cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19471310
title: <b>Predictors of mortality among low birth weight neonates after hospital discharge in a low-resource setting: A case study in Uganda.</b>

description: This was a prospective cohort study conducted at the Special Care Baby Unit (SCBU) of Mulago National Referral Hospital from 8<sup>th</sup> November 2018 to 28<sup>th</sup> January 2019.This study aimed at determining the proportion of LBW neonates that die after discharge from the SCBU, and the predictors of mortality.
DOI: 10.6084/m9.figshare.19471310.v1
title: <b>Predictors of mortality among low birth weight neonates after hospital discharge in a low-resource setting: A case study in Uganda.</b>

description: This was a prospective cohort study conducted at the Special Care Baby Unit (SCBU) of Mulago National Referral Hospital from 8<sup>th</sup> November 2018 to 28<sup>th</sup> January 2019.This study aimed at determining the proportion of LBW neonates that die after discharge from the SCBU, and the predictors of mortality.
DOI: 10.17632/87gyr3s426.1
title: Effects of Input Gradient Regularization on Neural Networks Time-Series Forecasting of Thermal Power Systems

description: Training logs and performance data for the neural networks tested on the Hunter power plant.
DOI: 10.17632/3p99kjhw95.1
title: Discrete prefrontal neuronal circuits determine repeated stress-induced behavioral phenotypes in male mice

description: Table S1. Differentially expressed genes in NS IP vs. NS input, related to Figure 5
Table S2. Differentially expressed genes (237 DEGs) in ANH IP vs. NS IP, related to Figure 5
Table S3. Differentially expressed genes (455 DEGs) in SA IP vs. NS IP, related to Figure 5
Table S4. Differentially expressed genes (1089 DEGs) in SA:ANH IP vs. NS IP, related to Figure 5
Table S5. Differentially expressed genes (85 DEGs) in RES IP vs. NS IP, related to Figure 5
Table S6. Differentially expressed genes (2204 DEGs) in SA IP vs. ANH IP, related to Figure 5
Table S7. Differentially expressed genes (882 DEGs) that are uniquely regulated in SA:ANH IP, related to Figure 5
Table S8. Differentially expressed genes (794 DEGs) in SA:ANH subtype vs. NS subtype, related to Figure 7
Table S9. Differentially expressed genes (658 DEGs) in SA:ANH subtype receiving KDM5Ci vs. SA:ANH subtype receiving vehicle, related to Figure 7
Table S10. Differentially expressed genes (162 DEGs) that are up-regulated in SA:ANH subtype and are normalized by KDM5Ci, related to Figure 7
Table S11. Differentially expressed genes (205 DEGs) that are down-regulated in SA:ANH subtype and are normalized by KDM5Ci, related to Figure 7
Table S12. List of primer sequences used for qPCR, related to STAR Methods
DOI: 10.17632/3p99kjhw95.2
title: Discrete prefrontal neuronal circuits determine repeated stress-induced behavioral phenotypes in male mice

description: Table S1. Differentially expressed genes in NS IP vs. NS input, related to Figure 5
Table S2. Differentially expressed genes (237 DEGs) in ANH IP vs. NS IP, related to Figure 5
Table S3. Differentially expressed genes (455 DEGs) in SA IP vs. NS IP, related to Figure 5
Table S4. Differentially expressed genes (1089 DEGs) in SA:ANH IP vs. NS IP, related to Figure 5
Table S5. Differentially expressed genes (85 DEGs) in RES IP vs. NS IP, related to Figure 5
Table S6. Differentially expressed genes (2204 DEGs) in SA IP vs. ANH IP, related to Figure 5
Table S7. Differentially expressed genes (882 DEGs) that are uniquely regulated in SA:ANH IP, related to Figure 5
Table S8. Differentially expressed genes (794 DEGs) in SA:ANH subtype vs. NS subtype, related to Figure 7
Table S9. Differentially expressed genes (658 DEGs) in SA:ANH subtype receiving KDM5Ci vs. SA:ANH subtype receiving vehicle, related to Figure 7
Table S10. Differentially expressed genes (162 DEGs) that are up-regulated in SA:ANH subtype and are normalized by KDM5Ci, related to Figure 7
Table S11. Differentially expressed genes (205 DEGs) that are down-regulated in SA:ANH subtype and are normalized by KDM5Ci, related to Figure 7
Table S12. List of primer sequences used for qPCR, related to STAR Methods
DOI: 10.17632/zgxwbhsc3d.1
title: Ortho Trauma Dataset

description: Dataset for the orthopedic trauma research regarding authorship.
DOI: 10.17632/nfjs94x9wj.1
title: Computational Results for the article titles as - A New Approach to Number of Tardy Job Minimization in No-Wait Flowshops

description: This file contains the computational results for the article titled "A New Approach to Number of Tardy Job Minimization in No-Wait Flowshops".
There is 1 folder containing instance details with the associated due dates (generated) for VRF instances, and there are 2 Excel files representing the computational results of the Mathematical Models and the Sacrifice & Rearrange Heuristic, as well as the best-known solutions for Taillard and VRF instances.
DOI: 10.17632/mr4byrhf6w.1
title: Data - Bayesian Logistic Regression Model for bovine brucellosis cervical bursitis risk factors

description: Bovine brucellosis is an endemic zoonosis prevalent in Brazil. Cervical bursitis is a brucellosis suggestive lesion found in bovine carcasses. This work aimed to study risk factors for brucellosis seropositivity and for Brucella abortus isolation in bovine carcasses with cervical bursitis from the states of Mato Grosso, Pará and Tocantins – Brazil, through Bayesian Logistic Regression Models (BLRM). Results of the diagnostic tests were the dependent variable in three distinct BLRM (model 1 serum aglutination test with 2-mercaptoethanol SAT/2ME, model 2 – rose Bengal RB and model 3 – isolation and identification Bac). Origin of the samples, sex and age of the animals were the explanatory variables. Females were 18.54 (95%CrI 4.727 – 85.98), 16.03 (95%CrI 4.198 – 71.57) and 5.796 (95%CrI 1.551 – 32.13) times more likely to be seropositive for brucellosis or had Brucella abortus isolated from the lesions than males, in models 1, 2 and 3 respectively. There was no risk associated with the age of the animals. The origin of the animals was a factor that reduced the risk for brucellosis in two models. The information provided in this study can support risk-based sampling for brucellosis vigilance and risk analysis.
DOI: 10.18168/bgi.24
title: Regional grids : Bay of Biscay, France

description: This page provides a package of 1'x1' resolution free-air gravity and geoid models determined at BGI (Vu et al.) for the Bay of Biscay (France). It includes the data derived from an airbone absolute gravity survey realized in Spring 2019 with the cold-atom gravity meter GIRAFE (Bidel et al., 2023). Quasigeoid computations are based on the remove-restore procedure, namely the Stokes integral using the Fast Fourier Transform approach and deterministic kernel modification proposed by Wong–Gore. The grid models resulting from the combination of heterogeneous airborne, ground & satellite data are performed as follows :

(1) 1'x1' Free-air gravity anomaly models :
-	GIRAFE merged Terrestrial gravity data : Free Air anomaly  is denoted as FABC_GIRAFE
-	DTU21 merged Terrestrial gravity data : Free Air anomaly is denoted as FABC_DTU21
-	UCSDv31 merged Terrestrial gravity data : Free Air anomaly is denoted as FABC_UCSDv31
-	Debiased shipborne merged Terrestrial gravity data : Free Air anomaly is denoted as FABC_shipborne(db).

(1) 1'x1' quasigeoid models :
-	GIRAFE merged Terrestrial gravity data : estimated quasigeoid denoted as QBC_GIRAFE
-	DTU21 merged Terrestrial gravity data : estimated quasigeoid denoted as QBC_DTU21
-	UCSDv31 merged Terrestrial gravity data : estimated quasigeoid denoted as QBC_UCSDv31
-	Shipborne without debiased merged Terrestrial gravity data : estimated quasigeoid denoted as QBC_shipborne
-	Debiased shipborne merged Terrestrial gravity data : estimated quasigeoid denoted as QBC_shipborne(db)
-	GIRAFE merged DTU21 removing the grid points within 10 km from coastline and Terrestrial gravity data : estimated quasigeoid denoted as QBC_GIRAFE_DTU21(r)
-	GIRAFE merged UCSDv31 removing the grid points within 10 km from coastline and Terrestrial gravity data : stimated quasigeoid denoted as QBC_GIRAFE_UCSDv31(r)
-	GIRAFE merged debiased shipborne and Terrestrial gravity data : estimated quasigeoid denoted as QBC_GIRAFE_shipborne(db)
DOI: 10.57745/beurvw
title: DisHiEd_Retranscript_RespServiceHandi_UB.docx

description: :unav
DOI: 10.57745/qsnuqy
title: DisHiEd_Retranscript_ChargéMission_UL.docx

description: :unav
DOI: 10.57745/0pz1ta
title: DisHiEd_Retranscript_RespServiceHandi_CYU.docx

description: :unav
DOI: 10.57745/fdufgg
title: DisHiEd_Retranscript_Etudiant_Master1_CYU.docx

description: :unav
DOI: 10.57745/hdoa8j
title: DisHiEd_Retranscript_Etudiant_Master2_CYU.docx

description: :unav
DOI: 10.57745/uoh1io
title: DisHiEd_Retranscript_CAA_UL.docx

description: :unav
DOI: 10.57745/ybmw6k
title: DisHiEd_Retranscript_RespServiceHandi_UPHF.docx

description: :unav
DOI: 10.57745/490dpq
title: DisHiEd_Retranscript_Etudiant_Licence1_CYU.docx

description: :unav
DOI: 10.57745/f1mgyc
title: DisHiEd_Retranscript_Médecin-directeur_UL.docx

description: :unav
DOI: 10.57745/en6jj3
title: DisHiEd_Retranscript_Etudiant_Licence2_CYU.docx

description: :unav
DOI: 10.57745/trbebq
title: DisHiEd_Retranscript_ChargéMission_CYU.docx

description: :unav
DOI: 10.57745/pvyyp0
title: DisHiEd_Retranscript_VP_UB.docx

description: :unav
DOI: 10.57745/boq8b3
title: DisHiEd_Retranscript_CAA_RespServiceHandi_UL.docx

description: :unav
DOI: 10.57745/8uwztu
title: DisHiEd_Retranscript_Médecin_UL.docx

description: :unav
DOI: 10.57745/s5uw9p
title: DisHiEd_Retranscript_Médecin-directeur_CYU.docx

description: :unav
DOI: 10.57745/yjqxfs
title: DisHiEd_Retranscript_Référent-local_UPHF.docx

description: :unav
DOI: 10.57745/oqsrvi
title: DisHiEd_Retranscript_CAA_UB.docx

description: :unav
DOI: 10.57745/tsgli4
title: DisHiEd_Retranscript_CAA_CYU.docx

description: :unav
DOI: 10.5281/zenodo.7642702
title: Water chemistry of LTER-Europe research site Lake Paione Superiore LTER_EU_IT_089 (1984-2013)

description: This dataset provides information about water chemical parameters for Lake Paione Superiore LTER_EU_IT_089: pH, Total alkalinity, conductivity, total nitrogen, major cations (calcium, magnesium, sodium, potassium), major anions (sulphate, nitrate, chloride) and silica for the period 1984-2013.

Lake Paione Superiore (LPS) is a high altitude Alpine lake, located at 2269 m a.s.l. in the Bognanco Valley, Province of Verbania, Piedmont Region, Italy. It has a surface area of 0.68 ha and a maximum depth of 11.5 m. The Lake, together with Lake Paione Inferiore (LPI), is included in the monitoring sites of the UN-ECE Program ICP WATERS (International Cooperative Programme on Assessment and Monitoring of Acidification of Rivers and Lakes) for which the CNR Water Research Institute is the National Focal Centre for Italy.

This dataset includes the following files: Metadata LTER_EU_IT_089.xls and per each parameter one xls file with data records.

Detailed description of the site LPS is available at https://deims.org/7e5837a9-ee27-4e27-822a-f50e5217c313

Dataset for water chemistry of LPS for the  period 2014-2020 is available at https://doi.org/10.5281/zenodo.10519126
description: The data collection was partly supported by the Italian Ministry for the Environment through the ICP Waters Programme.
To whom is interested in data of Lake Paione Inferiore LTER_EU_IT_088, we have published data of chemical parameters under DOI: 10.5281/zenodo.7642499
DOI: 10.5281/zenodo.7642703
title: Water chemistry of LTER-Europe research site Lake Paione Superiore LTER_EU_IT_089 (1984-2013)

description: This dataset provides information about water chemical parameters for Lake Paione Superiore LTER_EU_IT_089: pH, Total alkalinity, conductivity, total nitrogen, major cations (calcium, magnesium, sodium, potassium), major anions (sulphate, nitrate, chloride) and silica for the period 1984-2013.

Lake Paione Superiore (LPS) is a high altitude Alpine lake, located at 2269 m a.s.l. in the Bognanco Valley, Province of Verbania, Piedmont Region, Italy. It has a surface area of 0.68 ha and a maximum depth of 11.5 m. The Lake, together with Lake Paione Inferiore (LPI), is included in the monitoring sites of the UN-ECE Program ICP WATERS (International Cooperative Programme on Assessment and Monitoring of Acidification of Rivers and Lakes) for which the CNR Water Research Institute is the National Focal Centre for Italy.

This dataset includes the following files: Metadata LTER_EU_IT_089.xls and per each parameter one xls file with data records.

Detailed description of the site LPS is available at https://deims.org/7e5837a9-ee27-4e27-822a-f50e5217c313

Dataset for water chemistry of LPS for the  period 2014-2020 is available at https://doi.org/10.5281/zenodo.10519126
description: The data collection was partly supported by the Italian Ministry for the Environment through the ICP Waters Programme.
To whom is interested in data of Lake Paione Inferiore LTER_EU_IT_088, we have published data of chemical parameters under DOI: 10.5281/zenodo.7642499
DOI: 10.17632/8smwrc7dgz.1
title: Data for "Refining the roles of productivity, redox, and remineralization on the cadmium isotope composition of marine sediments"

description: This is accompanying data for the manuscript "Refining the roles of productivity, redox, and remineralization on the cadmium isotope composition of marine sediments"
DOI: 10.17632/8smwrc7dgz.2
title: Data for "Refining the roles of productivity, redox, and remineralization on the cadmium isotope composition of marine sediments"

description: This is accompanying data for the manuscript "Refining the roles of productivity, redox, and remineralization on the cadmium isotope composition of marine sediments"
DOI: 10.17632/g9rn83p7zd.1
title: Flow data for "Resilient anatomy and local plasticity of naïve and stress hematopoiesis "_9

description: Flow data for "Resilient anatomy and local plasticity of naïve and stress hematopoiesis "_9
DOI: 10.6084/m9.figshare.25004711
title: Pausing and reorienting behaviors enhance the performance of a spatial working memory task

description: Please cite these data and the articleGeorge, A. E., Stout, J. J., & Griffin, A. L. (2023). Pausing and reorienting behaviors enhance the performance of a spatial working memory task. <i>Behavioural Brain Research</i>, <i>446</i>, 114410.These are source and raw/processed data from the publication titled "Pausing and reorienting behaviors enhance the performance of a spatial working memory task". This includes IdPhi estimates, as well as position data used to estimate those values.To begin, load "zidphi_final.mat" into MATLAB. The variable "struct_allrats" contains raw and processed behavioral data, including zIdPhi (see Redish, 2016 Vicarious Trial and Error). The "struct_allrats" data are a structure array, where row represents rat and columns represent data or metadata.If trying to access x and y coordinate data (recorded using LEDs to track rat head movement) from the rat named "Baby Groot" (first row), you would write the following:x_data = struct_allrats(1).xPos;y_data = struct_allrats(1).yPos;The x_data and y_data variables are cell arrays in the same size as the number of sessions (struct_allrats(1).sessionfolder) and as such, x_data{1} is the x-coordinate data from rat #1 on session #1. The shape of x_data{1} represents the number of runs recorded from the delayed non-match to position task. So if length(x_data{1}) = 36, then we recorded 36 runs on the DNMP task. <b><i>This should not be confused with trials. </i></b>If you write the following in MATLAB: figure; plot(x_data{1}{1},y_data{1}{1}), you would plot the trajectory used (stem through T-junction run) for IdPhi estimation.For advanced indexing, you would use the struct_allrats(1).tsPos variable combined with struct_allrats(1).IntThe struct_allrats(1).Int file is used to get Timestamps, accuracy, and trajectory information from a session. struct_allrats(1).Int{1} is the Int file for the first session of the first rat in the structure array. Column #1 represents timestamps for stem entry, Column #5 represents T-junction entry, Column #6 represents T-junction exit, Column #2 is goal-zone entry, Column #7 is goal-arm entry, Column #8 is startbox entry.Accuracy information: Column #4 of the Int file. 0 = Correct. 1 = Incorrect.Trajectory information: Column #3 of the Int file. 1 = Left, 0 = Right (this may dependent on the dataset).Trials on the DNMP task consist of a sample run and choice run. If the size of the Int file is 36x8, that indicates 36 runs or 18 trials (1 sample, 1 choice). <b>Odd numbered rows are sample runs, even numbered rows are choice runs</b>.
DOI: 10.6084/m9.figshare.25004711.v1
title: Pausing and reorienting behaviors enhance the performance of a spatial working memory task

description: Please cite these data and the articleGeorge, A. E., Stout, J. J., & Griffin, A. L. (2023). Pausing and reorienting behaviors enhance the performance of a spatial working memory task. <i>Behavioural Brain Research</i>, <i>446</i>, 114410.These are source and raw/processed data from the publication titled "Pausing and reorienting behaviors enhance the performance of a spatial working memory task". This includes IdPhi estimates, as well as position data used to estimate those values.To begin, load "zidphi_final.mat" into MATLAB. The variable "struct_allrats" contains raw and processed behavioral data, including zIdPhi (see Redish, 2016 Vicarious Trial and Error). The "struct_allrats" data are a structure array, where row represents rat and columns represent data or metadata.If trying to access x and y coordinate data (recorded using LEDs to track rat head movement) from the rat named "Baby Groot" (first row), you would write the following:x_data = struct_allrats(1).xPos;y_data = struct_allrats(1).yPos;The x_data and y_data variables are cell arrays in the same size as the number of sessions (struct_allrats(1).sessionfolder) and as such, x_data{1} is the x-coordinate data from rat #1 on session #1. The shape of x_data{1} represents the number of runs recorded from the delayed non-match to position task. So if length(x_data{1}) = 36, then we recorded 36 runs on the DNMP task. <b><i>This should not be confused with trials. </i></b>If you write the following in MATLAB: figure; plot(x_data{1}{1},y_data{1}{1}), you would plot the trajectory used (stem through T-junction run) for IdPhi estimation.For advanced indexing, you would use the struct_allrats(1).tsPos variable combined with struct_allrats(1).IntThe struct_allrats(1).Int file is used to get Timestamps, accuracy, and trajectory information from a session. struct_allrats(1).Int{1} is the Int file for the first session of the first rat in the structure array. Column #1 represents timestamps for stem entry, Column #5 represents T-junction entry, Column #6 represents T-junction exit, Column #2 is goal-zone entry, Column #7 is goal-arm entry, Column #8 is startbox entry.Accuracy information: Column #4 of the Int file. 0 = Correct. 1 = Incorrect.Trajectory information: Column #3 of the Int file. 1 = Left, 0 = Right (this may dependent on the dataset).Trials on the DNMP task consist of a sample run and choice run. If the size of the Int file is 36x8, that indicates 36 runs or 18 trials (1 sample, 1 choice). <b>Odd numbered rows are sample runs, even numbered rows are choice runs</b>.
DOI: 10.5281/zenodo.7478230
title: Nepal Uber H3 level 6 database

description: Database for characterization of sub national units for Nepal using Uber H3 level 6 hexagons. Data includes crop, livestock, population a well as climate data. H3 hexagons have unique identifiers that can be used to sum or average underlying data covered by area. As compared to square pixels, hexagons are better to fully cover a countries area. The lower the level used the better the fit.

Data sources used:

Livestock: FAO GLW (Cattle, Goats, Sheep, Chicken, Buffaloes) year 2015

Population Meta for Good 2018

Crops: IFPRI SPAM 2010 (

Climate: Worldclim 2.1

Elevation: SRTM CSI CGIAR




buffalo2015
 
Buffaloes


cattle2015
 
Cattle


chicken2015
 
Chicken


sheep2015
 
Sheep


goats2015
 
Goats


hareabana
 
banana area ha


hareabanaplnt
 
banana plantan area ha


hareabarl
 
barley area ha


hareabean
 
bean area ha


hareachic
 
chickpea area ha


hareafoodcrp
 
foodcrops area ha


harealent
 
lentil area ha


hareamaiz
 
maize area ha


hareaocer
 
other cereals area ha


hareaopul
 
other pulses area ha


hareaorts
 
other roots and tubers area ha


hareapige
 
pigeonpea area ha


hareapota
 
potato area ha


harearice
 
rice area ha


hareasmil
 
millet area ha


hareasoyb
 
soybean area ha


hareawhea
 
wheat area ha


precann
 
annual rainfall


tmax_ann
 
longterm annual tmax (1970-2000)


tmin_ann
 
longterm annual tmin (1970-2000)


world250meter
 
Elevation m a.b.s.l.




Caveats:

Hexagon size can be smaller than actual pixel size like SPAM, values can be lower or higher depending on location and pixel below. Use as indicator of importance not absolute value.

 
description: Can be visualized at: 
https://csi.maps.arcgis.com/apps/mapviewer/index.html?layers=18a1243889a646268e5b27e454968f3e
DOI: 10.5281/zenodo.7478231
title: Nepal Uber H3 level 6 database

description: Database for characterization of sub national units for Nepal using Uber H3 level 6 hexagons. Data includes crop, livestock, population a well as climate data. H3 hexagons have unique identifiers that can be used to sum or average underlying data covered by area. As compared to square pixels, hexagons are better to fully cover a countries area. The lower the level used the better the fit.

Data sources used:

Livestock: FAO GLW (Cattle, Goats, Sheep, Chicken, Buffaloes) year 2015

Population Meta for Good 2018

Crops: IFPRI SPAM 2010 (

Climate: Worldclim 2.1

Elevation: SRTM CSI CGIAR




buffalo2015
 
Buffaloes


cattle2015
 
Cattle


chicken2015
 
Chicken


sheep2015
 
Sheep


goats2015
 
Goats


hareabana
 
banana area ha


hareabanaplnt
 
banana plantan area ha


hareabarl
 
barley area ha


hareabean
 
bean area ha


hareachic
 
chickpea area ha


hareafoodcrp
 
foodcrops area ha


harealent
 
lentil area ha


hareamaiz
 
maize area ha


hareaocer
 
other cereals area ha


hareaopul
 
other pulses area ha


hareaorts
 
other roots and tubers area ha


hareapige
 
pigeonpea area ha


hareapota
 
potato area ha


harearice
 
rice area ha


hareasmil
 
millet area ha


hareasoyb
 
soybean area ha


hareawhea
 
wheat area ha


precann
 
annual rainfall


tmax_ann
 
longterm annual tmax (1970-2000)


tmin_ann
 
longterm annual tmin (1970-2000)


world250meter
 
Elevation m a.b.s.l.




Caveats:

Hexagon size can be smaller than actual pixel size like SPAM, values can be lower or higher depending on location and pixel below. Use as indicator of importance not absolute value.

 
description: Can be visualized at: 
https://csi.maps.arcgis.com/apps/mapviewer/index.html?layers=18a1243889a646268e5b27e454968f3e