cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3939270
title: In situ temperature and brGDGTs measurements in soils from the Tropical Andes of Colombia and a tropical soil brGDGT compilation dataset

description: This dataset contain: Table S1. Air and soil mean monthly temperatures from the new 16 sites in the Eastern Cordillera of Colombia with data presented in this study.<br> Table S3. Soil Branched glycerol dialkyl glycerol tetraethers (brGDGTs) from the Eastern Cordillera of Colombia and data compilation from the tropics.<br> Table S4. nonlinear brGDGTs predictors for the tropical compilation data.
description: These files are the supplementary information for Pérez‐Angel, L. C., Sepúlveda, J., Molnar, P., Montes, C., Rajagopalan, B., Snell, K., Gonzalez-Arango, C., Dildar, N., (2020). Soil and air temperature calibrations using branched GDGTs for the Tropical Andes of Colombia: Towards a pan‐tropical calibration. Geochemistry, Geophysics, Geosystems, 21, e2020GC008941. https://doi.org/10.1029/2020GC008941 This work was funded by the National Science Foundation NSF | GEO | Division of Earth Sciences (EAR): 1929199
DOI: 10.5281/zenodo.8196589
title: Body size and geographic distribution of foraminiferal specimens during the Frasnian-Famennian (Frasnian), Guadalupian-Lopingian (Capitanian), Permian-Triassic (Changhsingian), Triassic-Jurassic (Rhaetian), and Cretaceous-Paleogene (Maastrichtian) mass extinctions.

description: This dataset is about body size and geographic distribution of foraminiferal specimens during the Frasnian-Famennian (Frasnian), Guadalupian-Lopingian (Capitanian), Permian-Triassic (Changhsingian), Triassic-Jurassic (Rhaetian), and Cretaceous-Paleogene (Maastrichtian) mass extinctions. Foraminiferal body size data collection First, we collected published papers with foraminiferal images from the Frasnian (382.7-372.2 Ma), Capitanian (264.28-259.51 Ma), Changhsingian (254.14-251.902 Ma), Rhaetian (208.5-201.3 Ma), and Maastrichtian (72.1-66.1 Ma). Second, we measured two primary axes of specimens, such as maximum length and height in a conical or cylindrical shell. For some specimens, where there is no way to measure the length of both axes, we calculate the length of the unknown axis based on the aspect ratio of the type species. Third, we used the test volume as a standard indicator of body size, taking into account the diversity of foraminiferal morphology. Finally, the test volumes were calculated on a logarithmic scale with base 10 due to large individual differences, and the specific calculation is shown in Feng et al. (2020).
DOI: 10.5281/zenodo.8196588
title: Body size and geographic distribution of foraminiferal specimens during the Frasnian-Famennian (Frasnian), Guadalupian-Lopingian (Capitanian), Permian-Triassic (Changhsingian), Triassic-Jurassic (Rhaetian), and Cretaceous-Paleogene (Maastrichtian) mass extinctions.

description: This dataset is about body size and geographic distribution of foraminiferal specimens during the Frasnian-Famennian (Frasnian), Guadalupian-Lopingian (Capitanian), Permian-Triassic (Changhsingian), Triassic-Jurassic (Rhaetian), and Cretaceous-Paleogene (Maastrichtian) mass extinctions. Foraminiferal body size data collection First, we collected published papers with foraminiferal images from the Frasnian (382.7-372.2 Ma), Capitanian (264.28-259.51 Ma), Changhsingian (254.14-251.902 Ma), Rhaetian (208.5-201.3 Ma), and Maastrichtian (72.1-66.1 Ma). Second, we measured two primary axes of specimens, such as maximum length and height in a conical or cylindrical shell. For some specimens, where there is no way to measure the length of both axes, we calculate the length of the unknown axis based on the aspect ratio of the type species. Third, we used the test volume as a standard indicator of body size, taking into account the diversity of foraminiferal morphology. Finally, the test volumes were calculated on a logarithmic scale with base 10 due to large individual differences, and the specific calculation is shown in Feng et al. (2020).
DOI: 10.6084/m9.figshare.23805141
title: Live Software Development (Systematic Literature Review) - Data

description: <pre>This dataset contains all the data used throughout the Systematic Literature Review done on the topic of Live Software Development.</pre>
DOI: 10.6084/m9.figshare.23805141.v1
title: Live Software Development (Systematic Literature Review) - Data

description: <pre>This dataset contains all the data used throughout the Systematic Literature Review done on the topic of Live Software Development.</pre>
DOI: 10.6084/m9.figshare.23805135
title: JSON_Update_Data.zip

description: This file describes how the performance tests in the paper "The Advantage of Partial Updates for Modification of JSON Data" can be reproduced. (All necessary files can be found in the zip file called "Partial_Update").<br>Creation of the tables<br>First, the four tables must be created. The corresponding SQL statements can be found in the file "!Figure2a.docx" (see cases CT1 till CT4).<br>Initial load<br>Second, the data must be loaded in these four tables. To load data into the "large" tables, use the SQL statements given in the file "Figure2a.docx" (see cases I3 and I4). To load the tables table_text_30K and table_json_30K with data, execute the statements given in the files INSERT_table_text and INSERT_table_json,respectively. To reach the size of the tables used in th paper, execute each INSERT statement 25 times.Different UPDATE statements<br>All UPDATE statements used for performance measurement are given in the files "!Figure2a.docx" and "!Figure2b.docx". The paper describes which UPDATE statement is used for a particular measurement.For instance, to measure the performance of the topic "Insert an Array's Element" (section 5.2.7), the UPDATE statements in IA1 till IA4 should be used.
DOI: 10.6084/m9.figshare.23805135.v1
title: JSON_Update_Data.zip

description: This file describes how the performance tests in the paper "The Advantage of Partial Updates for Modification of JSON Data" can be reproduced. (All necessary files can be found in the zip file called "Partial_Update").<br>Creation of the tables<br>First, the four tables must be created. The corresponding SQL statements can be found in the file "!Figure2a.docx" (see cases CT1 till CT4).<br>Initial load<br>Second, the data must be loaded in these four tables. To load data into the "large" tables, use the SQL statements given in the file "Figure2a.docx" (see cases I3 and I4). To load the tables table_text_30K and table_json_30K with data, execute the statements given in the files INSERT_table_text and INSERT_table_json,respectively. To reach the size of the tables used in th paper, execute each INSERT statement 25 times.Different UPDATE statements<br>All UPDATE statements used for performance measurement are given in the files "!Figure2a.docx" and "!Figure2b.docx". The paper describes which UPDATE statement is used for a particular measurement.For instance, to measure the performance of the topic "Insert an Array's Element" (section 5.2.7), the UPDATE statements in IA1 till IA4 should be used.
DOI: 10.6084/m9.figshare.23805138.v1
title: Earthquake catalog for part of Southwestern China, provided by the China Earthquake Network Center, is availa

description: This is dataset used in Liu Y., Zhuang J., Guo Y., Jiang C., Tian Q, Y. Zhang and Long F. (2003) <b>Background and Clustering Characteristics of Recent Seismicity in Southwestern China</b>. Submitted to Journal of Geophysical Research: Solid Earth.<br>
DOI: 10.6084/m9.figshare.23805153
title: JSON_Partial_Update

description: This file describes how the performance tests in the paper "The Advantage of Partial Updates for Modification of JSON Data" can be reproduced. (All necessary files can be found in the zip file called "JSON_Partial_Update").Creation of the tables<br>First, the four tables must be created. The corresponding SQL statements can be found in the file "!Figure2a.docx" (see cases CT1 till CT4).Initial load<br>Second, the data must be loaded in these four tables. To load data into the "large" tables, use the SQL statements given in the file "Figure2a.docx" (see cases I3 and I4). To load the tables table_text_30K and table_json_30K with data, execute the statements given in the files INSERT_table_text and INSERT_table_json, respectively. To reach the size of the tables used in the paper, execute each INSERT statement 25 times.Different UPDATE statements<br>All UPDATE statements used for performance measurement are given in the files "!Figure2a.docx" and "!Figure2b.docx". The paper describes which UPDATE statement is used for a particular measurement. For instance, to measure the performance of the topic "Insert an Array's Element" (section 5.2.7), the UPDATE statements in IA1 till IA4 should be used. <br>
DOI: 10.6084/m9.figshare.23805153.v1
title: JSON_Partial_Update

description: This file describes how the performance tests in the paper "The Advantage of Partial Updates for Modification of JSON Data" can be reproduced. (All necessary files can be found in the zip file called "JSON_Partial_Update").Creation of the tables<br>First, the four tables must be created. The corresponding SQL statements can be found in the file "!Figure2a.docx" (see cases CT1 till CT4).Initial load<br>Second, the data must be loaded in these four tables. To load data into the "large" tables, use the SQL statements given in the file "Figure2a.docx" (see cases I3 and I4). To load the tables table_text_30K and table_json_30K with data, execute the statements given in the files INSERT_table_text and INSERT_table_json, respectively. To reach the size of the tables used in the paper, execute each INSERT statement 25 times.Different UPDATE statements<br>All UPDATE statements used for performance measurement are given in the files "!Figure2a.docx" and "!Figure2b.docx". The paper describes which UPDATE statement is used for a particular measurement. For instance, to measure the performance of the topic "Insert an Array's Element" (section 5.2.7), the UPDATE statements in IA1 till IA4 should be used. <br>
DOI: 10.34740/kaggle/dsv/6219316
title: Egypt Gold Prices | Daily Updated
DOI: 10.5281/zenodo.7556745
title: Code for "A synchronized decline in the effects of warming on photosynthetic productivity and tree phenology"

description: This dataset contains the R codes of cleaning phenology dataset, matching climate factors, analysing temperature sensitivity of phenology and gross primary productivity and ploting images. PEP725_EOBS.r is a file of matching climate factors with ground observation phenological records. pep725_code_integ.r and GIMMS_code_integ.r are the files of primary analysis and plots.
DOI: 10.5519/qd.tpjpq2g4
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (7 records)
DOI: 10.6084/m9.figshare.22776218
title: Data and code for Fletcher et al. (2023): A framework for linking dispersal biology to connectivity across landscapes

description: Data and code for the empirical example on Florida black bears provided in Fletcher et al. 2023 (DOI: 10.1007/s10980-023-01741-8). bear_fig4.R: code to re-create Figure 4. This code illustrates tuning of different types of random-walk kernels.bear_fig5.R: code to re-create Figure 5. This code illustrates how to apply random-walk kernels across entire landscapes and illustrates using net-visitation rates to map movement in the context of dispersal kernels.bear_data.zip: folder including all spatial data needed for the example with the Florida black bear.
DOI: 10.5281/zenodo.8181020
title: A Systematic Review of LET-guided Treatment Plan Optimisation in Proton Therapy: Identifying the current state and future needs

description: Data extraction materials, TikZ and Python code for Figures 1 and 3, respectively, for the paper "A Systematic Review of LET-guided Treatment Plan Optimisation in Proton Therapy: Identifying the current state and future needs" published in Cancers, 2023.
DOI: 10.5281/zenodo.8181019
title: A Systematic Review of LET-guided Treatment Plan Optimisation in Proton Therapy: Identifying the current state and future needs

description: Data extraction materials, TikZ and Python code for Figures 1 and 3, respectively, for the paper "A Systematic Review of LET-guided Treatment Plan Optimisation in Proton Therapy: Identifying the current state and future needs" published in Cancers, 2023.
DOI: 10.21227/khgf-a775
title: The mental health of the elderly

description: A two-stage sampling method was employed for this study. In the first stage, five elderly care institutions in Tangshan Province were randomly selected from a pool of 70 institutions listed in the Notice of Hebei Provincial Department of Civil Affairs on the Grading Results of Elderly Care Institutions in January 2022. In the second stage, cluster sampling was conducted among the elderly residents within these five selected institutions. A total of 491 elderly individuals were initially surveyed, and 456 valid questionnaires were collected, resulting in an effective recovery rate of 92.9%.  To investigate the current status of mental health among older adults in nursing homes and examine the relationships between self-efficacy, mental health, sense of belonging, and happiness, as well as the mediating role of sense of belonging and happiness in the chain of effects.
DOI: 10.5281/zenodo.8186988
title: Dataset for: AutoSourceID-Classifier

description: This is the dataset used in the paper AutoSourceID-Classifier for Training, Test, Validation and Calibration. This repository contains: the folders with Training, Test, Validation and Calibration images and labels used for the analysis the catalogue with information on all the individual sources used in this paper If you use the dataset, please cite the appropriate DOIs. Code available at https://github.com/FiorenSt/AutoSourceID-Classifier
DOI: 10.5281/zenodo.8186987
title: Dataset for: AutoSourceID-Classifier

description: This is the dataset used in the paper AutoSourceID-Classifier for Training, Test, Validation and Calibration. This repository contains: the folders with Training, Test, Validation and Calibration images and labels used for the analysis the catalogue with information on all the individual sources used in this paper If you use the dataset, please cite the appropriate DOIs. Code available at https://github.com/FiorenSt/AutoSourceID-Classifier
DOI: 10.34740/kaggle/dsv/6219566
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.5281/zenodo.8196858
title: Dataset_Libye

description: Dataset je součástí článku o schopnostech a kapacitách evropských vzdušných sil a jejich vývoji v kontextu jejich významu a přesunu americké pozornosti do Asie.
DOI: 10.5281/zenodo.8196857
title: Dataset_Libye

description: Dataset je součástí článku o schopnostech a kapacitách evropských vzdušných sil a jejich vývoji v kontextu jejich významu a přesunu americké pozornosti do Asie.
DOI: 10.34740/kaggle/dsv/6219793
title: 2021 World Population (updated daily)
DOI: 10.34740/kaggle/dsv/6219791
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.48366/r603438
title: Comparison on Large Language Model augmented Knowledge Graph Query Answering

description: Knowledge graph question answering (KGQA) providing answers to natural language questions based on the structured information stored in knowledge graphs. Recently, studies are adopting LLMs to bridge the gap between natural language questions and structured knowledge graphs. This comparison will explore the models for Knowledge Graph Query Answering.
DOI: 10.5281/zenodo.8193007
title: DELETE (incorrect upload)

description: delete
DOI: 10.5281/zenodo.8193006
title: DELETE (incorrect upload)

description: delete
DOI: 10.5281/zenodo.8196984
title: arion_rufus_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8196983
title: arion_rufus_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8196992
title: clione_limacina_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8196991
title: clione_limacina_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8197006
title: concholepas_concholepas_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8197005
title: concholepas_concholepas_zanfona_1.fasta.gz

description: .
DOI: 10.34740/kaggle/dsv/6220037
title: COVID Vaccination in World (updated daily)
DOI: 10.6084/m9.figshare.23805324
title: A dataset for target recognition of bone marrow aspirate cells

description: There are 15 types of bone marrow cells in the data set, and their category names are in the file VOC-names.
DOI: 10.6084/m9.figshare.22347778.v1
title: Supplementary Tables.xlsx

description: （1）Target genes of the differentially expressed miRNAs； （2）miRNAs count in each sample Differentially expressed miRNA between groups E and F ； （3）Differentially expressed miRNA between groups C and E ； （4）Differentially expressed miRNA between groups D and F （5）GO analysis for miRNA-predicted target genes （6）KEGG pathway analyses for miRNA-predicted target genes
DOI: 10.5281/zenodo.8197122
title: FROST spectral measurements

description: Datasets collected for the development and performance testing of a low-cost instrument for fast-synchronized spatial measurements of light spectra (FROST). Figure references correspond with the Figures as published in AMT, Heusinkveld et al., 2023. Of special interest are the spectral measurements (ASD Fieldspec) of 11 March and 15 May 2022 at Wageningen Weather station Veenkampen, The Netherlands and the experimental data collected with the light spectroscopy sensor made by AMS, Austria: type: AS7265x including its spectral response data. Light diffusing PTFE and Acrylic glass filters light spectral data are also included. Reference: Heusinkveld, B.G., Mol, W.B., van Heerwaarden, C.: A new accurate low-cost instrument for fast synchronized spatial measurements of light spectra, 2023.
DOI: 10.5281/zenodo.8197123
title: FROST spectral measurements

description: Datasets collected for the development and performance testing of a low-cost instrument for fast-synchronized spatial measurements of light spectra (FROST). Figure references correspond with the Figures as published in AMT, Heusinkveld et al., 2023. Of special interest are the spectral measurements (ASD Fieldspec) of 11 March and 15 May 2022 at Wageningen Weather station Veenkampen, The Netherlands and the experimental data collected with the light spectroscopy sensor made by AMS, Austria: type: AS7265x including its spectral response data. Light diffusing PTFE and Acrylic glass filters light spectral data are also included. Reference: Heusinkveld, B.G., Mol, W.B., van Heerwaarden, C.: A new accurate low-cost instrument for fast synchronized spatial measurements of light spectra, 2023.
DOI: 10.5281/zenodo.8197128
title: FROST spectral measurements

description: Datasets collected for the development and performance testing of a low-cost instrument for fast-synchronized spatial measurements of light spectra (FROST). Figure references correspond with the Figures as published in AMT, Heusinkveld et al., 2023. Of special interest are the spectral measurements (ASD Fieldspec) of 11 March and 15 May 2022 at Wageningen Weather station Veenkampen, The Netherlands and the experimental data collected with the light spectroscopy sensor made by AMS, Austria: type: AS7265x including its spectral response data. Light diffusing PTFE and Acrylic glass filters light spectral data are also included. Reference: Heusinkveld, B.G., Mol, W.B., van Heerwaarden, C.: A new accurate low-cost instrument for fast synchronized spatial measurements of light spectra, 2023.
DOI: 10.6084/m9.figshare.22347778
title: Supplementary Tables and Figures

description: Table S1 Primer sequences for PCRTable S2 Target genes of rno-miR-124-3p<b>Figure S1 The relationship between VEGF and Mapk14 in the KEGG database.</b> (A) pathways in cancer (rno05200); (B) MAPK signaling pathway (rno04010).
DOI: 10.6084/m9.figshare.22347778.v3
title: Supplementary Tables and Figures

description: Table S1 Primer sequences for PCRTable S2 Target genes of rno-miR-124-3p<b>Figure S1 The relationship between VEGF and Mapk14 in the KEGG database.</b> (A) pathways in cancer (rno05200); (B) MAPK signaling pathway (rno04010).
DOI: 10.6084/m9.figshare.22347778.v2
title: Supplementary Tables.xlsx

description: Table S1 Primer sequences for PCRTable S2 Target genes of rno-miR-124-3p
DOI: 10.6084/m9.figshare.23512479
title: <strong>Supporting data for Magnetic reconnection in the Martian magnetotail: occurrence rate and impact on ion loss </strong>

description: The data files contain the parameters plotted in the Figures of Magnetic reconnection in the Martian magnetotail: occurrence rate and impact on ion loss.
DOI: 10.5281/zenodo.8197188
title: depressigyra_globulus_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.8197187
title: depressigyra_globulus_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.8197203
title: trichoglossus_haematodus_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8197202
title: trichoglossus_haematodus_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8197028
title: The symmetry-induced numerosity illusion depends on visual attention

description: For the ST, ST-WD: each file contains a matrix called “a”. Each row of the matrix “a” is a trial. The columns contain the following information: 1st: Numerosity of test stimulus 2nd: Log10 of numerosity of test stimulus divided by the standard numerosity 3rd: Participant response (0 = standard stimulus; 1 = test stimulus) 4th: 200 5th: Standard numerosity 6th: Dots size in pixels 7th: 10 8th: Condition (0= symmetry condition; 1= random condition) 9th: Standard-Test order (1 = standard first; 2 = standard second) 10th: Response time 11th: -99 12th: -99 13th: -99 14th: 0 For the DT: each file contains a matrix called “a”. Each row of the matrix “a” is a trial. The columns contain the following information: 1st: Numerosity of test stimulus 2nd: Log10 of numerosity of test stimulus divided by the standard numerosity 3rd: Participant response (0 = standard stimulus; 1 = test stimulus) 4th: 200 5th: Standard numerosity 6th: Dots size in pixels 7th: 10 8th: Condition (0= symmetry condition; 1= random condition) 9th: Standard-Test order (1 = standard first; 2 = standard second) 10th: Response time to the numerosity task 11th: Response to the color-conjunction task (1: target; 0: no-target) 12th: code of color-conjuction stimulus presented onscreen 13th: Accuracy to the color-conjuction task (1:correct; 0:wrong) 14th: 1
DOI: 10.5281/zenodo.8197027
title: The symmetry-induced numerosity illusion depends on visual attention

description: For the ST, ST-WD: each file contains a matrix called “a”. Each row of the matrix “a” is a trial. The columns contain the following information: 1st: Numerosity of test stimulus 2nd: Log10 of numerosity of test stimulus divided by the standard numerosity 3rd: Participant response (0 = standard stimulus; 1 = test stimulus) 4th: 200 5th: Standard numerosity 6th: Dots size in pixels 7th: 10 8th: Condition (0= symmetry condition; 1= random condition) 9th: Standard-Test order (1 = standard first; 2 = standard second) 10th: Response time 11th: -99 12th: -99 13th: -99 14th: 0 For the DT: each file contains a matrix called “a”. Each row of the matrix “a” is a trial. The columns contain the following information: 1st: Numerosity of test stimulus 2nd: Log10 of numerosity of test stimulus divided by the standard numerosity 3rd: Participant response (0 = standard stimulus; 1 = test stimulus) 4th: 200 5th: Standard numerosity 6th: Dots size in pixels 7th: 10 8th: Condition (0= symmetry condition; 1= random condition) 9th: Standard-Test order (1 = standard first; 2 = standard second) 10th: Response time to the numerosity task 11th: Response to the color-conjunction task (1: target; 0: no-target) 12th: code of color-conjuction stimulus presented onscreen 13th: Accuracy to the color-conjuction task (1:correct; 0:wrong) 14th: 1
DOI: 10.34740/kaggle/dsv/6220595
title: Mahanagar Yatayat Transport Dataset