cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e170401v1-142930
title: Code for: Reference Dependence in the Housing Market

description: We quantify reference dependence and loss aversion in the housing market using rich Danish administrative data. Our structural model includes loss aversion, reference dependence, financial constraints, and a sale decision, and matches key nonparametric moments, including a ``hockey stick" in listing prices with nominal gains, and bunching at zero realized nominal gains. Households derive substantial utility from gains over the original house purchase price; losses affect households roughly 2.5 times more than gains. The model helps explain the positive correlation between aggregate house prices and turnover, but cannot explain visible attenuation in reference dependence when households are more financially constrained.
DOI: 10.25824/redu/ppts91
title: Dados Validacao MNP e NDMA sem identificacao.tab

description: :unav
DOI: 10.25824/redu/90dsx0
title: A comprehensive LC-UHPLC-MS/MS method for the monitoring of N-nitrosamines in lipophilic drugs: a case study with rifampicin

description: Dados relacionados à publicação
DOI: 10.6084/m9.figshare.23925291.v10
title: Dataset Artificial warming decreases heterotroph densities and predator-prey biomass ratio in aquatic systems

description: This file holds the data and scripts used in the article "Artificial warming decreases heterotroph densities and predator-prey biomass ratio in aquatic systems".Data are available in files whose name begins with the prefix "df". The variables names "date" and "date_c" represent the sampling date in discrete or continue value, respectively. "mesocosm_ID" is the identity of the mesocosm. "fish" represent the fish treatment (NF for No Fish, LB for Large-bodied fish and SB for Small-bodied fish). "warming" is the temperature treatment (NW for no warming, W for warming). "tmean15" is the mean temperature of the 15 days prior to the sampling date. "compartment", "class", "order" and "taxa" represent the taxonomy of the organisms. "N_l" is the density per liter. "C_mug_l" is the carbon biomass in microgram per liter. "S" and "H" are the species richness and the Shannon diversity index, respectively. "ZP" is the zooplankton:phytoplankton biomass ratio. The variables in df_bss.csv file are described in the main text of the article.Files with a numerical prefix are the scripts used for the statistical analyzes (multivariate analyzes, ecological index, community density and biomass, Predator Prey Biomass Ratio and Biomass Size Spectra).
DOI: 10.6084/m9.figshare.23925291.v11
title: Dataset Artificial warming decreases heterotroph densities and predator-prey biomass ratio in aquatic systems

description: This file holds the data and scripts used in the article "Artificial warming decreases heterotroph densities and predator-prey biomass ratio in aquatic systems".Data are available in files whose name begins with the prefix "df". The variables names "date" and "date_c" represent the sampling date in discrete or continue value, respectively. "mesocosm_ID" is the identity of the mesocosm. "fish" represent the fish treatment (NF for No Fish, LB for Large-bodied fish and SB for Small-bodied fish). "warming" is the temperature treatment (NW for no warming, W for warming). "tmean15" is the mean temperature of the 15 days prior to the sampling date. "compartment", "class", "order" and "taxa" represent the taxonomy of the organisms. "N_l" is the density per liter. "C_mug_l" is the carbon biomass in microgram per liter. "S" and "H" are the species richness and the Shannon diversity index, respectively. "ZP" is the zooplankton:phytoplankton biomass ratio. The variables in df_bss.csv file are described in the main text of the article.Files with a numerical prefix are the scripts used for the statistical analyzes (multivariate analyzes, ecological index, community density and biomass, Predator Prey Biomass Ratio and Biomass Size Spectra).
DOI: 10.34740/kaggle/dsv/6926287
title: Indian Cities Distance Dataset
DOI: 10.6084/m9.figshare.24534261
title: R script for statistical analysis from Microbiome and photoperiod interactively determine thermal sensitivity of polar and temperate diatoms

description: R script to reproduce the statistical analysis. Requires the dataset “plateassay_growthrates.csv” as input.
DOI: 10.6084/m9.figshare.24534267
title: Growth rates data of <i>T. gravida</i> and <i>T. rotula</i> from Microbiome and photoperiod interactively determine thermal sensitivity of polar and temperate diatoms

description: Dataset of Growth rates of Thalassiosira gravida and Thalassiosira rotula across different levels of temperature (4°C; 9°C; 13.5°C), photoperiod (4h; 16h; 24h), and microbiome presence (xenic/axenic)
DOI: 10.6084/m9.figshare.24534267.v1
title: Growth rates data of <i>T. gravida</i> and <i>T. rotula</i> from Microbiome and photoperiod interactively determine thermal sensitivity of polar and temperate diatoms

description: Dataset of Growth rates of Thalassiosira gravida and Thalassiosira rotula across different levels of temperature (4°C; 9°C; 13.5°C), photoperiod (4h; 16h; 24h), and microbiome presence (xenic/axenic)
DOI: 10.6084/m9.figshare.24534270.v1
title: Read-me file for the growth rates dataset from Microbiome and photoperiod interactively determine thermal sensitivity of polar and temperate diatoms

description: Contains detailed description about the structure of this dataset and how it was processed.
DOI: 10.6084/m9.figshare.24534270
title: Read-me file for the growth rates dataset from Microbiome and photoperiod interactively determine thermal sensitivity of polar and temperate diatoms

description: Contains detailed description about the structure of this dataset and how it was processed.
DOI: 10.6084/m9.figshare.24534276
title: Read-me file for R script from Microbiome and photoperiod interactively determine thermal sensitivity of polar and temperate diatoms

description: Description file for the provided code to re-run the statistical analyses
DOI: 10.6084/m9.figshare.24534276.v1
title: Read-me file for R script from Microbiome and photoperiod interactively determine thermal sensitivity of polar and temperate diatoms

description: Description file for the provided code to re-run the statistical analyses
DOI: 10.25403/upresearchdata.21697388.v1
title: Sustainability of urban food gardening

description: For this dataset, data was collected through structured questionnaires which were sent to 80 respondents from currently operational food gardens in the Moletsane and Tladi areas in Soweto. This comprised of 20 cooperative participants, 20 group participants, and 40 individual backyard home gardeners. In addition, responses were obtained from fourteen key informants from nine key stakeholder organisations, comprising a municipality, four government departments, two universities, an NGO, and a farmers’ group in the area. A cross-sectional research design was employed to collect data from the 80 respondents, who were selected on a purposive basis, i.e. targeting those currently practising food gardening.
DOI: 10.25403/upresearchdata.21697388
title: Sustainability of urban food gardening

description: For this dataset, data was collected through structured questionnaires which were sent to 80 respondents from currently operational food gardens in the Moletsane and Tladi areas in Soweto. This comprised of 20 cooperative participants, 20 group participants, and 40 individual backyard home gardeners. In addition, responses were obtained from fourteen key informants from nine key stakeholder organisations, comprising a municipality, four government departments, two universities, an NGO, and a farmers’ group in the area. A cross-sectional research design was employed to collect data from the 80 respondents, who were selected on a purposive basis, i.e. targeting those currently practising food gardening.
DOI: 10.26050/wdcc/fesom14-recom2_b_1921_ca_cc
title: HighRes_highLat_SO FESOM1.4-REcoM2 model simulations to project future change in the coupled physical-biogeochemical system simB 1980_2100 constantAtmCO2 constantClimate

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 “simB 1980_2100” 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) are held constant throughout the simulation. Output is provided from 1980-2100. The years 1950-1979 are interpreted as spin-up and not provided here. 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: 10.25403/upresearchdata.24476977.v1
title: Analysis of 1500 women 25 years and older screened with 15 high-risk Human papillomavirus (hrHPV) genotyping test, and performance of various triage strategies of hrHPV positive women

description: This prospective clinical study evaluated various cervical cancer screening algorithms’ performance following a positive high-risk HPV (hrHPV) screening result. A total of 1500 women living with HIV (WLWH) and HIV-negative women in the ratio of 1:1 and 25 years and older, were enrolled into the study. All women underwent primary cervical cancer screening with the AmpFire Multiplex hrHPV DNA assay using self-collected swabs. Women testing positive for the15 hrHPV (HPV 16, 18, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68) and an additional 10% testing negative for hrHPV were called back for visual evaluation (VIA and colposcopy). The treatment decisions were based on colposcopy findings. Women with a visual lesion received treatment with loop electrosurgical excision procedure (LEEP), and those with no lesion or whose examination was inadequate received a biopsy or an endocervical curettage to ensure all women had a histological endpoint. The primary endpoint was cervical intraepithelial neoplasia grade 2 or worse (CIN 2+). Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) of hrHPV followed by triage with VIA, colposcopy, and three partial genotyping protocols (HPV 16/18; HPV 16/18/45; and HPV 16/18/31/33/35/45/52/58) were calculated as screening algorithms.
DOI: 10.25403/upresearchdata.24476977
title: Analysis of 1500 women 25 years and older screened with 15 high-risk Human papillomavirus (hrHPV) genotyping test, and performance of various triage strategies of hrHPV positive women

description: This prospective clinical study evaluated various cervical cancer screening algorithms’ performance following a positive high-risk HPV (hrHPV) screening result. A total of 1500 women living with HIV (WLWH) and HIV-negative women in the ratio of 1:1 and 25 years and older, were enrolled into the study. All women underwent primary cervical cancer screening with the AmpFire Multiplex hrHPV DNA assay using self-collected swabs. Women testing positive for the15 hrHPV (HPV 16, 18, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68) and an additional 10% testing negative for hrHPV were called back for visual evaluation (VIA and colposcopy). The treatment decisions were based on colposcopy findings. Women with a visual lesion received treatment with loop electrosurgical excision procedure (LEEP), and those with no lesion or whose examination was inadequate received a biopsy or an endocervical curettage to ensure all women had a histological endpoint. The primary endpoint was cervical intraepithelial neoplasia grade 2 or worse (CIN 2+). Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) of hrHPV followed by triage with VIA, colposcopy, and three partial genotyping protocols (HPV 16/18; HPV 16/18/45; and HPV 16/18/31/33/35/45/52/58) were calculated as screening algorithms.
DOI: 10.25829/e3cwb2
title: Historical local species richness (PREDICTS)
DOI: 10.25829/xe5m37
title: Tropical Andes macrogroup ecosystem extent
DOI: 10.25829/c8rz53
title: Tropical Andes formation ecosystem extent
DOI: 10.26050/wdcc/fesom14-recom2_c_hist_va_cc
title: HighRes_highLat_SO FESOM1.4-REcoM2 model simulations to project future change in the coupled physical-biogeochemical system simC historical variableAtmCO2 constantClimate

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 “simC historical” 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, atmospheric CO2 concentrations vary, but all other atmospheric forcing variables (e.g., air temperature, winds, humidity, precipitation) are held constant throughout the simulation. Output is provided from 1980-2014. The years 1950-1979 are interpreted as spin-up and not provided here. 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: 10.25829/vt7qk9
title: Global trends in biodiversity (BES-SIM PREDICTS)
DOI: 10.5281/zenodo.10091163
title: POPULARIZATION OF SPORTS AMONG THE YOUNG GENERATION IN UZBEKISTAN
DOI: 10.5281/zenodo.10091164
title: POPULARIZATION OF SPORTS AMONG THE YOUNG GENERATION IN UZBEKISTAN
DOI: 10.25829/5zmy41
title: Global trends in biodiversity (BES-SIM cSAR-iDiv)
DOI: 10.5281/zenodo.10090824
title: Case Study Scenarios (CSS) 3 for Biomass Burning for Limb Nadir application
DOI: 10.5281/zenodo.10090825
title: Case Study Scenarios (CSS) 3 for Biomass Burning for Limb Nadir application
DOI: 10.5281/zenodo.10090755
title: Case Study Scenarios (CSS) 1 for OSSE of tropopause fold

description: These are the simulated CAIRT ozone profiles for the period 15/Nov/2021-1/Feb/2022 using, in the following order:

CAMS hlqd control run simulation as reference ozone field
sim_CAIRT_grid.py to generate CAIRT retrieval grid (i.e. the geolocation of the CAIRT profiles)
BASCOE_MAO to interpolate CAMS hlqd 3-hourly output at CAIRT geolocation
FL2S to perturb CAMS@CAIRT ozone according to CAIRT expected performences (using the AK, the systematic and the random error) and add error bar to the ozone profiles.
These are the ozone profiles assimilated by BASCOE for the Observing System Simulation Experiment (OSSE) discussed in CAIRT Report for Assessment (RfA).
DOI: 10.5281/zenodo.10091174
title: IMPORTANCE OF PHYSICAL QUALITIES IN TRAINING YOUNG VOLLEYBALL PLAYERS.
DOI: 10.5281/zenodo.10090756
title: Case Study Scenarios (CSS) 1 for OSSE of tropopause fold

description: These are the simulated CAIRT ozone profiles for the period 15/Nov/2021-1/Feb/2022 using, in the following order:

CAMS hlqd control run simulation as reference ozone field
sim_CAIRT_grid.py to generate CAIRT retrieval grid (i.e. the geolocation of the CAIRT profiles)
BASCOE_MAO to interpolate CAMS hlqd 3-hourly output at CAIRT geolocation
FL2S to perturb CAMS@CAIRT ozone according to CAIRT expected performences (using the AK, the systematic and the random error) and add error bar to the ozone profiles.
These are the ozone profiles assimilated by BASCOE for the Observing System Simulation Experiment (OSSE) discussed in CAIRT Report for Assessment (RfA).
DOI: 10.5281/zenodo.10091175
title: IMPORTANCE OF PHYSICAL QUALITIES IN TRAINING YOUNG VOLLEYBALL PLAYERS.
DOI: 10.5281/zenodo.10091184
title: LACUNAES  AND NON-EQUIVALENT VOCABULARY AS A REFLECTION OF LINGUISTIC CULTURE
DOI: 10.5281/zenodo.10091185
title: LACUNAES  AND NON-EQUIVALENT VOCABULARY AS A REFLECTION OF LINGUISTIC CULTURE
DOI: 10.5281/zenodo.10091010
title: 3D cell tracking dataset of bacterial biofilm deformation and recovery under shear flow

description: This MAT file includes dataset in the scientific article "In vivo microrheology reveals elastic and plastic responses inside three-dimensional bacterial biofilms" by the following authors: Takuya Ohmura, Dominic Skinner, Konstantin Neuhaus, Gary Choi, Jörn Dunkel, Knut Drescher. This MAT file can be conveniently opened with Matlab. 
When you open this file with Matlab, you will find 4 variables stored in the file "Data_v3_loop2_newRxy_bidx1_274.mat"
Variable 1: name_parameter
Names of 31 parameters for columns in 3 variables: 'deformation_all', 'recovery_all' , 'plasticity_all'. The parameters have cell displacements, orientations, coordinates, biofilm indexes and experimental conditions. When the parameters have units, they are shown in the names. 

'x_Frame1[um]'
'y_Frame1[um]'
'z_Frame1[um]'
'Normalized_x_Frame1'
'Normalized_y_Frame1'
'Normalized_z_Frame1'
'LocalDensity_Frame1(VolumeFractionAround30px)'
'LocalCellNumberDensity_Frame1(VolumeFractionAround30px)'
'NematicOrderParameter_Frame1'
'AlignmentFlow_Frame1[rad]'
'AlignmentRadial_Frame1[rad]'
'AlignmentZaxis_Frame1[rad]'
'd_x[um]'
'd_y[um]'
'd_z[um]'
'Normalized_d_x'
'Normalized_d_y'
'Normalized_d_z'
'd_LocalDensity'
'd_LocalNumberDensity[um^-3]'
'd_NematicOrderParameter'
'd_AlignmentFlow[rad]'
'd_AlignmentRadial[rad]'
'd_AlignmentZaxis[rad]'
'CrossProduct[um^2]'
'BiofilmIndexNumber'
'Biofilm_width[um]'
'Biofilm_height[um]'
'Biofilm_volume[um^3]'
'FlowRate[ul/min]'
'Duration[min]'
Variable 2: deformation_all
The rows indicate 704198 single-cell trackings in deformations of 274 bacterial biofilms. Each of the 274 bacterial biofilms has a different 'BiofilmIndexNumber'. The columns indicate 31 parameters which names are shown in 'name_parameter'.
Variable 3: recovery_all
The rows indicate 685991 single-cell trackings in recoveries of 274 bacterial biofilms. Each of the 274 bacterial biofilms has a different 'BiofilmIndexNumber'. The columns indicate 31 parameters which names are shown in 'name_parameter'.
Variable 4: plasticity_all
The rows indicate 665749 single-cell trackings in plasticities of 274 bacterial biofilms. Each of the 274 bacterial biofilms has a different 'BiofilmIndexNumber'. The columns indicate 31 parameters which names are shown in 'name_parameter'.
 
To plot the cell tracked data in the figures of the article, use our MATLAB code uploaded in our GitHub (https://github.com/knutdrescher/biofilm-rheology).