cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21571377
title: Raw data of "Identification of EMT-related alternative splicing event of TMC7 to promote invasion and migration of pancreatic cancer"

description: Raw data of the manuscript "Identification of EMT-related alternative splicing event of TMC7 to promote invasion and migration of pancreatic cancer"
DOI: 10.6084/m9.figshare.21571377.v1
title: Raw data of "Identification of EMT-related alternative splicing event of TMC7 to promote invasion and migration of pancreatic cancer"

description: Raw data of the manuscript "Identification of EMT-related alternative splicing event of TMC7 to promote invasion and migration of pancreatic cancer"
DOI: 10.6084/m9.figshare.21571443.v3
title: OV_Wang

description: OV scRNA-seq datasets
DOI: 10.6084/m9.figshare.21571443
title: OV_Wang

description: Ovrian cancer
DOI: 10.6084/m9.figshare.21571443.v4
title: OV_Wang

description: Ovrian cancer
DOI: 10.6084/m9.figshare.21571443.v5
title: OV_Wang

description: Ovrian cancer
DOI: 10.5281/zenodo.3566178
title: ECOBREED WP2 T2.3 Wheat (Triticum aestivum) - Allelopathic activity nursery

description: Description of the common wheat (Triticum aestivum) allelopathic activity nursery. Tested within T2.3 in Spain (by UVIGO).
DOI: 10.5281/zenodo.3566179
title: ECOBREED WP2 T2.3 Wheat (Triticum aestivum) - Allelopathic activity nursery

description: Description of the common wheat (Triticum aestivum) allelopathic activity nursery. Tested within T2.3 in Spain (by UVIGO).
DOI: 10.5281/zenodo.3567789
title: ECOBREED WP2 T2.3 Wheat (Triticum aestivum) - Allelopathic activity nursery

description: Description of the common wheat (Triticum aestivum) allelopathic activity nursery. Tested within T2.3 in Spain (by UVIGO).
DOI: 10.18170/dvn/wrf6df
title: Evaluation of Doing Business in Chinese Provinces 2022

description: Well-established doing business works as an important basis for building a modern economic system and promoting a high-quality development. Based on the indicator system of business environment at province level in China, which contains four first-level indicators, 16 second-level indicators and 29 third-level indicators, we quantitatively evaluate the business environment across 31 provinces in mainland China from 2017 to 2020.
DOI: 10.5281/zenodo.7318724
title: Database from Preclinical Examination of Early-Onset Thalamic–Cortical Seizures after Hemispheric Stroke

description: The data files below show intracranial electroencephalography (iEEG) recordings from left hemisphere of adult CD-1 mice days before (basal, B) and after (post-stroke, P) by middle cerebral artery occlusion (infartion in the right hemisphere). The recording settings are as follows: <strong>Recording software: </strong>LabVIEW Biomedical Toolkit software (National Instruments) <strong>Data aquisition board:</strong> National Instruments PCI-6013 <strong>Sampling frequency: </strong>500 Hz <strong>AC GRASS 78D amplification: </strong>x8000 <strong>Band-pass filtered: </strong>0.3-100 Hz <strong>Notch filtered: </strong>50 Hz <strong>Voltage range: </strong>±5.00 V The files (obtained from recording software) provided are in .dat (and a header file .hea) format and consist of a single iEEG recording channel with a duration of approximately 24 hours. The files were named with a mouse's own identification code, M-(number)-(letter), separated with a low bar from their identification according to the registration before or after the infarction (B and P, respectively) and followed by the number of the day of registration (from 1 to 3 for B, from 1 to 6 for P). The files are the following: <strong>BASAL (B) </strong> <strong>B1: </strong>M3A_B1; M10B_B1; M10A_B1; M13B_B1; M17B_B1; M17C_B1; M27A_B1 <strong>B2: </strong>M3A_B2; M10B_B2; M10A_B2; M13B_B2; M17B_B2; M17C_B2; M26B_B; M27A_B2 <strong>B3: </strong>M10B_B3; M10A_B3; M13B_B3; M17B_B3; M17C_B3 <strong>POST-STROKE (P) </strong> <strong>P0: </strong>M2A_P0; M3A_P0; M10A_P0; M10B_P0; M13B_P0; M17B_P0; M17C_P0; M26B_P0; M27A_P0 <strong>P1: </strong>M2A_P1; M3A_P1; M10A_P1; M10B_P1; M13B_P1; M17B_P1; M17C_P1; M26B_P1; M27A_P1 <strong>P2: </strong>M2A_P2; M3A_P2; M10A_P2; M10B_P2; M17B_P2; M17C_P2; M26B_P2; M27A_P2 <strong>P3: </strong>M2A_P3; M3A_P3; M10A_P3; M10B_P3; M13B_P3; M17B_P3; M17C_P3; M27A_P3 <strong>P4: </strong>M3A_P4; M10A_P4; M10B_P4; M17B_P4; M17C_P4; M27A_P4 <strong>P5: </strong>M10A_P5; M10B_P5; M17B_P5; M17C_P5; M26B_P5; M27A_P5 <strong>P6: </strong>M10A_P6; M10B_P6; M17C_P6; M26B_P6; M27A_P6
DOI: 10.5281/zenodo.7318723
title: Database from Preclinical Examination of Early-Onset Thalamic–Cortical Seizures after Hemispheric Stroke

description: The data files below show intracranial electroencephalography (iEEG) recordings from left hemisphere of adult CD-1 mice days before (basal, B) and after (post-stroke, P) by middle cerebral artery occlusion (infartion in the right hemisphere). The recording settings are as follows: <strong>Recording software: </strong>LabVIEW Biomedical Toolkit software (National Instruments) <strong>Data aquisition board:</strong> National Instruments PCI-6013 <strong>Sampling frequency: </strong>500 Hz <strong>AC GRASS 78D amplification: </strong>x8000 <strong>Band-pass filtered: </strong>0.3-100 Hz <strong>Notch filtered: </strong>50 Hz <strong>Voltage range: </strong>±5.00 V The files (obtained from recording software) provided are in .dat (and a header file .hea) format and consist of a single iEEG recording channel with a duration of approximately 24 hours. The files were named with a mouse's own identification code, M-(number)-(letter), separated with a low bar from their identification according to the registration before or after the infarction (B and P, respectively) and followed by the number of the day of registration (from 1 to 3 for B, from 1 to 6 for P). The files are the following: <strong>BASAL (B) </strong> <strong>B1: </strong>M3A_B1; M10B_B1; M10A_B1; M13B_B1; M17B_B1; M17C_B1; M27A_B1 <strong>B2: </strong>M3A_B2; M10B_B2; M10A_B2; M13B_B2; M17B_B2; M17C_B2; M26B_B; M27A_B2 <strong>B3: </strong>M10B_B3; M10A_B3; M13B_B3; M17B_B3; M17C_B3 <strong>POST-STROKE (P) </strong> <strong>P0: </strong>M2A_P0; M3A_P0; M10A_P0; M10B_P0; M13B_P0; M17B_P0; M17C_P0; M26B_P0; M27A_P0 <strong>P1: </strong>M2A_P1; M3A_P1; M10A_P1; M10B_P1; M13B_P1; M17B_P1; M17C_P1; M26B_P1; M27A_P1 <strong>P2: </strong>M2A_P2; M3A_P2; M10A_P2; M10B_P2; M17B_P2; M17C_P2; M26B_P2; M27A_P2 <strong>P3: </strong>M2A_P3; M3A_P3; M10A_P3; M10B_P3; M13B_P3; M17B_P3; M17C_P3; M27A_P3 <strong>P4: </strong>M3A_P4; M10A_P4; M10B_P4; M17B_P4; M17C_P4; M27A_P4 <strong>P5: </strong>M10A_P5; M10B_P5; M17B_P5; M17C_P5; M26B_P5; M27A_P5 <strong>P6: </strong>M10A_P6; M10B_P6; M17C_P6; M26B_P6; M27A_P6
DOI: 10.5281/zenodo.5521308
title: Bio-priming of soybean with Bradyrhizobium japonicum and Bacillus megaterium

description: The data represent the impact of single and co-inoculation with<em> Bradyrhizobium japonicum</em> and <em>Bacillus megaterium</em> on seed germination and initial seedling growth of two soybean cultivars, under optimal and stressful conditions. Three laboratory tests,<em> i.e</em>., germination test, cold test, and accelerated aging test, were performed in order to evaluate seed quality and viability in relation to the applied bacterial treatments. The data are related to the publication of Miljakovic et al. (2022); doi: 10.3390/plants11151927
description: Seed bio-priming with plant growth-promoting rhizobacteria (PGPR) can enhance seed germination and improve seedling growth. Laboratory experiments were conducted to evaluate the effects of single and co-inoculation with Bradyrhizobium japonicum (strains from commercial inoculant) and Bacillus megaterium (newly isolated strains from soil) on seed germination and initial seedling growth of two soybean cultivars. The treated seeds were subjected to a germination test, a cold test and an accelerated aging test. Plant growth-promoting (PGP) characterization of the selected B. megaterium strains revealed the production of indole-3-acetic acid (IAA) and siderophores, nitrogen fixation, and phosphate solubilization/mineralization. Bacterial treatments remarkably improved seed quality and viability in all the tests, with the greatest improvement in cold-treated seeds, followed by aged and normal seeds. On average, single inoculation with B. megaterium had the highest effect on germination (4.0% and 4.1%) and seedling vigor index (12.5% and 32.4%) in normal and aged seeds. The highest increase in germination (7.8%) was detected in B. japonicum, while co-inoculation had the best effect on seedling vigor index (48.5%) of the cold-treated seeds. The results confirmed that indigenous strains of B. megaterium from soil could be used in soybean production both as single and co-inoculant, especially under adverse conditions.
DOI: 10.5281/zenodo.5521307
title: Bio-priming of soybean with Bradyrhizobium japonicum and Bacillus megaterium

description: The data represent the impact of single and co-inoculation with<em> Bradyrhizobium japonicum</em> and <em>Bacillus megaterium</em> on seed germination and initial seedling growth of two soybean cultivars, under optimal and stressful conditions. Three laboratory tests,<em> i.e</em>., germination test, cold test, and accelerated aging test, were performed in order to evaluate seed quality and viability in relation to the applied bacterial treatments. The data are related to the publication of Miljakovic et al. (2022); doi: 10.3390/plants11151927
description: Seed bio-priming with plant growth-promoting rhizobacteria (PGPR) can enhance seed germination and improve seedling growth. Laboratory experiments were conducted to evaluate the effects of single and co-inoculation with Bradyrhizobium japonicum (strains from commercial inoculant) and Bacillus megaterium (newly isolated strains from soil) on seed germination and initial seedling growth of two soybean cultivars. The treated seeds were subjected to a germination test, a cold test and an accelerated aging test. Plant growth-promoting (PGP) characterization of the selected B. megaterium strains revealed the production of indole-3-acetic acid (IAA) and siderophores, nitrogen fixation, and phosphate solubilization/mineralization. Bacterial treatments remarkably improved seed quality and viability in all the tests, with the greatest improvement in cold-treated seeds, followed by aged and normal seeds. On average, single inoculation with B. megaterium had the highest effect on germination (4.0% and 4.1%) and seedling vigor index (12.5% and 32.4%) in normal and aged seeds. The highest increase in germination (7.8%) was detected in B. japonicum, while co-inoculation had the best effect on seedling vigor index (48.5%) of the cold-treated seeds. The results confirmed that indigenous strains of B. megaterium from soil could be used in soybean production both as single and co-inoculant, especially under adverse conditions.
DOI: 10.5281/zenodo.7329866
title: NextFood Framework Metadata

description: This dataset contains underlying metadata for the NextFood Sustainability Impact Assessment Framework, intended for the monitoring, reporting and evaluation of quality assured knowledge transfer through practice-oriented research and education. The approach is intended to support the planning for a desired impact, the inclusion of relevant actors, and the fostering of interaction and mutual learning between actors.
DOI: 10.5281/zenodo.7329865
title: NextFood Framework Metadata

description: This dataset contains underlying metadata for the NextFood Sustainability Impact Assessment Framework, intended for the monitoring, reporting and evaluation of quality assured knowledge transfer through practice-oriented research and education. The approach is intended to support the planning for a desired impact, the inclusion of relevant actors, and the fostering of interaction and mutual learning between actors.
DOI: 10.5281/zenodo.6760926
title: MonkeyPox2022Tweets: The First Public Twitter Dataset on the 2022 MonkeyPox Outbreak

description: <strong>Please cite the following paper when using this dataset:</strong> N. Thakur, “MonkeyPox2022Tweets: The first public Twitter dataset on the 2022 MonkeyPox outbreak,” Preprints, 2022, DOI: 10.20944/preprints202206.0172.v2 <strong>Abstract</strong> The world is currently facing an outbreak of the monkeypox virus, and confirmed cases have been reported from 28 countries. Following a recent “emergency meeting”, the World Health Organization is considering whether the outbreak should be assessed as a “potential public health emergency of international concern” or PHEIC, as was done for the COVID-19 and Ebola outbreaks in the past. During this time, people from all over the world are using social media platforms, such as Twitter, for information seeking and sharing related to the outbreak, as well as for familiarizing themselves with the guidelines and protocols that are being recommended by various policy-making bodies to reduce the spread of the virus. This is resulting in the generation of tremendous amounts of Big Data related to such paradigms of social media behavior. Mining this Big Data and compiling it in the form of a dataset can serve a wide range of use-cases and applications such as analysis of public opinions, interests, views, perspectives, attitudes, and sentiment towards this outbreak. Therefore, this work presents MonkeyPox2022Tweets, an open-access dataset of Tweets related to the 2022 monkeypox outbreak that were posted on Twitter since the first detected case of this outbreak on May 7, 2022. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management. <strong>Data Description</strong> The dataset consists of a <strong>total of 102,452 tweet IDs</strong> of the same number of tweets about monkeypox that were posted on Twitter from 7<sup>th</sup> May 2022 to 26<sup>th</sup> June 2022 (the most recent date at the time of dataset upload). The Tweet IDs are presented in 5 different .txt files based on the timelines of the associated tweets. The following table provides the details of these dataset files. <strong>Filename</strong> <strong>No. of Tweet IDs</strong> <strong>Date Range of the Tweet IDs</strong> TweetIDs_Part1.txt 13926 May 7, 2022 to May 21, 2022 TweetIDs_Part2.txt 17705 May 21, 2022 to May 27, 2022 TweetIDs_Part3.txt 17585 May 27, 2022 to June 5, 2022 TweetIDs_Part4.txt 19718 June 5, 2022 to June 11, 2022 TweetIDs_Part5.txt 33518 June 12, 2022 to June 26, 2022 The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used. For hydrating this dataset the Hydrator application (link to download and a step-by-step tutorial on how to use Hydrator) may be used.
DOI: 10.5281/zenodo.5163326
title: Additional data; Biotechnologically produced chitosans with nonrandom acetylation patterns differ from conventional chitosans in properties and activities

description: This dataset contains additional data for the research article <em>Biotechnologically produced chitosans with nonrandom acetylation patterns differ from conventional chitosans in properties and activities</em> by Sruthi Sreekumar, Jasper Wattjes, Anna Niehues, Tamara Mengoni, Ana C. Mendes, Edwin R. Morris, Francisco M. Goycoolea, and Bruno M. Moerschbacher. The directory <em>Fig1ab_SuppFig1ab</em> contains data, code, and results of enzymatic mass-spectrometric fingerprinting experiments. The directory <em>cosms</em> contains source code and instructions to create a conda environment in which the code to create Figures 1 A+B and Supplementary Figures 1 A+B can be executed. The directory <em>Fig23456_SuppFig_2567</em> contains Excel Worksheets (.xlsx files) with data underlying Figures 2, 3, 4, 5 and 6, and Supplementary Figures 2, 5, 6 and 7. The directories <em>Fig5b</em> and <em>SuppFig7d</em> contain raw MS data (Bruker .d files) of oligomeric hydrolysis products produced by incubating different chitosans with different chitinolytic enzymes.
description: The research leading to these results has received funding from the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 613931 (B.M.M., F.M.G., A.C.M.) as well as from the German Bundesministerium für Ernährung und Landwirtschaft (BMEL) and its Fachagentur Nachwachsende Rohstoffe (FNR) under project number 22031315 (B.M.M.).
DOI: 10.5281/zenodo.5163327
title: Additional data; Biotechnologically produced chitosans with nonrandom acetylation patterns differ from conventional chitosans in properties and activities

description: This dataset contains additional data for the research article <em>Biotechnologically produced chitosans with nonrandom acetylation patterns differ from conventional chitosans in properties and activities</em> by Sruthi Sreekumar, Jasper Wattjes, Anna Niehues, Tamara Mengoni, Ana C. Mendes, Edwin R. Morris, Francisco M. Goycoolea, and Bruno M. Moerschbacher. The directory <em>Fig1ab_SuppFig1ab</em> contains data, code, and results of enzymatic mass-spectrometric fingerprinting experiments. The directory <em>cosms</em> contains source code and instructions to create a conda environment in which the code to create Figures 1 A+B and Supplementary Figures 1 A+B can be executed. The directory <em>Fig23456_SuppFig_2567</em> contains Excel Worksheets (.xlsx files) with data underlying Figures 2, 3, 4, 5 and 6, and Supplementary Figures 2, 5, 6 and 7. The directories <em>Fig5b</em> and <em>SuppFig7d</em> contain raw MS data (Bruker .d files) of oligomeric hydrolysis products produced by incubating different chitosans with different chitinolytic enzymes.
description: The research leading to these results has received funding from the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 613931 (B.M.M., F.M.G., A.C.M.) as well as from the German Bundesministerium für Ernährung und Landwirtschaft (BMEL) and its Fachagentur Nachwachsende Rohstoffe (FNR) under project number 22031315 (B.M.M.).
DOI: 10.6084/m9.figshare.20626806
title: Process-related GHG emissions in China and relevant data (1990-2020)

description: This is the data file for review of the manuscript "China’s process-related greenhouse gas emission dataset 1990-2020" submitted to Scientific Data <br> Xiang Yu 1, 2, †, Chang Tan 3, †, * 1. University of Chinese Academy of Social Sciences, Beijing 102488, China 2. Research Institute for Eco-civilization (RIEco), Chinese Academy of Social Sciences, Beijing 100710, China 3. Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China *Corresponding author: Chang Tan (tanc21@mails.tsinghua.edu.cn) † These authors should be regarded as co-first authors.
DOI: 10.6084/m9.figshare.21548007
title: Supplemental data_Excel from Metal-binding proteins and cross-linking in the defensive glue of the slug <i>Arion subfuscus</i>

description: This file contains the original data associated with figures 3 and 4, as well as MS/MS protein identification data.
DOI: 10.34808/p9tz-9y55
title: Measurements of the heat of hydration released by concrete specimens cured under adiabatic conditions

description: The DataSet contains measurements of heat of hydartion of concrete cubes (150 x 150 x 150 mm) cured under adiabatic conditions. The specimens were moulded from six types of concrete mixtures produced in the laboratory conditions. Mix #1: Portland cement CEM I 42.5R and gravel aggregate, mix #2: CEM I 42.5R and  basalt aggregate, mix#3: Portland-composite cement CEM II/B-M (S-V) 42.5 N contain silica fly ash and gravel aggregate, mix#4: CEM II/B-M (S-V) 42.5 N and basalt aggregate, mix #5: blast furnace slag cement CEM III/A 42.5N - LH/HSR/NA and gravel aggregate, mix #6: CEM III/A 42.5N - LH/HSR/NA and basalt aggregate.
The measurements of heat released by concrete during its hardening process in adiabatic conditions were performed according to the procedure specified in EN 12390-15. The adiabatic concrete calorimeter used in these studies was equipped with external insulating enclosure, calorimeter cell, two platinum PT 100 temperature sensors measuring sample and cell temperatures and PC software. The duration of each test was 168 h (7 days). The temperature of concrete specimen and the temperature of calorimeter cell was recorded at interval equals 1 min (10081 points of data were registered). The temperature measurements were used by a PID closed loop control system to ensure a stable sample conditioning according to the Standard requirements.
Each file consists of test parameters and 10 columns of data. The file has (.txt) extension and contains: number of the acquisition (1st column), time elapsed from start of the test (2nd column), total time elapsed from mixing (3rd column), concrete temperature (in oC, 4th column), calorimeter temperature (in oC, 5th column), adiabatism error (in oC/h, 6th column), measured temperature rise (in oC, 7th column), intrinsic temperature rise (in oC, 8th column), heat released of concrete (in J/kg, 9th column) and heat released of cement (in J/kg, 10th column).
DOI: 10.6084/m9.figshare.21571545
title: Fig.3:(b) Comparison between parabolic and hyperbolic models. from Blunt head impact causes a temperature rise in the brain

description: Figure 3b data; Summary - Raw data for Figure 3b (in tab-delimited format)
DOI: 10.6084/m9.figshare.21571545.v1
title: Fig.3:(b) Comparison between parabolic and hyperbolic models. from Blunt head impact causes a temperature rise in the brain

description: Figure 3b data; Summary - Raw data for Figure 3b (in tab-delimited format)
DOI: 10.6084/m9.figshare.21571548
title: Fig.2: Impact loading time history. from Blunt head impact causes a temperature rise in the brain

description: Figure 2 data; Summary - Raw data for Figure 2 (in tab-delimited format)
DOI: 10.6084/m9.figshare.21571548.v1
title: Fig.2: Impact loading time history. from Blunt head impact causes a temperature rise in the brain

description: Figure 2 data; Summary - Raw data for Figure 2 (in tab-delimited format)
DOI: 10.6084/m9.figshare.21571551
title: JRSOS_Data.xlsx from Blunt head impact causes a temperature rise in the brain

description: Data for all figures; Summary - Raw data for all figures (in Excel format)
DOI: 10.6084/m9.figshare.21571554.v1
title: Fig.3:(b) Comparison between parabolic and hyperbolic models. from Blunt head impact causes a temperature rise in the brain

description: Figure 3a data; Summary - Raw data for Figure 3a (in tab-delimited format)
DOI: 10.6084/m9.figshare.21571551.v1
title: JRSOS_Data.xlsx from Blunt head impact causes a temperature rise in the brain

description: Data for all figures; Summary - Raw data for all figures (in Excel format)
DOI: 10.6084/m9.figshare.21571554
title: Fig.3:(b) Comparison between parabolic and hyperbolic models. from Blunt head impact causes a temperature rise in the brain

description: Figure 3a data; Summary - Raw data for Figure 3a (in tab-delimited format)
DOI: 10.34740/kaggle/dsv/4048355
title: Lane segmentation
DOI: 10.6084/m9.figshare.21542952
title: LSDV_genomes

description: LSDV genomes for Oxford Hackathon n=60
DOI: 10.6084/m9.figshare.21542952.v2
title: LSDV_genomes

description: LSDV genomes for Oxford Hackathon n=60
DOI: 10.6084/m9.figshare.21542952.v1
title: LSDV_genomes

description: LSDV genomes for Oxford Hackathon n=60