Find research datasets worth reusing

opencc-by-4.0Mar 2023View details →

metaGOflow: a workflow for the analysis of marine Genomic Observatories shotgun metagenomics data - use case

Data products returned by <a href="https://github.com/emo-bon/MetaGOflow">metaGOflow</a> (<a href="https://github.com/emo-bon/MetaGOflow/releases/tag/v1.0.0">v1.0.0</a>) and packed as a Research Object (RO) Crate, when performed with: <ul> <li>a seawater metagenomic sample (TARA OCEAN, <a href="https://www.ebi.ac.uk/ena/browser/view/ERR599171">ERR599171</a>)</li> <li>a fish gut sample (<a href="https://www.ebi.ac.uk/ena/browser/view/ERR4765907">ERR4765907</a>)</li> <li>a human gut sample (<a href="https://www.ebi.ac.uk/ena/browser/view/SRR9654976">SRR9654976</a>)</li> </ul> This Zenodo repo accompanies the metaGOflow paper and more about the analysis of this sample can be found there. You can also have a look at some visual components of the workflow at this <a href="https://data.emobon.embrc.eu/MetaGOflow/">GitHub page</a>.  The source code of metaGOflow is available through <a href="http://github.com/emo-bon/MetaGOflow">GitHub</a>.

PhageHostLearn - training data and cluster analysis

These data comprise the processed phage RBP and Klebsiella K-loci sequence data to train our PhageHostLearn system, along with ESM-2 embeddings of the RBPs and loci, as well as results from the cluster analyses of K-loci proteins and RBPs at 50% identity with CD-HIT.

Potential Predictability of the Spring Bloom in the Southern Ocean Sea Ice Zone: data and analysis scripts

This repository contains the datasets and notebooks necessary to reproduce the figures in Buchovecky et al. "Potential Predictability of the Spring Bloom in the Southern Ocean Sea Ice Zone". All notebooks, except those deriving quantities from the raw model data, should work "out-of-the-box" after the appropriate local path has been set to the data.

Data from: Kúr et al. (2023) Cryptic invasion suggested by cytogeographic analysis of the halophytic Puccinellia distans complex (Poaceae) in Central Europe

A collection of flow cytometry standard (FCS) files generated during the cytogeographic analysis of the halophytic Puccinellia distans complex (Poaceae) in Central Europe. The data was generated as part of the research published in Kúr et al. (2023) Cryptic invasion suggested by cytogeographic analysis of the halophytic Puccinellia distans complex (Poaceae) in Central Europe [in prep.]

Analysis of gaps in rapeseed (Brassica napus L.) collections in European genebanks - supplementary data

Species distribution modelling (or ecological niche modelling) was used to predict the effects of climate change on the future distribution of the wild relatives of Brassica napus L. in Europe and countries bordering the Mediterranean Sea. Modelling procedures followed the methods described by Aguirre-Gutierrez et al. 2017 (10.1111/ddi.12573) and van Treuren et al. 2017+2020 (10.1016/j.biocon.2017.10.003; 10.1016/j.gecco.2020.e01054).

Raw data for analysis of archaeological macrolithic tools

The file includes data describing typology and morphometric and raw material characteristics of archaeological collections composed of macrolithic stone tools with a cutting edge. The finds are dated to the Neolithic period in Poland. The file also includes location data of the archaeological sites attributed to the Mesolithic, Neolithic and Early Iron Age in SW Poland. The collections are held at the University of Wrocław and the Polish Academy of Science. The collected data was used to study the biography and circulation of metabasite-made tools based on usewear method.

opencc-by-4.0Jul 2023View details →

Data from: Business and publication model of surgical journals: A bibliometric analysis

The dataset contains information about surgical journals included in the SCOPUS database used in our research. The following information is present in the data file: <ol> <li>Title</li> <li>Journal sub-specialty</li> <li>Country Origin</li> <li>Continent</li> <li>SJR</li> <li>Publisher</li> <li>Types of Publisher</li> <li>Publication Model</li> <li>Language</li> </ol>

opencc-by-3.0-usJul 2023View details →

CePNEM model analysis data and ANTSUN and microscopy neural network weights

Citation and publication To cite this work or access the publication, please use the citation information listed here: <a href="https://github.com/flavell-lab/AtanasKim-Cell2023/tree/main#citation">https://github.com/flavell-lab/AtanasKim-Cell2023/tree/main#citation</a>   Initially published as preprint in: Brain-wide representations of behavior spanning multiple timescales and states in C. elegans Adam A. Atanas*, Jungsoo Kim*, Ziyu Wang, Eric Bueno, McCoy Becker, Di Kang, Jungyeon Park, Cassi Estrem, Talya S. Kramer, Saba Baskoylu, Vikash K. Mansingkha, Steven W. Flavell bioRxiv 2022.11.11.516186; doi: <a href="https://doi.org/10.1101/2022.11.11.516186">https://doi.org/10.1101/2022.11.11.516186</a> * Equal Contribution   Contents 1. deepnet-weights.tar.bz2 contains the trained weights of the neural networks used in this project. 3dunet_540nm_voxels: 3D U-Net for segmenting neurons head_detector_unet: finding worm head landmark used in ANTSUN registration head_detector_unet_0622: an alternative version of the above, optimal for NeuroPAL datasets microscope_tracker: detecting keypoints for online tracking on the microscope behavior_nir: segmentation of the recorded NIR behavior images for behavior quantification 2. data files ANTSUN processed datasets and CePNEM processed model fits and analysis data. Check the project packages and notebooks in the project github repository (<a href="https://github.com/flavell-lab/AtanasKim-Cell2023/">https://github.com/flavell-lab/AtanasKim-Cell2023/</a>) on using these datasets.

opencc-by-4.0Jul 2023View details →

Data and scripts for the analysis of the influence of crop pollinator dependence and growth form on yield decline

Marcelo A. Aizen, Gabriela R. Gleiser, Thomas Kitzberger, Ruben Milla. Being a tree crop increases the odds of experiencing yield declines irrespective of pollinator dependence (to be submitted to PCI)   Data and R scripts to reproduce the analyses and the figures shown in the paper. All analyses were performed using R 4.0.2.   Data 1. FAOdata_21-12-2021.csv This file includes yearly data (1961-2020, column 8) on yield and cultivated area (columns 6 and 10) at the country, sub-regional, and regional levels (column 2) for each crop (column 4) drawn from the United Nations Food and Agriculture Organization database (data available at <a href="http://www.fao.org/faostat/en">http://www.fao.org/faostat/en</a>; accessed July 21-12-2021).  [Used in Script 1 to generate the synthesis dataset] 2. countries.csv This file provides information on the region (column 2) to which each country (column 1) belongs.  [Used in Script 1 to generate the synthesis dataset] 3. dependence.csv This file provides information on the pollinator dependence category (column 2) of each crop (column 1). 4. traits.csv This file provides information on the traits of each crop other than pollinator dependence, including, besides the crop name (column1), the variables type of harvested organ (column 5) and growth form (column 6). [Used in Script 1 to generate the synthesis dataset] 5. dataset.csv The synthesis dataset generated by Script 1. 6. growth.csv The yield growth dataset generated by Script 1 and used as input by Scripts 2 and 3. 7. phylonames.csv This file lists all the crops (column 1) and their equivalent tip names in the crop phylogeny (column 2). [Used in Script 2 for the phylogenetically-controlled analyses] 8.phylo137.tre File containing the phylogenetic tree.   Scripts 1. dataset This R script curates and merges all the individual datasets mentioned above into a single dataset, estimating and adding to this single dataset the growth rate for each crop and country, and the (log) cumulative harvested area per crop and country over the period 1961-2020. 2. analyses This R script includes all the analyses described in the article’s main text. 3. figures This R script creates all the main and supplementary figures of this article. 4. lme4_phylo_setup R function written by Li and Bolker (2019) to carry out phylogenetically-controlled generalized linear mixed-effects models as described in the main text of the article.   References Li, M., and B. Bolker. 2019. wzmli/phyloglmm: First release of phylogenetic comparative analysis in lme4- verse. Zenodo. https://doi.org/10.5281/zenodo.2639887.

opencc-by-4.0Jul 2023View details →

Data, scripts and simulations for ProxyOH-[OH] analysis using ATom data and F0AM and AM3 simulations

This dataset provides the simulations and analysis code used in Baublitz et al., An observation-based, reduced-form model for oxidation in the remote marine troposphere, Proceedings of the National Academy of Sciences, 120. Code, package versions The code is generally written in Python and saved to a Jupyter Notebook (.ipynb) format, except for the component developing the Bayesian regressions, which is written in R. For improved accessibility, the code has also been printed to PDF format so that it may be readable without requiring access to Jupyter. The code used to create the main text figures is specified in the file names. When the primary focus of a script is to create supplemental figures, the figure names have also been specified in the script file name. The code for creating other supplemental figures is also available in the script corresponding to the section where that figure is referenced.  The following packages and package versions were used to develop this analysis: Python (v3.10.0) <ul> <li>anaconda 4.13.0 <ul> <li>collections (native to anaconda installation)</li> <li>datetime</li> <li>os </li> <li>random</li> </ul> </li> <li>jupyter 1.0.0, jupyter-core 4.9.1</li> <li>matplotlib (visualization) 3.5.1</li> <li>notebook 6.4.6</li> <li>numpy 1.21.4</li> <li>pandas 1.3.4</li> <li>scipy 1.7.3</li> <li>seaborn (figure formatting) 0.11.2</li> </ul> The full environment is specified in the YAML file "atom_env.yml." Anaconda users (not tested, potentially restricted to Windows) may load this environment with this file and the following command: $ conda env create -f atom_env.yml R  (v4.1.2, includes package parallel) <ul> <li>tidyverse 1.3.1</li> <li>rjags 4-12</li> <li>runjags 2.2.0-3</li> <li>lattice 0.20-45</li> <li>lme4 1.1-31</li> <li>loo 2.4.1</li> <li>ggpubr 0.4.0</li> <li>matrixStats 0.61.0</li> </ul> Zipped directory contents The full set of global, hourly AM3 model simulations developed for this project are included in this repository (AM3_hourly_simulations_global_ATom1-4.zip) for reference and potential future application, though they are not used in the code. They are described here (vs listed) and span the dates for each campaign leg and are broken into four variable categories, concentrations and met fields ('stp_conc_v2'), individual reaction rates ('ind_rate'), integrated reaction rates ('all_rate') and deposition velocities or photolysis rates ('dep_jval'). Some of these files include all days in the range, while others include only the days that the campaign took measurements. In addition, a subset of the AM3 simulations that specifically include variables used in the manuscript analysis that have been sampled along the ATom flight is included, along with the 10 s ATom merge  data (AM3_model_simulations_sampled.zip). This is the file that should be downloaded for reproducing the manuscript in the analysis. <ul> <li>AM3_model_simulations_sampled.zip <ul> <li>atom1_10s_ss_030122.csv</li> <li>atom2_10s_ss_030122.csv</li> <li>atom3_10s_ss_030122.csv</li> <li>atom4_10s_ss_030122.csv</li> </ul> </li> <li>bayes_data.zip <ul> <li>bayes_atom_10s_model_122022.csv</li> <li>bayes_ats_10s_remNOlsth2sigma_highlogNO_emulate_122022.csv</li> <li>bayes_ats_10s_remNOlsth2sigma_highlogNO_emulate_allPOH_030723.csv</li> <li>base/ <ul> <li>.Rhistory</li> <li>atom_jags_010723.R</li> <li>atom_lmer_model_122122.R</li> <li>atom_sens_030723.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>gelman_list_base.csv</li> <li>levels.csv</li> <li>log_pd.csv</li> <li>model_b0.csv</li> <li>model_b1.csv</li> <li>p.fit.csv</li> <li>p.mu.csv</li> <li>p.sd.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>CH4_CO_HCHO_MHP/ <ul> <li>.Rhistory</li> <li>atom_altCH4_CO_HCHO_MHP_031423.R</li> <li>atom_jags_altCH4_CO_HCHO_MHP_031423.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_ch4_co_hcho_mhp.csv</li> <li>levels.csv</li> <li>log_pd.csv</li> <li>p.fit.csv</li> <li>p.mu.csv</li> <li>p.sd.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_ch4_co_hcho_mhp.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>CO_HCHO/ <ul> <li>.Rhistory</li> <li>atom_altCO_HCHO_031423.R</li> <li>atom_jags_altCO_HCHO_031423.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_CO_HCHO.csv</li> <li>levels.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_co_hcho.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>CO_HCHO_MHP/ <ul> <li>atom_altCO_HCHO_MHP_031423.R</li> <li>atom_jags_altCO_HCHO_MHP_031423.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_CO_HCHO_MHP.csv</li> <li>levels.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_CO_HCHO_MHP.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>H2O2_O3_CH4_CO_HCHO_MHP/ <ul> <li>.Rhistory</li> <li>atom_altH2O2_O2_CH4_CO_HCHO_MHP_122122.R</li> <li>atom_jags_altH2O2_O3_CH4_CO_HCHO_MHP_030723.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_h2o2_o3_ch4_co_hcho_mhp.csv</li> <li>levels.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_h2o2_o3_ch4_co_hcho_mhp.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>HCHO/ <ul> <li>atom_altHCHO_031423.R</li> <li>atom_jags_altHCHO_031423.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_HCHO.csv</li> <li>levels.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_HCHO.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>MHP/ <ul> <li>atom_altMHP_122122.R</li> <li>atom_jags_altMHP_122122.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_MHP.csv</li> <li>levels.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_MHP.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> </ul> </li> <li>F0AMv3.2.zip <ul> <li>mean_ratio_OH_loss_bins_oce.npy</li> <li>mean_ratio_OH_prod_bins_oce.npy</li> <li>mean_ratio_OH_prod_loss_bins_oce.npy</li> <li>Data/ <ul> <li>atom1/ <ul> <li>atom1_output_alt.cs</li> <li>atom1_output_CO.csv</li> <li>atom1_output_H2O.csv</li> <li>atom1_output_lat.csv</li> <li>atom1_output_lon.csv</li> <li>atom1_output_lossOH_ppt_lump15.csv</li> <li>atom1_output_M.csv</li> <li>atom1_output_NO.csv</li> <li>atom1_output_OH.csv</li> <li>atom1_output_prodOH_ppt_lump15.csv</li> <li>atom1_output_startTime.csv</li> <li>atom1_output_sza.csv</li> </ul> </li> <li>atom2/ <ul> <li>atom2_output_alt.csv</li> <li>atom2_output_CO.csv</li> <li>atom2_output_H2O.csv</li> <li>atom2_output_lat.csv</li> <li>atom2_output_lon.csv</li> <li>atom2_output_lossOH_ppt_lump15.csv</li> <li>atom2_output_M.csv</li> <li>atom2_output_NO.csv</li> <li>atom2_output_OH.csv</li> <li>atom2_output_prodOH_ppt_lump15.csv</li> <li>atom2_output_startTime.csv</li> <li>atom2_output_sza.csv</li> </ul> </li> <li>atom3/ <ul> <li>atom3_output_alt.csv</li> <li>atom3_output_CO.csv</li> <li>atom3_output_H2O.csv</li> <li>atom3_output_lat.csv</li> <li>atom3_output_lon.csv</li> <li>atom3_output_lossOH_ppt_lump15.csv</li> <li>atom3_output_M.csv</li> <li>atom3_output_NO.csv</li> <li>atom3_output_OH.csv</li> <li>atom3_output_prodOH_ppt_lump15.csv</li> <li>atom3_output_startTime.csv</li> <li>atom3_output_sza.csv</li> </ul> </li> <li>atom4/ <ul> <li>atom4_output_alt.cs</li> <li>atom4_output_CO.csv</li> <li>atom4_output_H2O.csv</li> <li>atom4_output_lat.cs</li> <li>atom4_output_lon.cs</li> <li>atom4_output_lossOH_ppt_lump15.csv</li> <li>atom4_output_M.csv</li> <li>atom4_output_NO.csv</li> <li>atom4_output_OH.csv</li> <li>atom4_output_prodOH_ppt_lump15.csv</li> <li>atom4_output_startTime.csv</li> <li>atom4_output_sza.csv</li> </ul> </li> </ul> </li> </ul> </li> </ul> For any further questions on the model simulations or code included here, please contact the corresponding author (Colleen Baublitz, cbb2158@columbia.edu).

Numerical model and natural river data for the timescale analysis of meandering channel migration

This is the archive of the numerical model and river centerline data used for analyzing the timescale related to meandering channel migration, which is tied to the manuscript submitted to Journal of Geophysical Research: Earth Surface: Li, Y., and Limaye, A. B., Timescale of the morphodynamic feedback between planform geometry and lateral migration of meandering rivers. Running this model needs a MATLAB® software environment. The model can be launched by the wrapper scripts saved under the folder "software code/example wrappers". The wrapper script called "wrapper01a_channelOnly_runModel.m" is used to generate all model simulations in this study.

Data for "Automated analysis of surface facets: the example of cesium telluride"

The AiiDA archives of the high-throughput calculations presented in the paper "Automated analysis of surface facets: the example of cesium telluride". The file "Cs2Te_surfaces_workflows.aiida" contains the actual calculation data and the files with suffix "*.yaml" contain configuration files of the workflows.

Dataset for "Computer vision assisted decomposition analysis of atom probe tomography data"

Dataset for the article "Computer vision assisted decomposition analysis of atom probe tomography data". APT measurements were performed by Marcus Hans at Materials Chemistry (RWTH Aachen University) using a CAMECA LEAP 4000X HR. Training data was created by Janis A. Sälker. Content: - 13 (V,Al)N and 3 (Ti,Al)N APT reconstructions (.epos file format) and the corresponding range file (.rrng file format). - Training data (images & masks) for 9 labeled (V,Al)N APT samples (h5 file format). Image data with key "image" of shape (2, number_of_slices, 608, 192), where 2 corresponds to the V- and Al-contribution/channel and 608/192 to the height/width of the images. Masks/labels with key "label" of shape (number_of_slices, 608, 192)

opencc-by-4.0Sep 2022View details →

Data from: Biological clocks as age estimation markers in animals: a review and meta-analysis

Complete dataset of Methylation and Telomere studies searched, retrieved, analysed, extracted and compared in the review and meta-analysis "Biological clocks as age estimation markers in animals: a review and meta-analysis".

Supplementary Data and Code: Change Point Analysis to decode Economic Crisis Information

Raw data, results and Python code of the corresponding publication "Efficient Multi-Change Point Analysis to decode Economic Crisis Information from the S&P500 Mean Market Correlation" (accepted in: Entropy; Section: Complexity; Special Issue: Complexity in Finance). The change point analysis can be performed using the <a href="https://anticpy.readthedocs.io/en/latest/">documented</a> Python package <a href="https://github.com/MartinHessler/antiCPy">antiCPy</a>. Some further helpful Python scripts are provided here under a GNU General Public License v3.0. In Data_Generation you can find a list of <ol> <li>S&P500 companies which are considered in the analysis,</li> <li>a jupyter notebook to create the correlation time series.</li> </ol> Data_Preprocessing contains the <ol> <li>S&P500 mean market correlation Financial_Time_Series_Centered_Interval__42days.csv,</li> <li>the Python code to thin it,</li> <li>the thinned data saved as .npy file,</li> <li>the time scale is saved <ul> <li>as integer numbers in thinned_time_thinning40.npy,</li> <li>as datetime in TimeScale_FinancialData.npy.</li> </ul> </li> </ol> In Change_Point_Analysis you find the following files: <ol> <li>cp_probs...npy contain the joint probabilities of the corresponding change point configurations (The joint probabilities are saved corresponding to the order in which Python's itertools.combinations() creates the configurations. This holds also for the cp_probs_5_cps.npy for which the whole combinations array is not saved for memory reasons),</li> <li>cp_pdfs...npy contain the marginal probability density functions of the ordinal change point positions averaged over all configurations,</li> <li>cp_configs...npy contain the configurations,</li> <li>segment_fit...npy contain the segment fit data,</li> <li>segment_fit_variance...npy contain corresponding variances,</li> <li>In the case of five change points only the plotted 1st, 13th and 26th most probable configuration in config_ranking_CP1_5.npy for memory reasons.</li> <li>the data and results of figure 3 for each crisis event can be found in the corresponding directory's folders: <ul> <li>blue corresponds to the pre-crisis data segments,</li> <li>green corresponds to the data segments up to the green vertical dotted line,</li> <li>red corresponds to the longest data segments incorporating near and in-crisis data.</li> </ul> </li> </ol>

Neutronic Analysis of the BYU MSMR - Data

Supporting data for the Neutronic Analysis of the BYU MSMR. Within these data files, the reactor geometries, materials, settings, and tallies can be found. The scripts and data used to generate graphs for feedbacks, power profile, and flux profile are also included.

Mammal Trophic Diversity Data and Analysis Code

A global scale dataset of terrestrial mammal species richness within three trophic groupings. Trophic groups are predators, herbivores, and omnivores. Spatial resolution is 30 x 30 km. Variable descriptions are as follows: FID_1: An identifier variable. y_coord: Latitudinal value. mamm_h_20: The number of herbivore species present in the pixel. mamm_o_20: The number of omnivore species present in the pixel. mamm_p_20: The number of predator species present in the pixel. total_20: The total number of mammals present in the pixel. p_herb_20: The proportion of total species in the pixel that are herbivores. p_omni_20: The proportion of total species in the pixel that are omnivores. p_pred_20: The proportion of total species in the pixel that are predators. GPP: Gross Primary Production of the pixel; extracted from doi 10.1038/sdata.2017.165. mean_temp: The mean annual temperature of the pixel in celsius; extracted from WorldClim v.2. mean_precip: The mean annual precipitation of the pixel in centimeters; extracted from WorldClim v.2. temp_season: The temperature seasonality of the pixel as standard deviation multiplied by 100. precip_season: The precipitation seasonality of the pixel as the coefficient of variation. iso: The isothermality of the pixel as diurnal temperature range divided by annual temperature range. A text file of the code used to analyze data in the R Statistical computing environment is also included.

opencc-by-4.0Dec 2022View details →

Dataset: Preliminary analysis of open data pertaining to the services available through the Health Insurance Institute of Slovenia and provided by family medicine

BACKGROUND: The Health Insurance Institute of Slovenia (ZZZS) began publishing service-related data in May 2023, following a directive from the Ministry of Health (MoH). The ZZZS website provides easily accessible information about the services provided by individual doctors, including their names. The user is provided relevant information about the doctor's employer, including whether it is a public or private institution. The data provided is useful for studying the public system's operations and identifying any errors or anomalies.  METHODS: The data for services provided in May 2023 was downloaded and analysed. The published data were cross-referenced using the provider's RIZDDZ number with the daily updated data on ambulatory workload from June 9, 2023, published by ZZZS. The data mentioned earlier were found to be inaccurate and were improved using alerts from the zdravniki.sledilnik.org portal. Therefore, they currently provide an accurate representation of the current situation. The total number of services provided by each provider in a given month was determined by adding up the individual services and then assigning them to the corresponding provider.  RESULTS: A pivot table was created to identify 307 unique operators, with 15 operators not appearing in both lists. There are 66 public providers, which make up about 72% of the contractual programme in the public system. There are 241 private providers, accounting for about 28% of the contractual programme. In May 2023, public providers accounted for 69% (n=646,236) of services in the family medicine system, while private providers contributed 31% (n=291,660). The total number of services provided by public and private providers was 937,896. Three linear correlations were analysed. The initial analysis of the entire sample yielded a high R-squared value of .998 (adjusted R-squared value of .996) and a significant level below 0.001. The second analysis of the data from private providers showed a high R Squared value of .904 (Adjusted R Squared = .886), indicating a strong correlation between the variables. Furthermore, the significance level was < 0.001, providing additional support for the statistical significance of the results. The third analysis used data from public providers and showed a strong level of explanatory power, with a R Squared value of 1.000 (Adjusted R Squared = 1.000). Furthermore, the statistical significance of the findings was established with a p-value < 0.001.  CONCLUSION: Our analysis shows a strong linear correlation between contract size of the program signed and number services rendered by family medicine providers. A stronger linear correlation is observed among providers in the public system compared to those in the private system. Our study found that private providers generally offer more services than public providers. However, it is important to acknowledge that the evaluation framework for assessing services may have inherent flaws when examining the data. Prescribing a prescription and resuscitating a patient are both assigned a rating of one service. It is crucial to closely monitor trends and identify comparable databases for pairing at the secondary and tertiary levels.

opencc-by-4.0Sep 2023View details →

Fermi-GBM and Swift-BAT Data Release Related to Analysis of Gravitational-Wave Candidates from the Third Gravitational-wave Observing Run

This material contains the data products associated with A Joint Fermi-GBM and Swift-BAT Analysis of Gravitational-Wave Candidates from the Third Gravitational-wave Observing Run [1]. It is based, in part, on data products associated with GWTC-2.1 [2][3] and GWTC-3 [4][5] from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration provided under a Creative Commons Attributional 4.0 International license. For more information, see the paper (<a href="https://arxiv.org/abs/2308.13666">https://arxiv.org/abs/2308.13666</a>) and the included README.txt. The data release includes the following directories: <ul> <li>3sigma_upper_limit_maps: 3 sigma flux upper limits reported by GBM as a function of sky position over a 10-1000 keV energy range.</li> <li>5sigma_upper_limit_maps: 5 sigma flux upper limits reported by GBM and BAT as a function of sky position over a 15-350 keV energy range.</li> <li>gbm_targeted_results: GRB candidates from the GBM Targeted Search.</li> <li>gbm_temporal_offset_analysis: input files to the time offset analysis applied to GBM on-board triggers and candidates from the GBM Untargeted Search.</li> <li>bbh_model_fluxes: predicted gamma-ray fluxes over the 10-1000 keV energy range for likely BBH mergers.</li> <li>gw_localizations: GW localization files used to overlay the 90% credible area onto GBM upper limits as a function of sky position.</li> <li>m1_m2_contours: 90% credible region paths for the GW component masses m1, m2.</li> </ul> The data release also includes a set of example scripts to show how the contents of the data files are used. Refer to the included README.txt for more details. References: <a href="https://arxiv.org/abs/2308.13666">[1] Fletcher, C. et al 2023, arXiv, 2308.13666</a> <a href="https://doi.org/10.5281/zenodo.6513631">[2] LIGO Scientific Collaboration and Virgo Collaboration. 2022,  Zenodo, 6513631</a> <a href="https://doi.org/10.5281/zenodo.5759108">[3] LIGO Scientific Collaboration and Virgo Collaboration. 2021, Zenodo, 5759108</a> <a href="https://doi.org/10.5281/zenodo.5546663">[4] LIGO Scientific Collaboration and Virgo Collaboration and KAGRA Collaboration. 2021, Zenodo, 5546663</a> <a href="https://doi.org/10.5281/zenodo.5546665">[5] LIGO Scientific Collaboration and Virgo Collaboration and KAGRA Collaboration 2021, Zenodo, 5546665</a>