cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26050/wdcc/ar6.c6acmrme0
title: IPCC DDC: MRI MRI-ESM2.0 model output prepared for CMIP6 AerChemMIP

description: Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.AerChemMIP.MRI.MRI-ESM2-0' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MRI-ESM2.0 climate model, released in 2017, includes the following components: aerosol: MASINGAR mk2r4 (TL95; 192 x 96 longitude/latitude; 80 levels; top level 0.01 hPa), atmos: MRI-AGCM3.5 (TL159; 320 x 160 longitude/latitude; 80 levels; top level 0.01 hPa), atmosChem: MRI-CCM2.1 (T42; 128 x 64 longitude/latitude; 80 levels; top level 0.01 hPa), land: HAL 1.0, ocean: MRI.COM4.4 (tripolar primarily 0.5 deg latitude/1 deg longitude with meridional refinement down to 0.3 deg within 10 degrees north and south of the equator; 360 x 364 longitude/latitude; 61 levels; top grid cell 0-2 m), ocnBgchem: MRI.COM4.4, seaIce: MRI.COM4.4. The model was run by the Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan (MRI) in native nominal resolutions: aerosol: 250 km, atmos: 100 km, atmosChem: 250 km, land: 100 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.
DOI: 10.17028/rd.lboro.23814873
title: Continuous glucose monitor (CGM) and venous plasma glucose (VPG) data for: Exploration of the correlation between glucose dynamics and energy intake in women

description: This presents a segment of the data collected for a glucose dip study. The dataset includes continuous glucose monitor (CGM) readings and corresponding venous plasma glucose (VPG) values from a cohort of 30 healthy young women.Project summary:Blood glucose has been identified as a potential factor implicated in the control of appetite and eating behaviour. Several studies have investigated this suggestion but findings have been conflicting. Recent studies using modern digital devices to measure glucose (continuous glucose monitors (CGMs)) suggest the decline in glucose after a meal is associated with greater appetite feelings and food intake. The study collected some pilot data using CGMs to examine whether meal-induced reductions in glucose can be replicated in a lab environment and to explore whether glucose profiles after a meal are associated with other appetite-related outcomes.<br><br>
DOI: 10.17028/rd.lboro.23814873.v1
title: Continuous glucose monitor (CGM) and venous plasma glucose (VPG) data for: Exploration of the correlation between glucose dynamics and energy intake in women

description: This presents a segment of the data collected for a glucose dip study. The dataset includes continuous glucose monitor (CGM) readings and corresponding venous plasma glucose (VPG) values from a cohort of 30 healthy young women.Project summary:Blood glucose has been identified as a potential factor implicated in the control of appetite and eating behaviour. Several studies have investigated this suggestion but findings have been conflicting. Recent studies using modern digital devices to measure glucose (continuous glucose monitors (CGMs)) suggest the decline in glucose after a meal is associated with greater appetite feelings and food intake. The study collected some pilot data using CGMs to examine whether meal-induced reductions in glucose can be replicated in a lab environment and to explore whether glucose profiles after a meal are associated with other appetite-related outcomes.<br><br>
DOI: 10.25403/upresearchdata.23899269.v1
title: Skills required for curriculum development at nursing education institutions

description: Data for a non-experimental, descriptive, quantitative study. A non-probability stratified random sampling was used to select respondents. A web-based self-administered questionnaire was shared with 320 Gauteng Province nurse educators and 82 (25.6%) responded. Collected data was analyzed using the International Business Machine for Statistical Package for the Social Sciences, version 28. The exploratory factor analysis, the Cronbach alpha together with the Spearman’s rho, the independent-samples, the Kruskal-Mann-Whitney U test; and the Kruskal-Wallis tests were done. The study found that the Gauteng province nurse educators, who responded to the study, demonstrated that the feeling of inadequate training, the lack of confidence in proper communication and presentation skills, the development of study modules, the review skills, and the knowledge of managing finances and assets limited them.
DOI: 10.25403/upresearchdata.23899269
title: Skills required for curriculum development at nursing education institutions

description: Data for a non-experimental, descriptive, quantitative study. A non-probability stratified random sampling was used to select respondents. A web-based self-administered questionnaire was shared with 320 Gauteng Province nurse educators and 82 (25.6%) responded. Collected data was analyzed using the International Business Machine for Statistical Package for the Social Sciences, version 28. The exploratory factor analysis, the Cronbach alpha together with the Spearman’s rho, the independent-samples, the Kruskal-Mann-Whitney U test; and the Kruskal-Wallis tests were done. The study found that the Gauteng province nurse educators, who responded to the study, demonstrated that the feeling of inadequate training, the lack of confidence in proper communication and presentation skills, the development of study modules, the review skills, and the knowledge of managing finances and assets limited them.
DOI: 10.25666/dataubfc-2023-07-06
title: 4′-(N-(Propargyl)pyrrol-2-yl)-2,2′:6′,2′′-terpyridine
title: 4′-(N-(Propargyl)pyrrol-2-yl)-2,2′:6′,2′′-terpyridine

description: A new terpyridine molecule, bearing a N-propargylated pyrrole, was prepared and characterized. Its synthesis was based on a Krohnke-type reaction between 2-acetylpyridine and N-propargylpyrrole-2-carboxaldehyde in a basic medium. An allene-containing terpyridine was also obtained as a by-product.
description: Une nouvelle molécule de la famille des terpyridines, portant un pyrrole N-propargylé à été préparée et caractérisée. Sa synthèse est basée sur une réaction de type Kröhnke entre la 2-acétylpyridine et le N-propargylpyrrole-2-aldéhyde en milieu basique. Un co-produit portant une fonction allène est aussi obtenu au cours de la réaction.
description: Audience: University: master, Research
description: UpdatePeriodicity: no update
description: Project: CoMICS - Chemistry Of Molecular Interactions Catalysis & Sensors, FEDER (European project), Région BFC (Region Bourgogne Franche-Comté)
description: Experimental data: Organic synthesis
description: Données expérimentales: Synthèse organique
DOI: 10.5281/zenodo.8223832
title: Database fro papers entitled Disease and viruses as negative factor prohibiting the growth of broiler chicken embryo as research topic trend: a bibliometric review

description: This database of documents used for analysis using bibliometric review were downloaded from Scopus website. The data was used for a published paper in F1000 Research journal with intended title. This data can be downloaded and reproduced for research purpose
DOI: 10.5281/zenodo.8223831
title: Database fro papers entitled Disease and viruses as negative factor prohibiting the growth of broiler chicken embryo as research topic trend: a bibliometric review

description: This database of documents used for analysis using bibliometric review were downloaded from Scopus website. The data was used for a published paper in F1000 Research journal with intended title. This data can be downloaded and reproduced for research purpose
DOI: 10.6084/m9.figshare.23904438
title: Telemedicine at a music festival - Evaluation of the technologiy by the tele-paramedic and the tele-physician

description: Raw data of the study of telemedicine at a music festival with the delegation of medical measures from a tele-physician to a lower-qualified paramedics (tele-paramedic). In these documents, the evaluation of the technology from the perspective of the tele-physician and tele-paramedics is presented. Document language: German
DOI: 10.6084/m9.figshare.23904438.v1
title: Telemedicine at a music festival - Evaluation of the technologiy by the tele-paramedic and the tele-physician

description: Raw data of the study of telemedicine at a music festival with the delegation of medical measures from a tele-physician to a lower-qualified paramedics (tele-paramedic). In these documents, the evaluation of the technology from the perspective of the tele-physician and tele-paramedics is presented. Document language: German
DOI: 10.5281/zenodo.1040777
title: Ligands detected by CheckMyBlob in the Protein Data Bank

description: A dataset containing numerical descriptions all ligands that CheckMyBlob was capable of detecting automatically on the entire PDB as of May 1st, 2017. It is the "master" data set containing all ligands queried and detected as described in the Kowiel et al. paper "Automatic recognition of ligands in electron density by machine learning methods". The file is compressed using 7zip to allow for faster downloads. The compressed file weighs around 1.1 GB, whereas the uncompressed CSV will take close to 3.0 GB of disk space. The all_summary.csv file can be used to reproduce the filtered ligand data sets (CMB, TAMC, CL) described in the Kowiel et al. paper. Additionally, the data set can be used as a source to create data sets based on other filtering criteria (e.g. ligand subsets of your choice), or on its own a as source of knowledge about all ligands that CheckMyBlob was capable of detecting automatically on the entire PDB as of May 1st, 2017. For machine learning applications, please read the code at https://github.com/dabrze/CheckMyBlob, to see examples describing how to preprocess the data. In particular, the following attributes should not be used during model training or testing: "pdb_code", "res_id", "chain_id", "local_res_atom_count", "local_res_atom_non_h_count", "local_res_atom_non_h_occupancy_sum", "local_res_atom_non_h_electron_sum", "local_res_atom_non_h_electron_occupancy_sum", "local_res_atom_C_count", "local_res_atom_N_count", "local_res_atom_O_count", "local_res_atom_S_count", "dict_atom_non_h_count", "dict_atom_non_h_electron_sum", "dict_atom_C_count", "dict_atom_N_count", "dict_atom_O_count", "dict_atom_S_count", "fo_col", "fc_col", "weight_col", "grid_space", "solvent_radius", "solvent_opening_radius", "part_step_FoFc_std_min", "part_step_FoFc_std_max", "part_step_FoFc_std_step", "local_volume", "res_coverage", "blob_coverage", "blob_volume_coverage", "blob_volume_coverage_second", "res_volume_coverage", "res_volume_coverage_second", "skeleton_data", "resolution_max_limit", "part_step_FoFc_std_min", "part_step_FoFc_std_max", "part_step_FoFc_std_step". The target attribute for classification is: <strong>res_name</strong>.
DOI: 10.5281/zenodo.1040778
title: Ligands detected by CheckMyBlob in the Protein Data Bank

description: A dataset containing numerical descriptions all ligands that CheckMyBlob was capable of detecting automatically on the entire PDB as of May 1st, 2017. It is the "master" data set containing all ligands queried and detected as described in the Kowiel et al. paper "Automatic recognition of ligands in electron density by machine learning methods". The file is compressed using 7zip to allow for faster downloads. The compressed file weighs around 1.1 GB, whereas the uncompressed CSV will take close to 3.0 GB of disk space. The all_summary.csv file can be used to reproduce the filtered ligand data sets (CMB, TAMC, CL) described in the Kowiel et al. paper. Additionally, the data set can be used as a source to create data sets based on other filtering criteria (e.g. ligand subsets of your choice), or on its own a as source of knowledge about all ligands that CheckMyBlob was capable of detecting automatically on the entire PDB as of May 1st, 2017. For machine learning applications, please read the code at https://github.com/dabrze/CheckMyBlob, to see examples describing how to preprocess the data. In particular, the following attributes should not be used during model training or testing: "pdb_code", "res_id", "chain_id", "local_res_atom_count", "local_res_atom_non_h_count", "local_res_atom_non_h_occupancy_sum", "local_res_atom_non_h_electron_sum", "local_res_atom_non_h_electron_occupancy_sum", "local_res_atom_C_count", "local_res_atom_N_count", "local_res_atom_O_count", "local_res_atom_S_count", "dict_atom_non_h_count", "dict_atom_non_h_electron_sum", "dict_atom_C_count", "dict_atom_N_count", "dict_atom_O_count", "dict_atom_S_count", "fo_col", "fc_col", "weight_col", "grid_space", "solvent_radius", "solvent_opening_radius", "part_step_FoFc_std_min", "part_step_FoFc_std_max", "part_step_FoFc_std_step", "local_volume", "res_coverage", "blob_coverage", "blob_volume_coverage", "blob_volume_coverage_second", "res_volume_coverage", "res_volume_coverage_second", "skeleton_data", "resolution_max_limit", "part_step_FoFc_std_min", "part_step_FoFc_std_max", "part_step_FoFc_std_step". The target attribute for classification is: <strong>res_name</strong>.
DOI: 10.5281/zenodo.1040850
title: CheckMyBlob evaluation data set (TAMC)

description: A data set of ligands used to evaluate the CheckMyBlob method, described in the Kowiel et al. paper "<em>Automatic recognition of ligands in electron density by machine learning methods</em>". This data set attempts to repeat the experimental setup from Terwilliger <em>et al.</em> described in "<em>Ligand identification using electron-density map correlations</em>". It consists of ligands from X-ray diffraction experiments with 6–150 non-H atoms. Connected PDB ligands were labeled as single alphabetically ordered strings of hetero-compound codes, whereas unknown species, water molecules, standard amino acids, and nucleotides were excluded. Finally, the data set was limited to 200 most popular ligands. The resulting data set consisted of 161,758 examples with individual ligand counts ranging from 36,535 examples for GOL (glycerol) to 114 for LMG (1,2-distearoyl-monogalactosyl-diglyceride). For machine learning (classification) purposes, the target attribute is: <strong>res_name</strong>.
DOI: 10.5281/zenodo.1040851
title: CheckMyBlob evaluation data set (TAMC)

description: A data set of ligands used to evaluate the CheckMyBlob method, described in the Kowiel et al. paper "<em>Automatic recognition of ligands in electron density by machine learning methods</em>". This data set attempts to repeat the experimental setup from Terwilliger <em>et al.</em> described in "<em>Ligand identification using electron-density map correlations</em>". It consists of ligands from X-ray diffraction experiments with 6–150 non-H atoms. Connected PDB ligands were labeled as single alphabetically ordered strings of hetero-compound codes, whereas unknown species, water molecules, standard amino acids, and nucleotides were excluded. Finally, the data set was limited to 200 most popular ligands. The resulting data set consisted of 161,758 examples with individual ligand counts ranging from 36,535 examples for GOL (glycerol) to 114 for LMG (1,2-distearoyl-monogalactosyl-diglyceride). For machine learning (classification) purposes, the target attribute is: <strong>res_name</strong>.
DOI: 10.5281/zenodo.1040856
title: CheckMyBlob evaluation data set (CL)

description: A data set of ligands used to evaluate the CheckMyBlob method, described in the Kowiel et al. paper "<em>Automatic recognition of ligands in electron density by machine learning methods</em>". This data set repeats the setup used in the study of Carolan & Lamzin titled "<em>Automated identification of crystallographic ligands using sparse-density representations</em>". It consists of ligands from X-ray diffraction experiments with 1.0–2.5 Å resolution. Adjacent PDB ligands were not connected. Ligands were labeled according to the PDB naming convention. The data set was limited to the 82 ligand types listed by Carolan & Lamzin. The resulting data set consists of 121,360 examples with ligand counts ranging from 42,622 examples for SO4 to 16 for SPO (spheroidene). For machine learning (classification) purposes, the target attribute is: <strong>res_name</strong>.
DOI: 10.5281/zenodo.1040857
title: CheckMyBlob evaluation data set (CL)

description: A data set of ligands used to evaluate the CheckMyBlob method, described in the Kowiel et al. paper "<em>Automatic recognition of ligands in electron density by machine learning methods</em>". This data set repeats the setup used in the study of Carolan & Lamzin titled "<em>Automated identification of crystallographic ligands using sparse-density representations</em>". It consists of ligands from X-ray diffraction experiments with 1.0–2.5 Å resolution. Adjacent PDB ligands were not connected. Ligands were labeled according to the PDB naming convention. The data set was limited to the 82 ligand types listed by Carolan & Lamzin. The resulting data set consists of 121,360 examples with ligand counts ranging from 42,622 examples for SO4 to 16 for SPO (spheroidene). For machine learning (classification) purposes, the target attribute is: <strong>res_name</strong>.
DOI: 10.5281/zenodo.1040842
title: CheckMyBlob ligand data set (CMB)

description: Ligand data set prepared for the CheckMyBlob study, described in <em>"Automatic recognition of ligands in electron density by machine learning methods"</em> by Kowiel, M. <em>et al.</em> It contains only structures from X-ray diffraction experiments determined to at least 4.0 Å resolution. Entries with R factor above 0.3 or ligands below 0.3 occupancy (according to wwPDB validation reports) were rejected. Only ligands with at least 2 non-H atoms were considered and structures with low ligand map correlation coefficients (RSCC &lt; 0.6, RSZO &lt;= 1, RSZD &gt; 6.0) were removed. Apart from taking into account quality factors, we removed from the experimental data set all moieties that are not considered proper ligands. These included: unknown species, water molecules, standard amino acids, and selected nucleotides. Moreover, connected ligands (as per the naming convention in the PDB) were labeled as alphabetically ordered strings of hetero-compound codes (e.g., NAG-NAG-NAG-NAG). Finally, the data set was limited to 200 most popular ligands. The resulting data set consisted of 219,986 examples with individual ligand counts ranging from 48,490 examples for SO4 (sulfate ion) to 106 for A2G (n-acetyl-2-deoxy-2-amino-galactose). More details concerning data selection can be found in the paper of Kowiel <em>et al.</em> For machine learning (classification) purposes, the target attribute is: <strong>res_name</strong>.
DOI: 10.5281/zenodo.1040843
title: Checkmyblob Ligand Data Set (Cmb)

description: Ligand data set prepared for the CheckMyBlob study, described in <em>"Automatic recognition of ligands in electron density by machine learning methods"</em> by Kowiel, M. <em>et al.</em> It contains only structures from X-ray diffraction experiments determined to at least 4.0 Å resolution. Entries with R factor above 0.3 or ligands below 0.3 occupancy (according to wwPDB validation reports) were rejected. Only ligands with at least 2 non-H atoms were considered and structures with low ligand map correlation coefficients (RSCC &lt; 0.6, RSZO &lt;= 1, RSZD &gt; 6.0) were removed. Apart from taking into account quality factors, we removed from the experimental data set all moieties that are not considered proper ligands. These included: unknown species, water molecules, standard amino acids, and selected nucleotides. Moreover, connected ligands (as per the naming convention in the PDB) were labeled as alphabetically ordered strings of hetero-compound codes (e.g., NAG-NAG-NAG-NAG). Finally, the data set was limited to 200 most popular ligands. The resulting data set consisted of 219,986 examples with individual ligand counts ranging from 48,490 examples for SO4 (sulfate ion) to 106 for A2G (n-acetyl-2-deoxy-2-amino-galactose). More details concerning data selection can be found in the paper of Kowiel <em>et al.</em>

For machine learning (classification) purposes, the target attribute is: <strong>res_name</strong>.
DOI: 10.5281/zenodo.1191394
title: CheckMyBlob ligand data set (CMB)

description: Ligand data set prepared for the CheckMyBlob study, described in <em>"Automatic recognition of ligands in electron density by machine learning methods"</em> by Kowiel, M. <em>et al.</em> It contains only structures from X-ray diffraction experiments determined to at least 4.0 Å resolution. Entries with R factor above 0.3 or ligands below 0.3 occupancy (according to wwPDB validation reports) were rejected. Only ligands with at least 2 non-H atoms were considered and structures with low ligand map correlation coefficients (RSCC &lt; 0.6, RSZO &lt;= 1, RSZD &gt; 6.0) were removed. Apart from taking into account quality factors, we removed from the experimental data set all moieties that are not considered proper ligands. These included: unknown species, water molecules, standard amino acids, and selected nucleotides. Moreover, connected ligands (as per the naming convention in the PDB) were labeled as alphabetically ordered strings of hetero-compound codes (e.g., NAG-NAG-NAG-NAG). Finally, the data set was limited to 200 most popular ligands. The resulting data set consisted of 219,986 examples with individual ligand counts ranging from 48,490 examples for SO4 (sulfate ion) to 106 for A2G (n-acetyl-2-deoxy-2-amino-galactose). More details concerning data selection can be found in the paper of Kowiel <em>et al.</em> For machine learning (classification) purposes, the target attribute is: <strong>res_name</strong>.
DOI: 10.5281/zenodo.2605119
title: RefleX: X-ray diffraction images dataset

description: Image dataset prepared for the RefleX study, described in <em>"Detecting anomalies in X-ray diffraction images using Convolutional Neural Networks"</em><em>.</em> The dataset contains 6311 X-ray diffraction images in 1024x1024 png format (reflex_img_1024_inter_nearest.zip). The repository also contains a file mapping each image to a set of labels (labels.csv) and files describing the assignment of each image to training, validation, and testing sets (labels_train.csv, labels_val.csv, labels_test.csv). The dataset can be used for multi-label classification. Each diffraction image can exhibit any combination of seven classes: Ice ring, Diffuse Scattering, Background Ring, Non-uniform Detector, Loop Scattering, Strong Background, and Artifact.