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hybrid · semantic + lexical · 2 datasets ranked · 0.36s

Structuretabular2
Depthmeasured2
Licensenon commercial2
Accessopen2
Formatcsv1
Sourcezenodo2
clear
1-2 of 2sortrelevancemeasured firstqualitysize
tabular

Dataset Priming against Salmonella enterica affects differentially haemocyte sub-populations in Armadillidium vulgare

0.00

Pailler, Louis

207 rows × 1 cols · 22 KB · csv

1 text

These datasets were collected using a total of 585 Armadillidium vulgare females. The objective of this study was to measure survival after immune priming with Salmonella enterica , using different doses and inactivation methods, and to characterise the underlying cellular mechanisms using flow cytometry. Survival analysis according to S. enterica method of inactivation and dosage Survival after injection of a lethal dose of live S. enterica was measured after immune priming using bacteria inactivated by heat (HK) or paraformaldehyde (PFA) and two different doses (10^3 and 10^6). This allowed to collect the Dataset_survival.csv, analysed using the Script_survival_analysis.R. Dataset_survival.csv: Ind: individual identification Treatment: priming treatment that females received. C : control females, no priming injection. PBS : females primed with sterile PBS. PFA3 / PFA6 : females primed with 10^3 or 10^6 PFA-inactivated S. enterica . HK3 / HK6 : females primed with 10^3 or 10^6 heat-inactivated S. enterica . Repl: experimental replicate Status: 1 = dead, 0 = live Survival : Time at death. 168 indicates living females at the end of the experiment Time: Time elapsed between the two injections. T24 : 24hours. T7 : 7 days Haemocyte sub-populations analysis according to priming treatment Following the survival experiment, and because the A. vulgare primed with PFA6 inactivated S. enterica exhibited the highest survival rates against LD50 infection, this inactivated method and dosage were used to examine the haemocyte sub-populations of A. vulgare mounting immune priming. Haemolymph was sampled from all females (C, PBS, PFA6) either 2 days (2D-PP) or 6 days (6D-PP) after the initial priming injection, or 2 days after the LD50 injection (2D-LD50). Distinct sets of females were used for each time point. This allowed to generate the Dataset_cytometry.csv, analysed using the Script_cytometry_pca_analysis.R. Principal Component Analysis (PCA) for each observation time allowed to extract projection values of the four PCs for each individual (Dataset_pca_ind_coord.csv). Dataset_cytometry.csv: Ind: individual identification Treatment: priming treatment that females received. C: control females, no priming injection. PBS: females primed with sterile PBS. PFA6: 10^6 PFA-inactivated S. enterica . Repl: experimental replicate Box: experimental box Exp: experimental day Time: Sampling time. 2D-PP: 2-days after priming. 6D-PP: 6-days after the priming injection. 2D-LD50: 2 days after the LD50 infection. P1_percent: Percentage of the first population. P1_FSCA: Cell diameter (size) of the first population. P1_SSCA: Internal complexity (internal granularity) of the first population. Viab_P1: Viability of the first population. P2_percent: Percentage of the second population. P2_FSCA: Cell diameter (size) of the second population. P2_SSCA: Internal complexity (internal granularity) of the second population. Viab_P2: Viability of the second population. Dataset_pca_ind_coord .csv: Ind: individual identification Treatment: priming treatment that females received. C: control females, no priming injection. PBS: females primed with sterile PBS. PFA6: 10^6 PFA-inactivated S. enterica . Repl: experimental replicate Box: experimental box Exp: experimental day Time: Sampling time. 2D-PP: 2-days after priming. 6D-PP: 6-days after the priming injection. 2D-LD50: 2 days after the lethal dose infection. Dim.1: projection values on the first principal component (PC1) for each individual. Dim.2: projection values on the second principal component (PC2) for each individual. Dim.3: projection values on the third principal component (PC3) for each individual. Dim.4: projection values on the fourth principal component (PC4) for each individual.

open·CC-BY-NC-4.0·Zenodo·0% null·completeSource
tabular

Dataset for the Study of Urban Morphology and Morning Traffic Congestion in Beer Sheva

0.00

Friedman, Gil

9,581 rows × 13 cols · 1.0 MB

8 numeric · 4 categorical · 1 datetime

This dataset was collected as part of a seminar research project at Tel Aviv University examining the relationship between urban morphology and morning traffic congestion in residential neighborhoods in Beer Sheva, Israel. The dataset combines street network metrics derived from OpenStreetMap using OSMnx with traffic measurements collected from the Google Maps API. Variables include network connectivity measures, topological indicators, demographic characteristics, and congestion metrics calculated from observed travel times during morning peak hours. Traffic data collection was performed using the Google Maps Traffic Scraper (gmaps-scraper): https://github.com/gilfriedman/gmaps-scraper Street network extraction, neighborhood exit identification, and network analysis were performed using the Traffic Network Analyzer: https://github.com/gilfriedman/traffic-network-analyzer The dataset was created for the research project "Urban Morphology and Morning Traffic Congestion in Beer Sheva" conducted within the Smart Cities seminar course at Tel Aviv University.

open·CC-BY-NC-4.0·Zenodo·0% null·completeSource

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