cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7785278
title: DAta set. Fallen Journals

description: Producción de las universidades españolas en el periodo 2018-2022, en las revistas expulsadas de Web of Science en 2023. Se incluye le listado de autores que tienen 6 o más trabajos.
DOI: 10.6084/m9.figshare.22358143
title: An improved global land cover mapping in 2015 with 30 m resolution (GLC-2015) based on a multi-source product fusion approach

description: The GLC-2015 is organized by a total of 1507 geographical grids of 4-degree. Shapefiles of 4-degree grids can be found in "fishnet_world.zip". Each image of the GLC-2015 dataset is named as “GLC-2015_lon_lat” (lon and lat representthe longitude and latitude of the grid’s lower left corner, respectively) in GeoTIFF format. For each pixel, values from 10 to 100 represent the land cover class, while 0 and 250 invalid fill value. FUNDING National Key Research & Development Program of China (Grant No. 2019YFA0607203) National Natural Science Foundation of China (Grant No. 42001326, 42171409） Natural Science Foundation of Guangdong Province of China (Grant No. 2022A1515012207)
DOI: 10.5281/zenodo.7785208
title: Data from: A Socio-technical Perspective on Software Vulnerabilities: A Causal Analysis

description: This data package contains supplemental material data for the under review TSE submission: A Socio-technical Perspective on Software Vulnerabilities: A Causal Analysis. The restricted access requirement will be lifted upon approval of the manuscript. The comprehensive explanation of this dataset can be found at: https://sailuh.github.io/causal_commit_flow_docs The following briefly describes the contents of the folders. The analysis presented in the manuscript requires the following: Git Log Mailing List Software Vulnerabilities (NVD Feed) This data is provided to a mining software repository tool, Kaiaulu. The data specifications and configuration parameters are defined in the OpenSSL project configuration file (.yml), also included in this package. An R notebook in Kaiaulu, taking the dataset above + project configuration file, can then perform the first analysis step: https://github.com/sailuh/kaiaulu/blob/master/vignettes/issue_social_smell_showcase.Rmd The file 1_openssl_social_smells_timeline.csv is generated as an output of this R Notebook, and included in the causal_model folder of this package. The following files in this folder numbered 2 through 16, describe transformation steps using Excel, Python scripts, and Tetrad (also an open source tool). These are described conceptually in the manuscript, but in more detail in the comprehensive explanation of this dataset linked at the start.
DOI: 10.5281/zenodo.7785207
title: Data from: A Socio-technical Perspective on Software Vulnerabilities: A Causal Analysis

description: This data package contains supplemental material data for the under review TSE submission: A Socio-technical Perspective on Software Vulnerabilities: A Causal Analysis. The restricted access requirement will be lifted upon approval of the manuscript. The comprehensive explanation of this dataset can be found at: https://sailuh.github.io/causal_commit_flow_docs The following briefly describes the contents of the folders. The analysis presented in the manuscript requires the following: Git Log Mailing List Software Vulnerabilities (NVD Feed) This data is provided to a mining software repository tool, Kaiaulu. The data specifications and configuration parameters are defined in the OpenSSL project configuration file (.yml), also included in this package. An R notebook in Kaiaulu, taking the dataset above + project configuration file, can then perform the first analysis step: https://github.com/sailuh/kaiaulu/blob/master/vignettes/issue_social_smell_showcase.Rmd The file 1_openssl_social_smells_timeline.csv is generated as an output of this R Notebook, and included in the causal_model folder of this package. The following files in this folder numbered 2 through 16, describe transformation steps using Excel, Python scripts, and Tetrad (also an open source tool). These are described conceptually in the manuscript, but in more detail in the comprehensive explanation of this dataset linked at the start.
DOI: 10.5066/p9yz07jy
title: Model Archive Summary for Turbidity Derived Suspended-Sediment Concentrations at USGS Station 11336680; South Mokelumne River at New Hope Bridge near Walnut Grove, California (2011 - 2015)

description: Continuous 15-minute time-series suspended-sediment concentration (SSC) data computed from USGS instream turbidity data using a YSI 6-series multi-parameter water quality sonde for the South Mokelumne River at New Hope Bridge near Walnut Grove, California station (SMR; USGS #11336680). A model archive summary describes the development of a continuous 15-minute SSC time-series regression model.
DOI: 10.34740/kaggle/dsv/5269269
title: Egypt Gold Prices | Daily Updated
DOI: 10.34740/kaggle/dsv/5269312
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.7785340
title: Adaptation and evolution of teaching method for university programming subject to the online learning environment

description: This is a full dataset for the article "Adaptation and evolution of teaching method for university programming subject to the online learning environment". Individual parts of this dataset can be found at the following URLs: - The GitLab commit dataset can be found at https://doi.org/10.5281/zenodo.7767673;<br> - student survey dataset can be found at https://doi.org/10.5281/zenodo.7785161;<br> - student point gain dataset can be found at https://doi.org/10.5281/zenodo.7785190;<br> - and changelog dataset can be found at https://doi.org/10.5281/zenodo.7767694. Since the changelog dataset is not publicly available due to the identifiable nature of the data and the risk of compromising participants’ privacy and confidentiality, this dataset is also not open. If you want to get access you must request it.
DOI: 10.5281/zenodo.7785339
title: Adaptation and evolution of teaching method for university programming subject to the online learning environment

description: This is a full dataset for the article "Adaptation and evolution of teaching method for university programming subject to the online learning environment". Individual parts of this dataset can be found at the following URLs: - The GitLab commit dataset can be found at https://doi.org/10.5281/zenodo.7767673;<br> - student survey dataset can be found at https://doi.org/10.5281/zenodo.7785161;<br> - student point gain dataset can be found at https://doi.org/10.5281/zenodo.7785190;<br> - and changelog dataset can be found at https://doi.org/10.5281/zenodo.7767694. Since the changelog dataset is not publicly available due to the identifiable nature of the data and the risk of compromising participants’ privacy and confidentiality, this dataset is also not open. If you want to get access you must request it.
DOI: 10.34740/kaggle/dsv/5258371
title: Indoor Climbing Gym Hold And Route Segmentation
DOI: 10.5281/zenodo.7785333
title: THE GROWTH IMPACT ON COMPANY VALUE: EVIDENCE FROM THE E-COMMERCE SECTOR

description: Thid dataset contains the information about e-commerce companies globally. The indicators include: region of operations (developed / developing), business-model (1st party trade / 3d party trade), key financial indicators Detailed description of variables: 1. Year: the period covers 2017-2021, annual data 2. Index: number of the company 3. EV/GMV_t: trailing mulriple EV/GMV 4. EV/GMV_f: forward multiple EV/GMV 5. EV/Revenue: trailing multiple EV/Revenue 6. EV/Gross Profit: trailing multiple EV/Gross Proift 7. EV/EBITDA: trailing multiple EV/EBITDA 8. EV/EBIT: trailing multiple EV/EBIT 9. Dummy_Dev: dummy variable, which equals 1 for companies, operating on developed markets, 0 - for developing 10. Dummy_Ind: dummy variable, which equals 1 for companies which operating model combines a marketplace and retailing, 0 - for retailers 11. Dummy_GP: dummy variable, which equals 1 for companies with positive Gross Profit at a given year 11. Dummy_EBITDA: dummy variable, which equals 1 for companies with positive EBITDA at a given year 12. GMV_g_1yH: GMV growth rate for 1 historical year 13. GMV_g_2yH: GMV CAGR rate for 2 historical years 14. GMV_g_1yF: GMV growth for 1 forecasted year 15. Rev_g_1yH, Rev_g_2yH, Rev_g_1yF, GP_g_1yH, GP_g_2yH, GP_g_1yF, EBITDA_g_1yH, EBITDA_g_2yH, EBITDA_m_1yF: the same as p.12-14 for Revenue, Gross Profit, and EBITDA 16. GP_m_1yH: Gross Margin for 1 historical year
DOI: 10.5281/zenodo.7785332
title: THE GROWTH IMPACT ON COMPANY VALUE: EVIDENCE FROM THE E-COMMERCE SECTOR

description: Thid dataset contains the information about e-commerce companies globally. The indicators include: region of operations (developed / developing), business-model (1st party trade / 3d party trade), key financial indicators Detailed description of variables: 1. Year: the period covers 2017-2021, annual data 2. Index: number of the company 3. EV/GMV_t: trailing mulriple EV/GMV 4. EV/GMV_f: forward multiple EV/GMV 5. EV/Revenue: trailing multiple EV/Revenue 6. EV/Gross Profit: trailing multiple EV/Gross Proift 7. EV/EBITDA: trailing multiple EV/EBITDA 8. EV/EBIT: trailing multiple EV/EBIT 9. Dummy_Dev: dummy variable, which equals 1 for companies, operating on developed markets, 0 - for developing 10. Dummy_Ind: dummy variable, which equals 1 for companies which operating model combines a marketplace and retailing, 0 - for retailers 11. Dummy_GP: dummy variable, which equals 1 for companies with positive Gross Profit at a given year 11. Dummy_EBITDA: dummy variable, which equals 1 for companies with positive EBITDA at a given year 12. GMV_g_1yH: GMV growth rate for 1 historical year 13. GMV_g_2yH: GMV CAGR rate for 2 historical years 14. GMV_g_1yF: GMV growth for 1 forecasted year 15. Rev_g_1yH, Rev_g_2yH, Rev_g_1yF, GP_g_1yH, GP_g_2yH, GP_g_1yF, EBITDA_g_1yH, EBITDA_g_2yH, EBITDA_m_1yF: the same as p.12-14 for Revenue, Gross Profit, and EBITDA 16. GP_m_1yH: Gross Margin for 1 historical year
DOI: 10.25505/fiz.icsd.cc2fhg6m
title: CSD 2249180: Experimental Crystal Structure Determination

description: Related Article: T. Lindemann|2023|ICSD Communication|||
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2fhgfv
title: CSD 2249187: Experimental Crystal Structure Determination

description: Related Article: T. Lindemann|2023|ICSD Communication|||
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2fhggw
title: CSD 2249188: Experimental Crystal Structure Determination

description: Related Article: T. Lindemann, O. Oeckler|2023|ICSD Communication|||
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2fhghx
title: CSD 2249189: Experimental Crystal Structure Determination

description: Related Article: T. Lindemann, O. Oeckler|2023|ICSD Communication|||
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.7785399
title: tyrannus_caudifasciatus_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7785398
title: tyrannus_caudifasciatus_spades_01.fasta.gz

description: .
DOI: 10.6084/m9.figshare.21557253
title: BBOB Instance Analysis - Code and Data

description: This repository contains the ELA features, performance data and scripts for data collection and visualization from the paper 'BBOB Instance Analysis: Landscape Properties and Algorithm Performance across Problem Instances'
DOI: 10.4121/17c8452a-a937-46a5-9693-f8212655779f
title: Campus of the Future Project FAIR Datasets

description: The Campus of the Future project proposes to develop evidence-based decision-making strategies to match supply and demand for the future campus. The two datasets presented in this folder are the outputs of the Campus of the Future project and they provide decision-makers and researchers with the most relevant and up-to-date campus management data: (1) University building data from Archdaily; (2) Corona-related campus news from Flipboard. These FAIR datasets also encourage campus decision-makers to provide open data on campus buildings in the future years, which will support decision-makers of individual universities with management information in making the right decisions for their future campus. Please check the “README [.txt]” and “campus.of.the.future.project.FAIR.datasets_codebook [.pdf]” files for more information.
DOI: 10.6084/m9.figshare.22361026
title: Ocean acidification has a strong effect on communities living on plastic in mesocosms

description: We conducted a mesocosm experiment to examine how ocean acidification (OA) affects communities of prokaryotes and eukaryotes growing on single-use drinking bottles in subtropical eutrophic waters of the East China Sea. Based on 16S rDNA gene sequencing, simulated high CO2 significantly altered the prokaryotic community, with the relative abundance of the phylum Planctomycetota increasing by 49 % under high CO2. Under high CO2, prokaryotes in plastisphere significantly enhanced nitrogen dissimilation and ureolysis, raising the possibility that elevated CO2 may modify nutrient cycling in subtropical eutrophic waters. The relative abundance of pathogenic and animal parasite bacteria also increased under simulated high CO2. Our results show that simulated high CO2 significantly affected some secondary producers based on 18S rDNA gene sequencing. For example, Mayorella amoebae were highly resistant to OA whereas labyrinthulids were sensitive to high CO2. This shows that OA may alter plastisphere food chains in subtropical eutrophic waters.
DOI: 10.6084/m9.figshare.22361026.v1
title: Ocean acidification has a strong effect on communities living on plastic in mesocosms

description: We conducted a mesocosm experiment to examine how ocean acidification (OA) affects communities of prokaryotes and eukaryotes growing on single-use drinking bottles in subtropical eutrophic waters of the East China Sea. Based on 16S rDNA gene sequencing, simulated high CO2 significantly altered the prokaryotic community, with the relative abundance of the phylum Planctomycetota increasing by 49 % under high CO2. Under high CO2, prokaryotes in plastisphere significantly enhanced nitrogen dissimilation and ureolysis, raising the possibility that elevated CO2 may modify nutrient cycling in subtropical eutrophic waters. The relative abundance of pathogenic and animal parasite bacteria also increased under simulated high CO2. Our results show that simulated high CO2 significantly affected some secondary producers based on 18S rDNA gene sequencing. For example, Mayorella amoebae were highly resistant to OA whereas labyrinthulids were sensitive to high CO2. This shows that OA may alter plastisphere food chains in subtropical eutrophic waters.
DOI: 10.34740/kaggle/dsv/5269519
title: Indoor Climbing Gym Hold And Route Segmentation
DOI: 10.5281/zenodo.7783485
title: 101-nights -sleep and dream study night 016

description: The research project presented aims at putting the personal perception of Nathalie’s dreams through an objective, quantitative analysis using electroencephalography (EEG), in an attempt to establish a linkage between the two dimensions. During sleep periods, brain activity is similar to that of an awakened state, yet the thalamus, a phylogenetically ancient structure in the nervous system, isolates us from the environment. But this isolation is not total, and sometimes external stimuli are incorporated into the plot of our dreams. To establish a bridge between the record (EEG) and Nathalie’s dream narrative, we experiment with auditory stimuli as a possible mechanism of interference. The 101 nights is a longitudinal dataset. At the core of the study is the concordance of two divergent fields of knowledge to record and represent the dream experience. For 101 nights physiological and behavioral data are continuously paired with the dreamer’s inner life, geared towards a dialogue. A unique dataset for scientific analyses, methodological developments as well artistic projects, including cognitive science and multiple modalities of art. The unprecedented project allows an internal and external perspective on Nathalie’s dreams, containing extensive data for 101 consecutive nights and days. the project produced four immediate results: 1. 952 GB of brain data was produced by the registry of 256 sensors over the period of 101 nights in continuity, including her body movement (actimetry and infrared camera).<br> 2. the audio logs of the words triggered by the computer system, each night with their exact time.<br> 3. Nathalie’s daily dream diary entries.<br> 4. day-by-day activity.
description: dreamsessions.org would like to acknowledge: the acquisition of 101-nights was possible with the in-kind support of EGI, a division of Philips Healthcare, a medical device company, and a key component of the dense array EEG method based in Eugene, Oregon, U.S.A. the project 101 nights, was launched on September 1st, 2017, in Eugene, OR, USA
DOI: 10.5281/zenodo.7783484
title: 101-nights -sleep and dream study night 016

description: The research project presented aims at putting the personal perception of Nathalie’s dreams through an objective, quantitative analysis using electroencephalography (EEG), in an attempt to establish a linkage between the two dimensions. During sleep periods, brain activity is similar to that of an awakened state, yet the thalamus, a phylogenetically ancient structure in the nervous system, isolates us from the environment. But this isolation is not total, and sometimes external stimuli are incorporated into the plot of our dreams. To establish a bridge between the record (EEG) and Nathalie’s dream narrative, we experiment with auditory stimuli as a possible mechanism of interference. The 101 nights is a longitudinal dataset. At the core of the study is the concordance of two divergent fields of knowledge to record and represent the dream experience. For 101 nights physiological and behavioral data are continuously paired with the dreamer’s inner life, geared towards a dialogue. A unique dataset for scientific analyses, methodological developments as well artistic projects, including cognitive science and multiple modalities of art. The unprecedented project allows an internal and external perspective on Nathalie’s dreams, containing extensive data for 101 consecutive nights and days. the project produced four immediate results: 1. 952 GB of brain data was produced by the registry of 256 sensors over the period of 101 nights in continuity, including her body movement (actimetry and infrared camera).<br> 2. the audio logs of the words triggered by the computer system, each night with their exact time.<br> 3. Nathalie’s daily dream diary entries.<br> 4. day-by-day activity.
description: dreamsessions.org would like to acknowledge: the acquisition of 101-nights was possible with the in-kind support of EGI, a division of Philips Healthcare, a medical device company, and a key component of the dense array EEG method based in Eugene, Oregon, U.S.A. the project 101 nights, was launched on September 1st, 2017, in Eugene, OR, USA
DOI: 10.5281/zenodo.7783460
title: 101-nights -sleep and dream study night 014

description: The research project presented aims at putting the personal perception of Nathalie’s dreams through an objective, quantitative analysis using electroencephalography (EEG), in an attempt to establish a linkage between the two dimensions. During sleep periods, brain activity is similar to that of an awakened state, yet the thalamus, a phylogenetically ancient structure in the nervous system, isolates us from the environment. But this isolation is not total, and sometimes external stimuli are incorporated into the plot of our dreams. To establish a bridge between the record (EEG) and Nathalie’s dream narrative, we experiment with auditory stimuli as a possible mechanism of interference. The 101 nights is a longitudinal dataset. At the core of the study is the concordance of two divergent fields of knowledge to record and represent the dream experience. For 101 nights physiological and behavioral data are continuously paired with the dreamer’s inner life, geared towards a dialogue. A unique dataset for scientific analyses, methodological developments as well artistic projects, including cognitive science and multiple modalities of art. The unprecedented project allows an internal and external perspective on Nathalie’s dreams, containing extensive data for 101 consecutive nights and days. the project produced four immediate results: 1. 952 GB of brain data was produced by the registry of 256 sensors over the period of 101 nights in continuity, including her body movement (actimetry and infrared camera).<br> 2. the audio logs of the words triggered by the computer system, each night with their exact time.<br> 3. Nathalie’s daily dream diary entries.<br> 4. day-by-day activity.
description: dreamsessions.org would like to acknowledge: the acquisition of 101-nights was possible with the in-kind support of EGI, a division of Philips Healthcare, a medical device company, and a key component of the dense array EEG method based in Eugene, Oregon, U.S.A. the project 101 nights, was launched on September 1st, 2017, in Eugene, OR, USA