cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/4fgvgkhhkh.1
title: Geographic datasets supporting the N balance in four basins of semiarid saline wetlands in the Ebro Basin, NE, Spain

description: We presented the background information for the study “An assessment of the N load from animal farms in saline wetland catchments in the Ebro Basin, NE Spain”. The studied wetlands basins are the: Saladas de Alcañiz-Calanda, Gallocanta Lake, Saladas de Sástago-Bujaraloz and Sariñena Lake. All the areas are catalogued in the Natura 2000 European Network, under the European Directives Birds (2009/147/EC) and Habitats (92/43/EEC), and are nitrates vulnerable areas according to the European Water Framework Directive (WFD, 91/676/EC) designation. The data presented here belong to the research projects AGROWET, funded by MCIN/AEI/10.13039/501100011033 and by the “European Union NextGeneration EU/PRTR”, and ISABEL, funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”.
The dataset includes 4 compressed folders: “Saladas de Alcañiz-Calanda (ALC).zip”, “Gallocanta Lake (GA).zip”, “Saladas de Sastago-Bujaraloz (BU).zip” and “Sariñena Lake (SA).zip”. Each folder comprises fives GIS files generated in ArcGIS v.10.8: the “Watershed_name_of_basin”, “dem_name_of_basin”, “NVZ_name_of_basin” (Nitrate Vulnerable Zone), “Livestock_name_of_basin” and “Crop_name_of_basin”.
“Watershed_name_of_basin” is a shape (polygon) delimited following the drainage divides with the help of the digital elevation model (DEM). “DEM_name_of_basin” is a raster file generated from the LiDAR data with a vertical accuracy of 2 m and a density of 0.5 points m2. “NVZ_name_of_basin” is a shape (polygon) downloaded from the National Spatial Data Infrastructure of the MITECO website (https://www.miteco.gob.es/es/cartografia-y-sig/ide/descargas/agua/zonas-vulnerables.html). “Livestock_name_of_basin” is a shape (point) of the livestock census collected as Table from the official administrative Register of Livestock Farms of Aragon, Spain (REGA), and it was available in the Open Data platform (https://opendata.aragon.es/). This shape presents a legend layer (.lyr). “Crop_name_of_basin” is a shape (polygon) based on the information provided by the farmer declarations in the context of the Common Agricultural Policy (CAP). This shape presents a legend layer with the declared crops grouped for a better spatial representation. The abovementioned geographic layers are map projected in the European Datum ETRS89/UTM zone 30N.
The dataset includes an Excel spreadsheet “shape_attribute_info.xlsx” where the head columns of the attribute table of the shapes “livestock” and “crop” are explained. 
Apart from the basic information from the whole basins, we include the compressed folder named “Farnaca.zip” containing the geographic files used for the specific study of this area in the “Saladas de Sástago-Bujaraloz”: the shape (polygon) delimitating the study area (“area_farnaca”), and the slope of this polygon (“slope_farnaca”) with their corresponding legend layer.
DOI: 10.15468/dl.8vfjwe
title: Occurrence Download

description: A dataset containing 11473 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Neomyrtus Burret"
 ]
}

 The dataset includes 11473 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007822-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/fxy862xx4m.1
title: Dataset of Operational Energy of forty Andean Buildings from 1980 to 2020.

description: The files presented in this Dataset Article are divided in three parts: the Operational Energy results; the Simulation data which include simulation files and morphological/materials characteristics of each sample; and the Climate data which refer to an updated climate file for the study region. These three parts of the data are described below:
1) Operational Energy Data.
The is the entire house Operational Energy, named as Specific Total Operational Energy STOE, which accounts for the summatory of space heating and space cooling (sh + sc), lighting and electrical appliances (l + e) and Domestic Hot Water and Cooking (d + c), divided by the total floor area of each residential building. This value is calculated for each of the forty houses in the study accounting both annually and a 50-years lifespan consumption. 
These results are shown in the repository in the file named STOE.xlsx. 
2) Simulation Data 
This section contains two types of files. On one hand, numerical data is presented in three Excel files which are in the repository with the names: Building_Features.xlsx, Measurements.xlsx, and Measurements&Simulations_Results.xlsx. On the other hand, there are forty DesignBuilder (.dsb) files which are in the repository in a folder named SimulationDATA. In First instance this section presents the Building_Features.xlsx table that contains the raw data from the envelope materials of each one of the 40 sample buildings. 
The Measurements.xlsx file contains the raw data measured in situ from eight houses used in the study to validate the simulations. 
Furthermore, the Measurements&Simulations_Results.xlsx file, this table contains the raw data from each one of the eight samples measured in-situ and simulated in DesignBuilder accordingly.
Lastly, this section presents 40 DesignBuilder Files (.dsb) one for each of the 40 analysed houses and stored in separate .dsb files, named using the format P#_S#, where P represents the period (decade), and S represents the sample number. Each file is configured according to the unique morphology and the materials are taken from the file Building_Features.xlsx . The intensity and schedule of lighting, equipment and occupancy were established as a common setting for all forty samples. 
3) Climate Data 
This section contains two files. The first one is the climate data gathered to update the weather file to use for simulation, named as Climate_Data_Forupdate.xlsx. The second one is the updated climate file itself, named as ECU_CUENCA_IWEC_2022.epw.
The Climate_Data_Forupdate.xlsx is an excel table, which was created through the climate data gathered during July and August from the weather station ICUENC29 (coordinates: 2.89°S, 79.01°W, altitude: 2564 masl) 
Finally, the second file is ECU_CUENCA_IWEC_2022.epw which is an Energy Plus Weather file updated with the information collected from the meteorological station from July 1, 2021, at 12:04 AM to August 31, 2021, at 11:59 PM, with a sampling interval of 1-hour.
DOI: 10.17632/fxy862xx4m.2
title: Dataset of Operational Energy of forty Andean Buildings from 1980 to 2020.

description: The files presented in this Dataset Article are divided in three parts: the Operational Energy results; the Simulation data which include simulation files and morphological/materials characteristics of each sample; and the Climate data which refer to an updated climate file for the study region. These three parts of the data are described below:
1) Operational Energy Data.
The is the entire house Operational Energy, named as Specific Total Operational Energy STOE, which accounts for the summatory of space heating and space cooling (sh + sc), lighting and electrical appliances (l + e) and Domestic Hot Water and Cooking (d + c), divided by the total floor area of each residential building. This value is calculated for each of the forty houses in the study accounting both annually and a 50-years lifespan consumption. 
These results are shown in the repository in the file named STOE.xlsx. 
2) Simulation Data 
This section contains two types of files. On one hand, numerical data is presented in three Excel files which are in the repository with the names: Building_Features.xlsx, Measurements.xlsx, and Measurements&Simulations_Results.xlsx. On the other hand, there are forty DesignBuilder (.dsb) files which are in the repository in a folder named SimulationDATA. In First instance this section presents the Building_Features.xlsx table that contains the raw data from the envelope materials of each one of the 40 sample buildings. 
The Measurements.xlsx file contains the raw data measured in situ from eight houses used in the study to validate the simulations. 
Furthermore, the Measurements&Simulations_Results.xlsx file, this table contains the raw data from each one of the eight samples measured in-situ and simulated in DesignBuilder accordingly.
Lastly, this section presents 40 DesignBuilder Files (.dsb) one for each of the 40 analysed houses and stored in separate .dsb files, named using the format P#_S#, where P represents the period (decade), and S represents the sample number. Each file is configured according to the unique morphology and the materials are taken from the file Building_Features.xlsx . The intensity and schedule of lighting, equipment and occupancy were established as a common setting for all forty samples. 
3) Climate Data 
This section contains two files. The first one is the climate data gathered to update the weather file to use for simulation, named as Climate_Data_Forupdate.xlsx. The second one is the updated climate file itself, named as ECU_CUENCA_IWEC_2022.epw.
The Climate_Data_Forupdate.xlsx is an excel table, which was created through the climate data gathered during July and August from the weather station ICUENC29 (coordinates: 2.89°S, 79.01°W, altitude: 2564 masl) 
Finally, the second file is ECU_CUENCA_IWEC_2022.epw which is an Energy Plus Weather file updated with the information collected from the meteorological station from July 1, 2021, at 12:04 AM to August 31, 2021, at 11:59 PM, with a sampling interval of 1-hour.
DOI: 10.17632/bm49k7d8wv
title: Breeding Woodcock Ireland survey data and analysis

description: This data contains the data and code required to replicate analyses for a two-step species distribution modelling approach to estimate the distribution and abundance of breeding Eurasian woodcock (Scolopax rusticola) across the Republic of Ireland. Quantitative species occurrence data was collected at 238 randomly-selected forest sites by volunteers who counted displaying (roding) male woodcock at dusk in May and June of 2017, 2019 and 2021. Environmental data used consisted of historical bioclimatic variables downloaded from www.worldclim.org, and landcover (forest and urban settlement) derived from datasets provided by Coillte, DAFM Forestry Division, DAERA Forest Service and CORINE landcover. The first step was an ensemble model using biomod2 in R, to estimate the geographical range of breeding woodcock in Ireland using bioclimatic variables at a 1 * 1 kilometre resolution. The second step was a Generalised Additive Model (GAM) using 'mgcv' in R to determine the bioclimatic and landcover drivers behind woodcock abundance, conditional on presence (falling within the geographic range estimated in the first step). The GAM was then projected across 1 km squares with a minimum threshold of forest habitat (0.1 km2) within the estimated range to predict the distribution and abundance across Ireland. Finally, the predicted density from all relevant 1km squares were summed to produce a national population estimate, with standard errors.
DOI: 10.17632/bm49k7d8wv.1
title: Breeding Woodcock Ireland survey data and analysis

description: This data contains the data and code required to replicate analyses for a two-step species distribution modelling approach to estimate the distribution and abundance of breeding Eurasian woodcock (Scolopax rusticola) across the Republic of Ireland. Quantitative species occurrence data was collected at 238 randomly-selected forest sites by volunteers who counted displaying (roding) male woodcock at dusk in May and June of 2017, 2019 and 2021. Environmental data used consisted of historical bioclimatic variables downloaded from www.worldclim.org, and landcover (forest and urban settlement) derived from datasets provided by Coillte, DAFM Forestry Division, DAERA Forest Service and CORINE landcover. The first step was an ensemble model using biomod2 in R, to estimate the geographical range of breeding woodcock in Ireland using bioclimatic variables at a 1 * 1 kilometre resolution. The second step was a Generalised Additive Model (GAM) using 'mgcv' in R to determine the bioclimatic and landcover drivers behind woodcock abundance, conditional on presence (falling within the geographic range estimated in the first step). The GAM was then projected across 1 km squares with a minimum threshold of forest habitat (0.1 km2) within the estimated range to predict the distribution and abundance across Ireland. Finally, the predicted density from all relevant 1km squares were summed to produce a national population estimate, with standard errors.
DOI: 10.17632/shhtywfbpf.1
title: Source Data and Source Code for: Cytoplasmic Mg2+ supersedes carbon source preference to dictate Salmonella metabolism

description: Source data and source code for the manuscript entitled "Cytoplasmic Mg2+ supersedes carbon source preference to dictate
Salmonella metabolism," authored by Nick D. Pokorzynski, Katarina A. Jones, Shawn R. Campagna, and Eduardo A. Groisman
DOI: 10.17632/shhtywfbpf.2
title: Source Data and Source Code for: Cytoplasmic Mg2+ supersedes carbon source preference to dictate Salmonella metabolism

description: Source data and source code for the manuscript entitled "Cytoplasmic Mg2+ supersedes carbon source preference to dictate
Salmonella metabolism," authored by Nick D. Pokorzynski, Katarina A. Jones, Shawn R. Campagna, and Eduardo A. Groisman
DOI: 10.17632/kn3k9f5vph.2
title: Intracranial EEG (iEEG) Recording in a Rat Model of Epilepsy

description: Summary:
This database, collected at the Neural Engineering Laboratory, Iran University of Science and Technology, comprises iEEG recordings from Wistar rats during healthy and epileptic conditions. Recordings were collected from 5 rats (3 males, 2 females, weighing 260-378 g and aged 4-5 months). iEEG signals were recorded from 3 brain sites: motor cortex (left M1), thalamus (left ANT), and hippocampus (right CA1) of freely moving rats. As a result, for each rat, a matrix with 3 columns (representing the 3 signals) is available in this dataset.
To induce temporal lobe epilepsy (TLE), a nonconvulsive pentylenetetrazol (PTZ) model was used. In this epileptic model, rats displayed recurrent spontaneous seizures at Racine stage 1-2, accompanied by spike-wave discharges (SWDs). A seizure can be detected by identifying high-amplitude, repetitive SWDs in the epileptic signals.
Recordings were conducted in two conditions: the healthy condition (before PTZ injection) and the epileptic condition (after PTZ injection). Each healthy and epileptic condition contains several trials.

The details of recording and experimental methods can be seen in the paper:
Azam Ghanaei and Abbas Erfanian, ''Real-time Seizure Prediction Using Nonlinear Dynamical Analysis of Intracranial EEG and Deep Learning in a Rodent Model of Epilepsy''.

Data files organization:
The data files are organized into five main folders, each corresponding to a subject (Rat01, Rat02, Rat03, Rat04, and Rat05). Each main folder contains two subfolders for the healthy and epileptic conditions, named 'Healthy' and 'Epileptic'. Each subfolder contains several .mat files corresponding to the recording trials. Each .mat file contains three iEEG signals from a single recording trial. 
The name of each .mat file is 'RatID#_E/H_S#,' where 'ID#' refers to the rat’s number, 'E/H' indicates the animal conditions (E for 'Epileptic' and H for 'Healthy'), and 'S#' indicates the experimental trial. For example, 'Rat01_E_01.mat' contains the recorded iEEG signals from the epileptic animal for rat 1, trial 1.

Data format: 
The data are in MATLAB format (.mat). Each .mat file contains a signal matrix (i.e., 'RatID#_E/H_S#') with n rows and m columns (n×m), representing iEEG signals from a single trial.
 n is the number of samples, which can be calculated as Sampling Rate × Trial Recording Time.
 m is the number of channels (m=3)
To have signals in the millivolt range, divide the amplitude by 10000.
DOI: 10.17632/mypxw5zbct.1
title: metadados Vanessa

description: NaNbO3@WO3
DOI: 10.15468/dl.7742fd
title: Occurrence Download

description: A dataset containing 1325 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Agonum gratiosum (Mannerheim, 1853)",
 "Year is greater than or equal to 1950",
 {
 "or" : [
 "GadmGid is USA",
 "GadmGid is CAN",
 "GadmGid is MEX"
 ]
 }
 ]
}

 The dataset includes 1325 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007823-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/h7m9f98x94.1
title: Effects of a Job Demand-Control-Support intervention on managers' depression, anxiety, and stress

description: This study tested the impact of a job-demand-control-support (JDCS) model intervention on depression, anxiety, and stress (DAS) levels among Brazilian remote managers. The intervention aimed to enhance perceived control and coping strategies, reducing DAS levels. The intervention protocol is presented in detail. A longitudinal field experiment with 122 managers used a mixed-method approach across three stages: pre-intervention, intervention, and follow-up after 3 months. Managers were divided into intervention (n=33) and control (n=34) groups. The Goodness-of-fit indices of Job Demand Control Support and DASS area presented. Results revealed that social support reduced perceived demands in the intervention group, serving as a protective factor within the JDCS model. Additionally, demands acted differently between groups, highlighting a dynamic interaction.
DOI: 10.17632/4z8z2tm7dh.1
title: LINF_170020700

description: Nicotinamide Mononucleotide Adenylyltransferase | NMNAT
Leishmania infantum (strain JPCM5)
DOI: 10.17632/4z8z2tm7dh.2
title: LINF_170020700

description: Nicotinamide Mononucleotide Adenylyltransferase | NMNAT; Leishmania infantum (strain JPCM5)
DOI: 10.17632/c8pvz89ztr.1
title: LINF_170021200

description: protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/c8pvz89ztr.2
title: LINF_170021200

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/8hmr85pjw7.1
title: Chemical and Behavioral Fingerprinting Data for the Identification of Neuroactive Compounds in Crude Oil-Derived Water Accommodated Fractions

description: Data and Code Description
This dataset supports the identification and mechanistic evaluation of neuroactive chemicals present in crude oil-derived water accommodated fractions (WAF) using behavioral phenotyping in zebrafish larvae and analytical chemistry. 
It includes GC×GC-MS compound annotations, behavioral cluster assignments for reference neuroactive chemicals, and R scripts for generating standardized effect size (SSMD) heatmaps, cloud plots, and multidimensional scaling (MDS) analyses. 
Data processing was performed using KNIME, and all visualizations were created in RStudio.
DOI: 10.17632/8hmr85pjw7.2
title: Chemical and Behavioral Fingerprinting Data for the Identification of Neuroactive Compounds in Crude Oil-Derived Water Accommodated Fractions

description: Data and Code Description
This dataset supports the identification and mechanistic evaluation of neuroactive chemicals present in crude oil-derived water accommodated fractions (WAF) using behavioral phenotyping in zebrafish larvae and analytical chemistry. 
It includes GC×GC-MS compound annotations, behavioral cluster assignments for reference neuroactive chemicals, and R scripts for generating standardized effect size (SSMD) heatmaps, cloud plots, and multidimensional scaling (MDS) analyses. 
Data processing was performed using KNIME, and all visualizations were created in RStudio.
DOI: 10.17632/xx2fyny36k.1
title: LINF_170021900

description: Calcium channel protein; Leishmania infantum (strain JPCM5)
DOI: 10.17632/xx2fyny36k.2
title: LINF_170021900

description: Calcium channel protein; Leishmania infantum (strain JPCM5)
DOI: 10.17632/25z97kkndg.1
title: Intracranial evoked EEG activities recorded before, during and after tDCS in humans

description: This dataset supports our study titled "Transcranial Direct Current Stimulation Neuromodulates Intracranial Cognitive Evoked Activity in Humans". The study aimed to investigate the intracerebral electrophysiological effects of Transcranial Direct Current Stimulation (tDCS) on the occipito-temporal network during a visual recognition task in humans. 
The study involved 11 patients with drug-resistant epilepsy. Patients gave written consent to participate in the study, which was conducted according to the Declaration of Helsinki and approved by a national ethics committee (Comité de Protection des Personnes Sud-Ouest et Outre Mer 4) certified by the French Ministry of Health (Institutional Review Board: IORG0009855).
The experiment combined simultaneously intracerebral recordings (SEEG), fast periodic visual stimulation (FPVS) and tDCS. The FPVS paradigm implemented in this study allows the investigation of two main responses: the face-selective responses and the general visual responses. 
Both responses were investigated before, during and after a 20 min tDCS. 
The dataset provided the signal to noise ratios and the baseline corrected amplitudes of the intracerebral EEG recordings for the contacts showing significant responses during the FPVS testing prior to this testing period. 
Unlike global visual iEEG responses, significant neuromodulations of face-selective iEEG responses were observed during and after tDCS (in comparison to before stimulation) for the contacts in the right hemisphere. 
Interestingly, despite a unique tDCS session, the face-selective neuromodulation in the right visual occipito-temporal cortex remained significant during and after tDCS (p&lt;0.001). 
Neuromodulations in anterior & posterior temporal lobe and in the occipital lobe were found with baseline-corrected amplitude increases of 4% and 3% , 16% and 13%, and 36% and 33%, during and after tDCS ( in comparison to before tDCS), respectively. 

This dataset contains four files (in xls format):
Amplitude and SNR values of contacts showing significant face-selective responses.
Amplitude and SNR values of contacts showing general visual responses.
Anatomical localization of the iEEG contacts in the VOTC for face-selective responses.
Anatomical localization of the iEEG contacts in the VOTC for general visual responses.
DOI: 10.17632/xb97dzb93c
title: Collaborative Innovation

description: Data of Brazilian companies and innovation measures.
DOI: 10.17632/xb97dzb93c.1
title: Collaborative Innovation

description: Data of Brazilian companies and innovation measures.
DOI: 10.17632/c7rrrb74d8.1
title: Engineered core-shell immunomodulators for dual immune activation and sustained anti-MRSA protection via macrophage metabolic reprogramming

description: Artificial biofilm-coated nucleoshell immunomodulator (LM@HiSeL) is a novel immunomodulator designed to achieve precise modulation of the body's immune system through its unique nucleoshell structure and biofilm coating, in order to enhance the body's defenses against pathogens (e.g., methicillin-resistant Staphylococcus aureus, MRSA). This database aims to systematically document the comprehensive properties of LM@HiSeL, including key information on its characterization, safety, stability, bioactivity assessment, and mechanism of action, so as to provide data support for subsequent research and clinical applications.