cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24533122
title: DeepRMSF-main

description: IntroductionDeepRMSF is an automated deep-learning based approach for ‘imaging’ the dynamics of RNA at atomic resolution. Starting with a given PDB-formatted structure, DeepRMSF, it is first translated to density map, structure feature in density map is translated by structure. The maps are segmented into a series of density boxes, which served as input for the model. Finally, predicted RMSF subboxes were then merged into an RMSF map as the RMSF prediction map for this RNA. Source code for DeepRMSF: an automated computational method for RNA dynamics modeling by deep learningFilesrna_util.pyThis file undergoes the most basic data processing, such as generating simulated maps.rna_to_input.pyThis file divides the maps into boxes as input to the model.model_util.pyFunctions required for model training.rmsf_model.pyDeepRMSF model.rna_five_fold.pyWe train and test model using 5-fold cross-validation.main.pyUsageDownload the code file and run <b>main.py</b>. python main.py [-h] [--ori_dir ORI_DIR] [--data_dir DATA_DIR] [--box_file BOX_FILE] [--log_dir LOG_DIR]<br>You can enter the following parameters,--ori_dirThe folder for saving PDBs of RNAs.--data_dirThe folder where you want to save the simulated maps.--box_fileThe folder where you want to save the box_files.--log_dirThe folder where you want to save predicted data.-h or --helpYou can consult the help.InputPDBs of RNAs.OutputPDBs with predictive normalized RMSF values which replace B-factor values. The file names are "{pdbid}_pre_nor.pdb". These PDBs can be visualized by Chimera and so on.ExampleYou can view the <b>test</b> folder to learn about the DeepRMSF prediction process.Supporting softwaresx3DNA-DSSRTo obtain secondary structure.UCSF ChimeraSimulated maps were obtained with UCSF Chimera, developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco, with support from NIH P41-GM103311.DeepRMSF<br>
DOI: 10.6084/m9.figshare.24533122.v2
title: DeepRMSF-main

description: IntroductionDeepRMSF is an automated deep-learning based approach for ‘imaging’ the dynamics of RNA at atomic resolution. Starting with a given PDB-formatted structure, DeepRMSF, it is first translated to density map, structure feature in density map is translated by structure. The maps are segmented into a series of density boxes, which served as input for the model. Finally, predicted RMSF subboxes were then merged into an RMSF map as the RMSF prediction map for this RNA. Source code for DeepRMSF: an automated computational method for RNA dynamics modeling by deep learningFilesrna_util.pyThis file undergoes the most basic data processing, such as generating simulated maps.rna_to_input.pyThis file divides the maps into boxes as input to the model.model_util.pyFunctions required for model training.rmsf_model.pyDeepRMSF model.rna_five_fold.pyWe train and test model using 5-fold cross-validation.main.pyUsageDownload the code file and run <b>main.py</b>. python main.py [-h] [--ori_dir ORI_DIR] [--data_dir DATA_DIR] [--box_file BOX_FILE] [--log_dir LOG_DIR]<br>You can enter the following parameters,--ori_dirThe folder for saving PDBs of RNAs.--data_dirThe folder where you want to save the simulated maps.--box_fileThe folder where you want to save the box_files.--log_dirThe folder where you want to save predicted data.-h or --helpYou can consult the help.InputPDBs of RNAs.OutputPDBs with predictive normalized RMSF values which replace B-factor values. The file names are "{pdbid}_pre_nor.pdb". These PDBs can be visualized by Chimera and so on.ExampleYou can view the <b>test</b> folder to learn about the DeepRMSF prediction process.Supporting softwaresx3DNA-DSSRTo obtain secondary structure.UCSF ChimeraSimulated maps were obtained with UCSF Chimera, developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco, with support from NIH P41-GM103311.DeepRMSF<br>
DOI: 10.6084/m9.figshare.24533122.v1
title: data.zip

description: IntroductionDeepRMSF is an automated deep-learning based approach for ‘imaging’ the dynamics of RNA at atomic resolution. Starting with a given PDB-formatted structure, DeepRMSF, it is first translated to density map, structure feature in density map is translated by structure. The maps are segmented into a series of density boxes, which served as input for the model. Finally, predicted RMSF subboxes were then merged into an RMSF map as the RMSF prediction map for this RNA. Source code for DeepRMSF: an automated computational method for RNA dynamics modeling by deep learningFilesrna_util.pyThis file undergoes the most basic data processing, such as generating simulated maps.rna_to_input.pyThis file divides the maps into boxes as input to the model.model_util.pyFunctions required for model training.rmsf_model.pyDeepRMSF model.rna_five_fold.pyWe train and test model using 5-fold cross-validation.main.pyUsageDownload the code file and run <b>main.py</b>. python main.py [-h] [--ori_dir ORI_DIR] [--data_dir DATA_DIR] [--box_file BOX_FILE] [--log_dir LOG_DIR]<br>You can enter the following parameters,--ori_dirThe folder for saving PDBs of RNAs.--data_dirThe folder where you want to save the simulated maps.--box_fileThe folder where you want to save the box_files.--log_dirThe folder where you want to save predicted data.-h or --helpYou can consult the help.InputPDBs of RNAs.OutputPDBs with predictive normalized RMSF values which replace B-factor values. The file names are "{pdbid}_pre_nor.pdb". These PDBs can be visualized by Chimera and so on.ExampleYou can view the <b>test</b> folder to learn about the DeepRMSF prediction process.Supporting softwaresx3DNA-DSSRTo obtain secondary structure.UCSF ChimeraSimulated maps were obtained with UCSF Chimera, developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco, with support from NIH P41-GM103311.DeepRMSF<br>
DOI: 10.5281/zenodo.10090695
title: Dataset of 3D-complexes of SARS-CoV-2:Human proteins

description: The dataset of human: SARS-CoV-2 protein complexes used for the study.
DOI: 10.5281/zenodo.10090696
title: Dataset of 3D-complexes of SARS-CoV-2:Human proteins

description: The dataset of human: SARS-CoV-2 protein complexes used for the study.
DOI: 10.5281/zenodo.10091025
title: Evaluation of different grow-out systems for oysters in Sweden

description: Aim: Evaluate different grow-out systems for oysters (Ostrea edulis). The data describes the evaluation of two systems; the commercial Aquapurse cages and a newly designed cylindrical metal cage system, for production of oysters in Sweden.


Data includes growth and survival of oysters in different systems, measurements of fouling load as well as handling/robustness of the grow-out systems. Data originates from the Swedish west coast.
DOI: 10.5281/zenodo.8429901
title: Evaluation of different grow-out systems for oysters in Sweden

description: Aim: Evaluate different grow-out systems for oysters (Ostrea edulis). The data describes the evaluation of two systems; the commercial Aquapurse cages and a newly designed cylindrical metal cage system, for production of oysters in Sweden.


Data includes growth and survival of oysters in different systems, measurements of fouling load as well as handling/robustness of the grow-out systems. Data originates from the Swedish west coast.
DOI: 10.5281/zenodo.8429902
title: Evaluation of different grow-out systems for oysters in Sweden

description: Aim: Evaluate different grow-out systems for oysters (<em>Ostrea edulis</em>). The data describes the evaluation of two systems; the commercial Aquapurse cages and a newly designed cylindrical metal cage system, for production of oysters in Sweden. Data includes growth and survival of oysters in different systems, measurements of fouling load as well as handling/robustness of the grow-out systems. Data originates from the Swedish west coast.
DOI: 10.5281/zenodo.10090890
title: Lifestyle habits of adolescents before pandemic and one-year later

description: The database includes variables related to lifestyle habits (i.e. free-time activities, maladaptive behaviors, sleep quality, screen use) and anxiety of a sample of students (N=153, 16 years mean age, 72% female) who were assessed before pandemic (T0) and one year later (T1).
DOI: 10.6084/m9.figshare.24533989
title: Data from Benefits of helping in PCFW Open Science from Best of both worlds? Helpers in a cooperative fairy-wren assist most to breeding pairs comprised a potential mate and a relative

description: Dataset supporting article.
DOI: 10.6084/m9.figshare.24533989.v1
title: Data from Benefits of helping in PCFW Open Science from Best of both worlds? Helpers in a cooperative fairy-wren assist most to breeding pairs comprised a potential mate and a relative

description: Dataset supporting article.
DOI: 10.6084/m9.figshare.24533992
title: R code from Benefits of helping in PCFW Open Science from Best of both worlds? Helpers in a cooperative fairy-wren assist most to breeding pairs comprised a potential mate and a relative

description: Code used for statistical analyses.
DOI: 10.6084/m9.figshare.24533992.v1
title: R code from Benefits of helping in PCFW Open Science from Best of both worlds? Helpers in a cooperative fairy-wren assist most to breeding pairs comprised a potential mate and a relative

description: Code used for statistical analyses.
DOI: 10.6084/m9.figshare.24533824
title: Supplemental Table 5-6

description: Supplemental Table 5–The fasting insulin levels in women with and without gestational hypertension.Supplemental Table 6–Effect of AMH on gestational hypertension in insulin subgroup.
DOI: 10.6084/m9.figshare.24533824.v1
title: Effect of AMH on gestational hypertension in insulin subgroup

description: Supplemental Table 5–The fasting insulin levels in women with and without gestational hypertension.Supplemental Table 6–Effect of AMH on gestational hypertension in insulin subgroup.
DOI: 10.6084/m9.figshare.24533824.v2
title: Supplemental Table 5-6

description: Supplemental Table 5–The fasting insulin levels in women with and without gestational hypertension.Supplemental Table 6–Effect of AMH on gestational hypertension in insulin subgroup.
DOI: 10.5258/soton/d2850
title: Dataset in support of the article 'Enabling Image Net-scale deep learning on MCUs for accurate and efficient inference'

description: Data obtained in Enabling Image Net-Scale Deep Learning on MCUs for Accurate and Efficient Inference research. To support an article published in IEEE Internet of Things Journal
DOI: 10.26050/wdcc/fesom14-recom2_a_s126_va_vc
title: HighRes_highLat_SO FESOM1.4-REcoM2 model simulations to project future change in the coupled physical-biogeochemical system simA SSP126 variableAtmCO2 variableClimate

description: Project: Global simulations of ocean-sea ice-biogeochemistry model FESOM1.4-REcoM2 with ice-shelf cavities and eddy-permitting grid resolution in the high-latitude Southern Ocean - This project includes global model simulations with the global multi-resolution Finite Element Sea Ice-Ocean Model (FESOM version 1.4) coupled to the Regulated Ecosystem Model (REcoM version 2). For this project, model simulations include a representation of ice-shelf cavities and were run from 1950-2100 on a model grid with eddy-permitting resolution on the Antarctic continental shelves. The ocean-only model simulations were forced at the ocean surface with 3-hourly atmospheric output from the AWI Climate Model. The project includes model experiments under four “Shared Socioeconomic Pathways” emission scenarios and sensitivity experiments facilitating the separation of natural and anthropogenic components of the oceanic carbon cycle. The provided model simulations were initially run to assess the potential over the 21st century for the crossing of tipping points in Antarctic Bottom Water formation and the associated carbon sequestration in the Weddell Sea. Since then, these simulations have been used for a broader assessment of potential future trajectories of the coupled physical-biogeochemical system in the high-latitude Southern Ocean. Computing resources were provided by the North-German Supercomputing Alliance (HLRN) project hbk00079, and the work has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 820989 (project COMFORT; https://comfort.w.uib.no/). COMFORT aims to assess tipping points in the coupled cycles of carbon, oxygen, and nutrients in the ocean for determining and achieving safe operating spaces. Summary: This dataset includes all variables of the model experiment “simA ssp126” conducted with the global ocean-sea ice-biogeochemistry model FESOM1.4-REcoM2 with ice-shelf cavities and eddy-permitting resolution on Antarctic shelves. For this experiment, both atmospheric CO2 concentrations and all other atmospheric forcing variables (e.g., air temperature, winds, humidity, precipitation) vary throughout the simulation. Output is provided from 2015-2100. The output is sorted by output frequency (monthly or daily) and model component (FESOM or REcoM). The data is sorted as follows: - annual_FESOM - annual_REcoM - monthly_FESOM - monthly_REcoM Filname convention: Variable_outputFreq_FESOM1.4-REcoM2_experimentName_experimentTime_year.nc Computing resources were provided by the North-German Supercomputing Alliance (HLRN) project hbk00079.
DOI: 10.6084/m9.figshare.24533815
title: Supplemental Table 1

description: Supplemental Table 1 - Collinearity test before multivariable logistic regression analysisUntitled Item
DOI: 10.6084/m9.figshare.24533815.v1
title: Collinearity test before multivariable logistic regression analysisUntitled Item

description: Collinearity test before multivariable logistic regression analysisUntitled Item
DOI: 10.5281/zenodo.10091107
title: Dataset for Citizen Science as a Relevant Approach to the Challenges of Complex Thinking Development in Higher Education

description: Dataset with questions and analysis for the article Citizen Science as a Relevant Approach to the Challenges of Complex Thinking Development in Higher Education: Mapping and Bibliometric Analysis
DOI: 10.5281/zenodo.10091106
title: Dataset for Citizen Science as a Relevant Approach to the Challenges of Complex Thinking Development in Higher Education

description: Dataset with questions and analysis for the article Citizen Science as a Relevant Approach to the Challenges of Complex Thinking Development in Higher Education: Mapping and Bibliometric Analysis
DOI: 10.6084/m9.figshare.24526681
title: Supplemental Table 2-4

description: We analyzed 3651 non-PCOS patients, and the clinical characteristics of the two groups are shown in <b>Supplemental Table 2</b>. There were statistically significant differences in BMI, gestational week, and total Gn dosage (P&lt;0.05), but no statistically significant differences in serum AMH (P&gt;0.05). Therefore, we separately performed PSM on age and age +BMI+total Gn dosage to analyse the relationship between serum AMH and gestational hypertension. In PSM, the clamp value was set at 0.2, and the ratio of patients with and without gestational hypertension was 1:4.First, the clinical characteristics of the two groups after age matching is shown in <b>Supplemental </b><b>Table 3</b>. Of these patients, 86 people were in the gestational hypertension group and 3,565 people were in the without-gestational hypertension group. Second, the clinical characteristics of the two groups after matching for age, BMI, and total Gn dose are shown in <b>Supplemental </b><b>Table 4</b>, in which 85 people were in the gestational hypertension group and 334 people were in the without-gestational hypertension group.<br>
DOI: 10.6084/m9.figshare.24526681.v2
title: Supplemental Table 2-4

description: We analyzed 3651 non-PCOS patients, and the clinical characteristics of the two groups are shown in <b>Supplemental Table 2</b>. There were statistically significant differences in BMI, gestational week, and total Gn dosage (P&lt;0.05), but no statistically significant differences in serum AMH (P&gt;0.05). Therefore, we separately performed PSM on age and age +BMI+total Gn dosage to analyse the relationship between serum AMH and gestational hypertension. In PSM, the clamp value was set at 0.2, and the ratio of patients with and without gestational hypertension was 1:4.First, the clinical characteristics of the two groups after age matching is shown in <b>Supplemental </b><b>Table 3</b>. Of these patients, 86 people were in the gestational hypertension group and 3,565 people were in the without-gestational hypertension group. Second, the clinical characteristics of the two groups after matching for age, BMI, and total Gn dose are shown in <b>Supplemental </b><b>Table 4</b>, in which 85 people were in the gestational hypertension group and 334 people were in the without-gestational hypertension group.<br>
DOI: 10.6084/m9.figshare.24526681.v1
title: The analysis of AMH and gestational hypertension in women without PCOS

description: We analyzed 3651 non-PCOS patients, and the clinical characteristics of the two groups are shown in <b>Supplemental Table 2</b>. There were statistically significant differences in BMI, gestational week, and total Gn dosage (P&lt;0.05), but no statistically significant differences in serum AMH (P&gt;0.05). Therefore, we separately performed PSM on age and age +BMI+total Gn dosage to analyse the relationship between serum AMH and gestational hypertension. In PSM, the clamp value was set at 0.2, and the ratio of patients with and without gestational hypertension was 1:4.First, the clinical characteristics of the two groups after age matching is shown in <b>Supplemental </b><b>Table 3</b>. Of these patients, 86 people were in the gestational hypertension group and 3,565 people were in the without-gestational hypertension group. Second, the clinical characteristics of the two groups after matching for age, BMI, and total Gn dose are shown in <b>Supplemental </b><b>Table 4</b>, in which 85 people were in the gestational hypertension group and 334 people were in the without-gestational hypertension group.<br>
DOI: 10.34740/kaggle/dsv/6929943
title: FARSI CAR LICENSE PLATE
DOI: 10.4121/4c91188a-ccdd-418e-a570-75497113096e
title: Dataset supporting the paper "Measuring and Supporting Simultaneous Flow in Duos of Elderly Cyclists"

description: This study investigates how simultaneous flow during cycling experiences can be measured and supported among duos of elderly cyclists, a demographic with increasing e-bike usage. We collected heart rate, cadence, and position data together with participant-generated labels in an experimental study with 21 participants. Data was collected via sensors on the bicycles and on the human participants, as well as via a forward-facing action camera on the bicycle handlebar. Via a post-ride video-based recall method, participants generated labels for each 30-second time window, indicating whether or not they where in individual flow in that time window. The sensor data and participant labels are shared in CSV data. The video recordings are not shared because anonimization was out of scope for the study, however the research team can be contacted in case of interest in these recordings.
DOI: 10.26050/wdcc/fesom14-recom2_a_s245_va_vc
title: HighRes_highLat_SO FESOM1.4-REcoM2 model simulations to project future change in the coupled physical-biogeochemical system simA SSP245 variableAtmCO2 variableClimate

description: Project: Global simulations of ocean-sea ice-biogeochemistry model FESOM1.4-REcoM2 with ice-shelf cavities and eddy-permitting grid resolution in the high-latitude Southern Ocean - This project includes global model simulations with the global multi-resolution Finite Element Sea Ice-Ocean Model (FESOM version 1.4) coupled to the Regulated Ecosystem Model (REcoM version 2). For this project, model simulations include a representation of ice-shelf cavities and were run from 1950-2100 on a model grid with eddy-permitting resolution on the Antarctic continental shelves. The ocean-only model simulations were forced at the ocean surface with 3-hourly atmospheric output from the AWI Climate Model. The project includes model experiments under four “Shared Socioeconomic Pathways” emission scenarios and sensitivity experiments facilitating the separation of natural and anthropogenic components of the oceanic carbon cycle. The provided model simulations were initially run to assess the potential over the 21st century for the crossing of tipping points in Antarctic Bottom Water formation and the associated carbon sequestration in the Weddell Sea. Since then, these simulations have been used for a broader assessment of potential future trajectories of the coupled physical-biogeochemical system in the high-latitude Southern Ocean. Computing resources were provided by the North-German Supercomputing Alliance (HLRN) project hbk00079, and the work has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 820989 (project COMFORT; https://comfort.w.uib.no/). COMFORT aims to assess tipping points in the coupled cycles of carbon, oxygen, and nutrients in the ocean for determining and achieving safe operating spaces. Summary: This dataset includes all variables of the model experiment “simA ssp245” conducted with the global ocean-sea ice-biogeochemistry model FESOM1.4-REcoM2 with ice-shelf cavities and eddy-permitting resolution on Antarctic shelves. For this experiment, both atmospheric CO2 concentrations and all other atmospheric forcing variables (e.g., air temperature, winds, humidity, precipitation) vary throughout the simulation. Output is provided from 2015-2100. The output is sorted by output frequency (monthly or daily) and model component (FESOM or REcoM). The data is sorted as follows: - annual_FESOM - annual_REcoM - monthly_FESOM - monthly_REcoM Filname convention: Variable_outputFreq_FESOM1.4-REcoM2_experimentName_experimentTime_year.nc Computing resources were provided by the North-German Supercomputing Alliance (HLRN) project hbk00079.
DOI: 10.4121/4c91188a-ccdd-418e-a570-75497113096e.v1
title: Dataset supporting the paper "Measuring and Supporting Simultaneous Flow in Duos of Elderly Cyclists"

description: This study investigates how simultaneous flow during cycling experiences can be measured and supported among duos of elderly cyclists, a demographic with increasing e-bike usage. We collected heart rate, cadence, and position data together with participant-generated labels in an experimental study with 21 participants. Data was collected via sensors on the bicycles and on the human participants, as well as via a forward-facing action camera on the bicycle handlebar. Via a post-ride video-based recall method, participants generated labels for each 30-second time window, indicating whether or not they where in individual flow in that time window. The sensor data and participant labels are shared in CSV data. The video recordings are not shared because anonimization was out of scope for the study, however the research team can be contacted in case of interest in these recordings.
DOI: 10.34740/kaggle/dsv/6929965
title: COVID Vaccination in World (updated daily)
DOI: 10.6084/m9.figshare.24534104
title: Cognitive task data from Repeatability and heritability of inhibitory control performance in wild toutouwai (<i>Petroica longipes</i>)

description: Raw data from detour, associative learning and reversal learning tasks.
DOI: 10.6084/m9.figshare.24534104.v1
title: Cognitive task data from Repeatability and heritability of inhibitory control performance in wild toutouwai (<i>Petroica longipes</i>)

description: Raw data from detour, associative learning and reversal learning tasks.
DOI: 10.6084/m9.figshare.24534107
title: R script from Repeatability and heritability of inhibitory control performance in wild toutouwai (<i>Petroica longipes</i>)

description: R script containing all code for analyses.