cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh295718.07fu
title: SH295718.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh295719.07fu
title: SH295719.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh295720.07fu
title: SH295720.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh295721.07fu
title: SH295721.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh295722.07fu
title: SH295722.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh295723.07fu
title: SH295723.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh295724.07fu
title: SH295724.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh295725.07fu
title: SH295725.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh295726.07fu
title: SH295726.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh295727.07fu
title: SH295727.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.5517/ccdc.csd.cc2c1glh
title: CCDC 2176156: Experimental Crystal Structure Determination

description: Related Article: Szymon Smółka, Mirosław Mączka, Dawid Drozdowski, Dagmara Stefanska, Anna Gagor, Adam Sieradzki, Jan K. Zaręba, Maciej Ptak|2022|Inorg.Chem.|61|15225|doi:10.1021/acs.inorgchem.2c02496
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.cc2c1ghd
title: CCDC 2176153: Experimental Crystal Structure Determination

description: Related Article: Szymon Smółka, Mirosław Mączka, Dawid Drozdowski, Dagmara Stefanska, Anna Gagor, Adam Sieradzki, Jan K. Zaręba, Maciej Ptak|2022|Inorg.Chem.|61|15225|doi:10.1021/acs.inorgchem.2c02496
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.cc2c1gkg
title: CCDC 2176155: Experimental Crystal Structure Determination

description: Related Article: Szymon Smółka, Mirosław Mączka, Dawid Drozdowski, Dagmara Stefanska, Anna Gagor, Adam Sieradzki, Jan K. Zaręba, Maciej Ptak|2022|Inorg.Chem.|61|15225|doi:10.1021/acs.inorgchem.2c02496
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.cc2cnkyk
title: CCDC 2194519: Experimental Crystal Structure Determination

description: Related Article: Zhao Chaowei|2023|CSD Communication|||
description: Related Article: Zhou Zhang, Chaowei Zhao, Yuefeng Zhang, Yingzi Han, Ziwei Zhang, Jie Fang, Dongdong Xia, Shengyong You, Qiaomei Chen, Weiwei Li|2023|Chem.Eng.Journal|454|140002|doi:10.1016/j.cej.2022.140002
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.cc2cnkzl
title: CCDC 2194520: Experimental Crystal Structure Determination

description: Related Article: Zhou Zhang, Chaowei Zhao, Yuefeng Zhang, Yingzi Han, Ziwei Zhang, Jie Fang, Dongdong Xia, Shengyong You, Qiaomei Chen, Weiwei Li|2023|Chem.Eng.Journal|454|140002|doi:10.1016/j.cej.2022.140002
description: Related Article: Zhao Chaowei|2023|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: 10.18462/iir.icr.2023.0243
title: A novel frost detection: applying image recognition with wide-angle lens.
DOI: 10.18462/iir.icr.2023.0280
title: Numerical analysis of a frost prevention system for refrigerated warehouses and planning of a test bench for validation.
DOI: 10.18462/iir.icr.2023.0494
title: Thermodynamic analysis of a multi-evaporator refrigeration cycle using a two-phase ejector as an expansion device.
DOI: 10.18462/iir.icr.2023.0661
title: Modelling and simulation of heat sink cascade CO2 systems in low-income countries with high ambient temperature.
DOI: 10.18462/iir.icr.2023.0694
title: Effect of temperature and relative humidity on the carvacrol release kinetics from active packaging for fresh horticultural products.
DOI: 10.18462/iir.icr.2023.0757
title: Thermo-economic design and optimization of a waste heat driven system for medium temperature refrigeration.
DOI: 10.34740/kaggle/dsv/6700709
title: Exploring Shark Encounters in California
DOI: 10.18462/iir.icr.2023.0814
title: Numerical simulation of solid-liquid transition of carbon dioxide in pillow plate heat exchangers.
DOI: 10.18462/iir.icr.2023.0853
title: Numerical investigation of air curtain velocity ratio in an open refrigerated display cabinet with dual air curtain.
DOI: 10.18462/iir.icr.2023.0924
title: Cost effective storage and distribution of temperature sensitive vaccine.
DOI: 10.18462/iir.icr.2023.0954
title: Saving energy using RQ-based dynamic controlled atmosphere storage of blueberry fruit.
DOI: 10.5525/gla.researchdata.1471
title: Experimental Analysis of a Nonlinear Piecewise Multi-Degrees of Freedom System

description: The dataset contains the experimental dynamic measurements of a nonlinear Multi-Degree of Freedom System featuring hardening and piecewise stiffness characteristics. The system was studied in different ways: a linear analysis was performed with low-amplitude random excitation while a second nonlinear analysis was performed via stepped sine excitation. The nonlinear analysis was repeated with and without the stoppers (which approximate a piecewise stiffness characteristic) to better understand the nature of the system. All the data are stored in MAT and CSV files which are accessible with MATLAB and Python codes (attached to the dataset).
DOI: 10.18462/iir.icr.2023.0244
title: The effect of humidity on food refrigeration.
DOI: 10.18462/iir.icr.2023.0091
title: Study on retaining natural refrigerant in seafood processing industries in India.
DOI: 10.18462/iir.icr.2023.0109
title: Modelling thermal diffusivity of foods during freezing.
DOI: 10.18462/iir.icr.2023.0159
title: Towards the implementation of a sustainable cold chain for the livestock value chain in Bangladesh.
DOI: 10.18462/iir.icr.2023.0248
title: Effect of thermal property model selection on the accuracy of a one-dimensional numerical solution for the forced air chilling and freezing of food products.
DOI: 10.5281/zenodo.10014538
title: Estadísticas del catastro rústico de España, por municipio

description: Estadísticas del catastro rústico de España, por municipios. Como la descarga de la fuente original debe hacerse por provincias, aquí se han unido todos los registros en un único fichero. Las variables incluidas son Superficie rústica; número de Parcelas; número de Subparcelas; código municipal según el INE; media de la superficie de las parcelas (obtenido dividiendo superficie rústica por número de parcelas).
La fuente original de los datos es la sede del catastro, del   Ministerio de Hacienda y Función Pública
La Dirección General del Catastro tiene atribuida la competencia de la generación de las Estadísticas Catastrales, incluidas estas dentro del Plan Estadístico Nacional. Las Estadísticas Catastrales contienen información acerca de las principales variables del Catastro Inmobiliario, existiendo un apartado específico para cata tipo de bienes: urbano, rústico y de características especiales, así como para los datos correspondientes a la titularidad catastral. Catastro Inmobiliario Rústico incluye los siguientes datos: año renovación, parcelas rústicas, subparcelas, valor catastral y superficie rústica (incluye desglose en porcentaje según tipo de cultivo de las tres últimas variables). El R.D. Legislativo 1/2004, de 5 de marzo, por el que se aprueba el Texto refundido de la Ley del Catastro Inmobiliario (TRLCI), en su artículo 4, establece que la formación y el mantenimiento del Catastro Inmobiliario, así como la difusión de la información catastral es competencia exclusiva del Estado a través de la Dirección General del Catastro. El ámbito del registro administrativo del Catastro inmobiliario corresponde a todo el territorio nacional, excepto País Vasco y Navarra. Datos de 1990 a 2005 en formato DBF
DOI: 10.5281/zenodo.10014539
title: Estadísticas del catastro rústico de España, por municipio

description: Estadísticas del catastro rústico de España, por municipios. Como la descarga de la fuente original debe hacerse por provincias, aquí se han unido todos los registros en un único fichero. Las variables incluidas son Superficie rústica; número de Parcelas; número de Subparcelas; código municipal según el INE; media de la superficie de las parcelas (obtenido dividiendo superficie rústica por número de parcelas).
La fuente original de los datos es la sede del catastro, del   Ministerio de Hacienda y Función Pública
La Dirección General del Catastro tiene atribuida la competencia de la generación de las Estadísticas Catastrales, incluidas estas dentro del Plan Estadístico Nacional. Las Estadísticas Catastrales contienen información acerca de las principales variables del Catastro Inmobiliario, existiendo un apartado específico para cata tipo de bienes: urbano, rústico y de características especiales, así como para los datos correspondientes a la titularidad catastral. Catastro Inmobiliario Rústico incluye los siguientes datos: año renovación, parcelas rústicas, subparcelas, valor catastral y superficie rústica (incluye desglose en porcentaje según tipo de cultivo de las tres últimas variables). El R.D. Legislativo 1/2004, de 5 de marzo, por el que se aprueba el Texto refundido de la Ley del Catastro Inmobiliario (TRLCI), en su artículo 4, establece que la formación y el mantenimiento del Catastro Inmobiliario, así como la difusión de la información catastral es competencia exclusiva del Estado a través de la Dirección General del Catastro. El ámbito del registro administrativo del Catastro inmobiliario corresponde a todo el territorio nacional, excepto País Vasco y Navarra. Datos de 1990 a 2005 en formato DBF
DOI: 10.5281/zenodo.10014603
title: echeveria_elegans_decontaminated_FSCR.fasta.gz

description: echeveria_elegans_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.10014604
title: echeveria_elegans_decontaminated_FSCR.fasta.gz

description: echeveria_elegans_decontaminated_FSCR.fasta.gz
DOI: 10.18462/iir.icr.2023.0350
title: Understanding the impact of freezing and storage on the microstructure of a porous food using synchrotron X-rays microtomography.
DOI: 10.5281/zenodo.10014148
title: A didactical dataset to learn supervised classification with candy

description: A didactical dataset to learn supervised classification
It was obtained from university level students measuring candy that was mixed and distributed in bowls to them. The goal of this dataset creation was to expose the students to the data taking process. Further, the dataset is meant for classification.
Dataset Structure
The dataset consists of 6 csv files:

peanuts.csv represents the entire dataset (a concatenation of all group?.csv files) omitting the sample column
peanuts_all.csv represents the entire dataset (a concatenation of all group?.csv files)
files matching group[1-5].csv represent the measurements of each group
Data Representation
Each file contains 5 columns.  

color, int values, 0: white, 1: black, 2: brown, 3: other
shape, int values, 0: irregular, 1 round, 2: lens-like
height, float values, in millimeter
width, float values, in millimeter
label, category, peanut/nopeanut
For more information on the didactical background, see the original publication that presented the concept for this activity.
DOI: 10.5281/zenodo.10014149
title: A didactical dataset to learn supervised classification with candy

description: A didactical dataset to learn supervised classification
It was obtained from university level students measuring candy that was mixed and distributed in bowls to them. The goal of this dataset creation was to expose the students to the data taking process. Further, the dataset is meant for classification.
Dataset Structure
The dataset consists of 6 csv files:

peanuts.csv represents the entire dataset (a concatenation of all group?.csv files)
files matching group[1-5].csv represent the measurements of each group
Data Representation
Each file contains 5 columns.  

color, int values, 0: white, 1: black, 2: brown, 3: other
shape, int values, 0: irregular, 1 round, 2: lens-like
height, float values, in millimeter
width, float values, in millimeter
label, category, peanut/nopeanut
For more information on the didactical background, see the original publication that presented the concept for this activity.