTIFF files in the dataset are collated from a variety of sources (see references below)For details on the process for the creation of the OMEinfo data source, see the explanation hereData References<b>Data Source:</b> Fossil Fuel CO<sub>2</sub> emissions data<br><b>Citation: </b>Tomohiro Oda, Shamil Maksyutov (2015), ODIAC Fossil Fuel CO<sub>2</sub> Emissions Dataset (Version name: ODIAC2020b), Center for Global Environmental Research, National Institute for Environmental Studies<br><b>DOI: </b>https://doi.org/10.17595/20170411.001<b>Data Source: </b>Köppen-Geiger Climate Classification<br><b>Citation: </b>Beck, H., Zimmermann, N., McVicar, T. et al. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci Data 5, 180214 (2018)<br><b>DOI: </b>https://doi.org/10.1038/sdata.2018.214<b>Data Source: </b>Population Density<br><b>Citation: </b>Schiavina, Marcello; Freire, Sergio; MacManus, Kytt (2019): GHS population grid multitemporal (1975, 1990, 2000, 2015) R2019A. European Commission, Joint Research Centre (JRC)<br><b>DOI: </b>https://doi.org/10.2905/42E8BE89-54FF-464E-BE7B-BF9E64DA5218)<b>Data Source: </b>Rurality<br><b>Citation: </b>Pesaresi, Martino; Florczyk, Aneta; Schiavina, Marcello; Melchiorri, Michele; Maffenini, Luca (2019): GHS settlement grid, updated and refined REGIO model 2014 in application to GHS-BUILT R2018A and GHS-POP R2019A, multitemporal (1975-1990-2000-2015), R2019A. European Commission, Joint Research Centre (JRC)<br><b>DOI: </b>https://doi.org/10.2905/42E8BE89-54FF-464E-BE7B-BF9E64DA5218)<b>Data Source: </b>Tropospheric NO<sub>2</sub> Emissions data<br><b>Citation: </b>Romahn, Pedergnana, Loyola, Apituley, Sneep and Veefkind (2022): Sentinel-5 Precursor/TROPOMI Level 2 Product User Manual: Cloud Properties <br><b>Reference: </b>https://sentinel.esa.int/documents/247904/2474726/Sentinel-5P-Level-2-Product-User-Manual-Cloud<b>Data Source: </b>Relative Deprivation Index<br><b>Citation: </b>NASA Socioeconomic Data and Applications Center (SEDAC) (2022) <br><b>Reference: </b>https://sedac.ciesin.columbia.edu/data/set/povmap-grdi-v1
DOI: 10.48425/sg4fp8x5
title: V_rodents_snaptrapping_abundance_regional

description: Snap trapping data of rodents on the Varanger and Nordkynn peninsula. This dataset has data from the COAT regional study design and has data on site-based abundances. Two related datasets have information on i) trapped individuals and ii) trap status, i.e. missing and closed traps. 

The dataset includes four different types of files and all files are saved as ;-separated txt-files:

* One data file per year (_YEAR.txt)
* One coordinate file with coordinates of all sites (_coordinates.txt)
* One auxiliary file with information about which sites are included in the study design (_aux.txt)
* One readme file with additional information (_readme.pdf)

The dataset has a two-year embargo, older versions of the dataset are publicly available for download.
DOI: 10.5281/zenodo.8182597
title: Dataset: Assessing Background Contamination of Sample Tubes used in Human Biomonitoring by Non-targeted Liquid Chromatography–High Resolution Mass Spectrometry

description: Data set of the Publication: 



Krauss, Martin, Carolin Huber, Tobias Schulze, Martina Bartel-Steinbach, Till Weber, Marike Kolossa-Gehring, und Dominik Lermen (2024): Assessing background contamination of sample tubes used in human biomonitoring by non-targeted liquid chromatography–high resolution mass spectrometry. Environment International 183: 108426. https://doi.org/10.1016/j.envint.2024.108426.


- raw LC-HRMS data in mzML format for positive and negative mode.

- merged MS/MS spectra of whole data set after MZMine 2.53 processing in mgf format.
DOI: 10.5281/zenodo.8182598
title: Dataset: Assessing Background Contamination of Sample Tubes used in Human Biomonitoring by Non-targeted Liquid Chromatography–High Resolution Mass Spectrometry

description: Data set of the Publication: 



Krauss, Martin, Carolin Huber, Tobias Schulze, Martina Bartel-Steinbach, Till Weber, Marike Kolossa-Gehring, und Dominik Lermen (2024): Assessing background contamination of sample tubes used in human biomonitoring by non-targeted liquid chromatography–high resolution mass spectrometry. Environment International 183: 108426. https://doi.org/10.1016/j.envint.2024.108426.


- raw LC-HRMS data in mzML format for positive and negative mode.

- merged MS/MS spectra of whole data set after MZMine 2.53 processing in mgf format.
DOI: 10.6084/m9.figshare.25002131.v3
title: Water Quality Monitoring Dataset (Ireland)

description: This study utilizes water quality data from <b>Cork Harbour, Moy Killala, and 15 other coastal locations in Ireland</b>. The Environmental Protection Agency (EPA) of Ireland manages and evaluates this dataset as part of their monitoring program. The extracted data comprises 445,125 entries from Cork Harbour, 722,532 entries from Moy Killala, and 15 other randomly selected locations, all stored in .csv format. Upon merging these disparate .csv files, the dataset expanded to 1,276,654 entries. Following a pivot operation on <b>11 parameters</b> (<b>Alkalinity-total (as CaCO3), Ammonia-Total (as N), BOD - 5 days (Total), Chloride, Conductivity @25°C, Dissolved Oxygen, ortho-phosphate (as P) - unspecified, pH, Temperature, Total Hardness (as CaCO3), True Colour</b>), the dataset was reduced to <b>29,159 rows</b>. The selection of parameters was based on their availability across the entire dataset and aimed at encompassing fundamental measurements of water quality.This repository has two csv files:Water Quality Monitoring Dataset_ Ireland.csv - with Raw Water Quality DataWQI Results on Dataset - Which has the calculated Water Quallity Indices (CCME, WAWQI, Horton, SRDD, Brown) results of water quality