Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

12,683

datasets available to search

ShareScore release 0.9.0

Dataset results

12,683 results for “tolerability”

Learn how ShareScore rates datasets ↗

zenodo36/100

Scanning electron microscopy (SEM) images of single bacterial-Pb tolerant isolates

In-Vitro Trace Element Uptake by Bacteria in Liquid Cultures-Scanning Electron Microscopy (SEM-EDX) Analysis Batch studies were conducted using 50 mL Falcon tubes that contained 25 mL GY rich medium supplemented with 100 mg L-1 of Pb (Pb(NO3)2). All tubes were inoculated with 1 mL of a bacterial cell suspension (optical density at 600 nm = 1), which was previously grown in GY medium at 28 °C for 24 h. Samples were incubated at 28 °C and 100 rpm on an orbital shaker. In order to estimate Pb sequestration by bacterial cells, cultures were first centrifuged 15 min at 4800 rpm, bacterial pellets were washed 3 times with 0.01 M phosphate-buffer (PBS, pH 7.0) to remove unbound metals, sugars and proteins. The samples were dehydrated with ethanol/water mixtures, until absolute ethanol. Then 100% acetone was used. Pellets were resuspended in 2% glutaraldehyde for 1h at room temperature. Afterwards, bacterial samples were centrifuged for 3 min at 3000 rpm and pellets were washed 3 times with milli-Q water. One uL of sample was placed on a sample holder, in carbon conductive tape. Then, samples were coated 30 s with a 15 nm gold layer and analysed using a Scanning Electron Microscope (Phenom™ ProX Desktop, Thermofisher). Images were taken using an accelerating voltage of 15 kV. Bacillus paramycoides ST4, Bacillus wiedmannii ST29 and Bacillus proteolyticus ST9 images obtained by SEM-EDX are presented in this dataset. Pb was found present in/on the bacterial cell wall of these bacteria cultures and detected by EDX spectra.

opencc-by-4.0May 2024View details →

dryad36/100

Data from: dispersal restriction and facilitation in species with differing tolerance to development: a landscape genetics study of native and introduced lizards

Aim: The development of natural habitats into urban land uses has greatly accelerated in the recent past due to human activities. This habitat development disrupts species' natural dispersal processes and can lead to both direct and indirect impacts on dispersal. Whether human activities result in restricted or facilitated dispersal may depend on a species' development tolerance; however, this premise has not been tested. We examined the impact of urbanization and road networks on the dispersal of three lizard species in the context of their development tolerance. Location: Curaçao Methods: To quantify species' development tolerance, we modelled three lizard species abundances at sites based on surrounding landscape development. Using microsatellite genotypes, we conducted individual-based resistance surface analyses and modelled the effect of habitat development on genetic admixture to assess indirect dispersal restriction and facilitation. We explored direct facilitation of dispersal using network analysis of mitochondrial haplotypes. Results: Phyllodactylus martini, a native gecko species, was the least tolerant of development and experienced indirect dispersal restriction due to roads, according to resistance surface analyses. Anolis lineatus, a native anole species, exhibited a neutral relationship with development. Resistance surfaces and Structure analyses showed that A. lineatus faced indirect dispersal restrictions from roads and developed areas, while mitochondrial haplotype networks suggested they benefited from occasional human-facilitated long-distance dispersal events. Hemidactylus mabouia, an introduced gecko species, was the most tolerant of development, and experienced no dispersal restriction, but mitochondrial haplotypes suggest direct long-distance dispersal facilitation. Main conclusions: Our findings highlight development tolerance as a key predictor of dispersal impact for these species and future work should test whether these patterns are upheld in other systems. Understanding how human activities affect species' dispersal will aid in managing introduced species while promoting connectivity for native species navigating dispersal challenges in dynamic landscapes.

opencc-zeroMay 2024View details →

dryad36/100

Quantifying intraspecific variation in host resistance and tolerance to a lethal pathogen

Testing for intraspecific variation for host tolerance or resistance in wild populations is important for informing conservation decisions about captive breeding, translocation, and disease treatment. Here, we test the importance of tolerance and resistance in multiple populations of boreal toads (Anaxyrus boreas boreas) against Batrachochytrium dendrobatidis (Bd), the amphibian fungal pathogen responsible for the greatest host biodiversity loss due to disease. Boreal toads have severely declined in Colorado (CO) due to Bd, but toad populations challenged with Bd in western Wyoming (WY) appear to be less affected. We used a common garden infection experiment to expose post-metamorphic toads sourced from four populations (2 in CO and 2 in WY) to Bd and monitored changes in mass, pathogen burden, and survival for eight weeks. We used a multi-state modeling approach to estimate weekly survival and transition probabilities between infected and cleared states, reflecting a dynamic infection process that traditional approaches fail to capture. We found that WY boreal toads are more tolerant to Bd infection with higher survival probabilities than those in CO when infected with identical pathogen burdens. WY toads also appeared more resistant to Bd with a higher probability of infection clearance and an average of five days longer to reach peak infection burdens. Our results demonstrate strong intraspecific differences in tolerance and resistance that likely contribute to why population declines vary regionally across this species. Our multi-state framework allowed us to gain inference on typically hidden disease processes when testing for host tolerance or resistance. Our findings demonstrate that describing an entire host species as 'tolerant' or 'resistant' (or lack thereof) is unwise without testing for intraspecific variation.

opencc-zeroMay 2024View details →

dryad36/100

Dataset and scripts from: Predicting organismal response to marine heatwaves using dynamic thermal tolerance landscape models

Marine heatwaves (MHWs) can cause thermal stress in marine organisms, experienced as extreme 'pulses' against the gradual trend of anthropogenic warming. When thermal stress exceeds organismal capacity to maintain homeostasis, organism survival becomes time-limited and can result in mass mortality events. Current methods of detecting and categorizing MHWs rely on statistical analysis of historic climatology, and do not consider biological effects as a basis of MHW severity. The reemergence of ectotherm thermal tolerance landscape models provides a physiological framework for assessing the lethal effects of MHWs by accounting for both the magnitude and duration of extreme heat events. Here, we used a simulation approach to understand the effects of a suite of MHW profiles on organism survival probability across 1) three thermal tolerance adaptive strategies, 2) interannual temperature variation, and 3) seasonal timing of MHWs. We identified survival isoclines across MHW magnitude and duration where acute (short duration-high magnitude) and chronic (long duration-low magnitude) events had equivalent lethal effects on marine organisms. While most research attention has focused on chronic MHW events, we show similar lethal effects can be experienced by more common but neglected acute marine heat spikes. Critically, a statistical definition of MHWs does not accurately categorize biological mortality. By letting organism responses define the extremeness of a MHW event, we can build a mechanistic understanding of MHW effects from a physiological basis. Organism responses can then be transferred across scales of ecological organization and better predict marine ecosystem shifts to MHWs.

opencc-zeroMay 2024View details →

zenodo36/100

Data from: Frequency-dependent tolerance to aircraft disturbance drastically alters predicted impact on shorebirds

This data package includes data and R script belonging to the publication "Frequency-dependent tolerance to aircraft disturbance drastically alters predicted impact on shorebirds" by van der Kolk et al. (2024) in Ecology Letters. The script analysis_figures_vanderKolketal2024.R can be used to reproduce all analysis and figures in the manuscript. The Figures are stored in the Output folder. The data folder includes an excel file with metadata explaining all columns in the csv files.

opencc-by-4.0May 2024View details →

zenodo36/100

StArt Protocol for systematic review: Use of CRISPR technology in gene editing for tolerance to biotic factors in plants: A systematic review.

StArt Protocol  for systematic review: Use of CRISPR technology in gene editing for tolerance to biotic factors in plants: A systematic review.

opencc-by-4.0May 2024View details →

dryad36/100

Exposure to teflubenzuron reduces drought tolerance of collembolans

This is the raw data from an experiment investigating the effects of sequential exposure to a pesticide (teflubenzuron) and three abiotic stressors (heat, cold, and drought) on soil arthropods. We exposed adult collembolans (Folsomia candida) to teflubenzuron through food for two weeks. Then survivors were immediately divided into three groups for acute heat, cold, and drought exposure. After acute exposure to these natural stressors, the collembolans were moved to optimal conditions for a one-week recovery period during which their survival, time to regain reproduction, and egg production were examined.

opencc-zeroJun 2024View details →

zenodo36/100

Figure 2 in Response and tolerance mechanism of food crops under high temperature stress: a review

Figure 2. Schematic representation of tolerance mechanism of plants during heat stress.

opencc-by-4.0Dec 2022View details →

dryad36/100

No evidence of adaptive tolerance of parasitism in a cavity-nesting brood parasite host

Acceptance of avian brood parasitism by hosts is one of the most enigmatic aspects of brood parasite-host coevolution. The most common explanation for acceptance of parasitism by hosts of the brown-headed cowbird (Molothrus ater) is evolutionary lag, which suggests that hosts have not had enough time to evolve defenses against parasitism. Alternatively, acceptance may be the optimal strategy when the costs of rejecting parasitism exceed the benefits. The lack of nest site hypothesis applies to secondary cavity-nesting birds that cannot excavate their own nests and predicts that hosts accept parasitism instead of deserting a parasitized nest when there are no vacant nest sites available in which to renest. I tested this hypothesis using the prothonotary warbler (Pronotaria citrea), a commonly parasitized, cavity-nesting cowbird host. I used a paired nest box design and predicted that if hosts accept parasitism because of a lack of alternative nest sites, they should desert parasitized nests and renest in the vacant nest box on their territory. I recorded 37 cases where a nest was parasitized and warblers only deserted 2 parasitized nest boxes for a vacant nest box. Both desertions were attributable to factors other than parasitism and the rate of desertion did not differ from controls that only had a single nest box. Moreover, seven of the warblers initiated clutches in nest boxes that already contained cowbird egg despite having vacant nest boxes available on their territories. These results indicate that warblers do not accept parasitism because of tolerance, but likely due to evolutionary lag.

opencc-zeroJul 2024View details →

zenodo36/100

Fig. 1. A in Tolerance to Anhydrobiotic Conditions Among Two Coexisting Tardigrade Species Differing in Life Strategies.

Fig. 1. A simple schematic illustration of the experiments.

opencc-by-4.0Dec 2021View details →

zenodo36/100

Untargeted mutagenesis of brassinosteroid receptor SbBRI1 confers drought tolerance by altering phenylpropanoid metabolism in Sorghum bicolor

Metabolomics data from the study: Untargeted mutagenesis of brassinosteroid receptor SbBRI1 confers drought tolerance by altering phenylpropanoid metabolism in Sorghum bicolor. Files are raw chromatograms. Metabolite profiling analysis Metabolite Extraction and Quantification. The whole metabolite extraction and quantification pipeline followed the guidelines as described in Giavalisco et al. (2011) and Salem et al. (2020). Briefly, 10-25 mg of fresh plant tissue was harvested and immediately frozen in N2, and ground using zirconia beads with the help of Tissue Lyser Mixer-Mill (Qiagen) 3 min at 25 Hz. After samples were extracted in 100% cold methanol, they were centrifuged for 10 min at 14000 rpm (Room temperature) and 3:1 chloroform was added. After vortexing thoroughly, 1:1 volume of water was added and the samples were subsequently vortexed and centrifuged. The semi-polar phase was used for analysis of semi-polar secondary and primary metabolites. For secondary semi-polar metabolites, the dried polar aliquots were resuspended in water:methanol (1:1 v/v). 3 Analysis of semi-polar metabolites was performed using a Thermo Q Exactive Focus coupled to a reverse-phase C18 column. The column was maintained at 40°C with a flow rate of 400 μl/min, and the eluent system consisted of water (eluent A) and acetonitrile (eluent B), both containing 0.1% formic acid. Mass spectra were acquired in full scan mode over a range of 100-1500 m/z using data-independent acquisition (DIA) with high-energy collisional dissociation (HCD) energy set at 30 eV in positive mode. 9 For primary metabolite metabolites, the dried polar was derivatized as described in Lisec et al. (2006). Derivatization was carried out at 37°C for 120 min using 40 μl of 20 mg/ml methoxyamine hydrochloride in pyridine, followed by a 30-min treatment at 37°C with 70 μl of trimethylsilyl-N-methyl trifluoroacetamide (MSTFA). The derivatized samples (1 μl) were injected in splitless mode into a gas chromatograph coupled to a time-of-flight mass spectrometer (Pegasus HT TOF-MS). Gas chromatography was performed on a 30-m DB-35 column using helium as the carrier gas. The initial temperature of the oven was 85°C, and it was ramped up at a rate of 15°C/min to a final temperature of 360°C. Mass spectra were recorded in the range of 70-600 m/z at a rate of 20 scans/s. Data Processing and Compound Annotation. LC-MS full scan data were processed using MS Refiner (Expressionist 14.0). Processing of chromatograms, peak detection, and integration were performed using RefinerMS (version 5.3; GeneData). Metabolite identification and annotation were performed using in-house reference compound library, tandem MS (MS/MS) 22 fragmentation, and metabolomics databases (Alseekh et al., 2021). For the annotation of metabolites measured by GC-MS the Golm Metabolome Database was used (Kopka et al., 2005). <div> <div> #heatmap of fold changes  </div> <div> ts <-read.csv("all_log2fc_heat_all.csv", sep=";", fileEncoding='latin1',check.names=F, header=TRUE)  </div> <div> ts1 <- ts  </div> <div> ts1$class <- NULL   </div> <div>   </div> <div> row.names(ts1) <- ts1$compound  </div> <div> ts1$compound <- NULL  </div> <div> m <- as.matrix(ts1)  </div> <div>   </div> <div> ann <- ts[1:2]  </div> <div> row.names(ann) <- ann$compound  </div> <div> ann$compound <- NULL  </div> <div>   </div> <div> install.packages("pheatmap")  </div> <div>   </div> <div> library("pheatmap")  </div> <div>   </div> <div> m1 <- m[rownames(ann), ]  </div> <div>   </div> <div> pheatmap(m1, annotation_row = ann, cluster_rows = F,cluster_cols = F, cellheight = 10,   </div> <div>          cellwidth = 10, gaps_row = cumsum(c(3,105,30,11,21,4,32,5,17)), gaps_col = cumsum(c(2,2,2)), scale = 'none')  </div> <div>   </div> <div> #boxplots  </div> <div> install.packages("ggplot2")  </div> <div> install.packages("readr")  </div> <div>   </div> <div> library(ggplot2)  </div> <div> library(readr)  </div> <div>   </div> <div> install.packages("tidyverse")  </div> <div> library(tidyverse)  </div> <div> grid <- read.csv("sec_exp1_logfc_20.csv", sep=";", header=TRUE)  </div> <div>   </div> <div> grid_g <- grid%>% gather("compound", "value", 6:25)  </div> <div>   </div> <div> d=ggplot(data = grid_g, aes(x=compound, y=value, fill=factor(group)))+  </div> <div>   geom_boxplot(outlier.shape = NA)+  </div> <div>   geom_point(position = position_jitterdodge(0.1))  </div> <div>   </div> <div> d+theme(text = element_text(size = 15))+  </div> <div>   scale_fill_manual(values = c("#ca5826","#e4ab92", "#008080", "#99cccc"))+  </div> <div>   labs(y="log2fc(metabolite content)",fill="gtype")+  </div> <div>   theme(axis.text.x = element_text(angle = 60, hjust = 1, size = 5)) +   </div> <div>   theme_classic() +  </div> <div>   coord_flip() +  </div> <div>   facet_grid(condition ~ .)  </div> <div>   </div> <div> ggsave("logfcsum_exp1_15_bri1.png", width = 10, height = 10)  </div> <div>   </div> <div> #boxplots all data  </div> <div> library(ggplot2)  </div> <div> library(readr)  </div> <div> library(viridis)  </div> <div> library(tidyverse)  </div> <div> library(dplyr)  </div> <div>   </div> <div> sPCA <-read.csv("GCMS_polar.csv", sep=",", fileEncoding='latin1', check.names = F, header=TRUE)  </div> <div>   </div> <div> row.names(sPCA) <- sPCA$Sample  </div> <div> sPCA_D <- sPCA[6:155]  </div> <div> sPCA_Dlog <- log(sPCA_D)  </div> <div> sPCA_Dlog10 <- log10(sPCA_D)  </div> <div>   </div> <div> sPCA_Dlog <- cbind(sPCA_Dlog, group = sPCA$Sample_group)  </div> <div> sPCA_Dlog <- sPCA_Dlog%>% select(group, everything())  </div> <div>   </div> <div> sPCA_Dlog <- cbind(sPCA_Dlog, sample = sPCA$Sample)  </div> <div> sPCA_Dlog <- sPCA_Dlog%>% select(sample, everything())  </div> <div>   </div> <div> write.csv(sPCA_Dlog10, "GCMS_polar_lg.csv")  </div> <div>   </div> <div> sPCA_Dlog_g <- sPCA_Dlog%>% gather("compound", "value", 3:152)  </div> <div>   </div> <div> d <- ggplot(sPCA_Dlog_g, aes(x=sample, y=value, fill=factor(group)))+  </div> <div>   geom_boxplot(outlier.shape = NA)+  </div> <div>   geom_point(position = position_jitterdodge(0.1))  </div> <div>   </div> <div> d + theme(text = element_text(size = 15))+  </div> <div>   scale_fill_manual(values = c("#CC6677","#882255","#44AA99", "#117733"))+  </div> <div>   labs(y="lg(normalized metabolite content)", x= "sample" , fill="group")+  </div> <div>   theme(axis.text.x = element_text(angle = 60, hjust = 1)) +   </div> <div>   theme_classic() +  </div> <div>   geom_hline(yintercept=0, linetype="dashed", color = "black")  </div> <div> #PCA  </div> <div> install.packages("plotly")  </div> <div> install.packages("ggfortify")  </div> <div> library(plotly)  </div> <div> library(ggfortify)  </div> <div>   </div> <div> sPCA <-read.csv("prim.csv", sep=";", header=TRUE)  </div> <div>   </div> <div> sPCA <- subset(sPCA, experiment!="2")  </div> <div>   </div> <div> sPCAtd <- all_d[7:240]  </div> <div> sPCAtdn <- sPCAtd[-c(24), ]  </div> <div> sPCAn <- sPCA[-c(24), ]  </div> <div>   </div> <div> #normal  </div> <div>   </div> <div> sec.pcat <- prcomp(sPCAtd, center = TRUE,scale. = TRUE)  </div> <div>   </div> <div> summary(sec.pcat)  </div> <div>   </div> <div> p <- autoplot(sec.pcat, data = all_d, colour = 'condition', shape = 'Genotype') +   </div> <div>   geom_text(aes(label = Replicate), nudge_y = 0.02) +  </div> <div>   theme_classic() +  </div> <div>   scale_color_manual(values=c("#56B4E9", "#E69F00"))  </div> <div>   </div> <div> ggplotly(p)  </div> <div> #experiment wise fold changes  </div> <div> write.csv(sec_fc_mean, "sec_foldchange_mean.csv")  </div> <div>   </div> <div> sec_fc_exp1 <- sect[1]  </div> <div>   </div> <div> sec_fc_exp1$bri1.drought <- foldchange(sect$X72.bri1.drought, sect$median.WT.c1)  </div> <div> sec_fc_exp1$bri1.drought1 <- foldchange(sect$X72.bri1.drought.1, sect$median.WT.c1)  </div> <div> sec_fc_exp1$bri1.drought2 <- foldchange(sect$X72.bri1.drought.2, sect$median.WT.c1)  </div> <div>   </div> <div> sec_fc_exp1$bri1.control <- foldchange(sect$X72.bri1.control, sect$median.WT.c1)  </div> <div> sec_fc_exp1$bri1.control1 <- foldchange(sect$X72.bri1.control.1, sect$median.WT.c1)  </div> <div> sec_fc_exp1$bri1.control2 <- foldchange(sect$X72.bri1.control.2, sect$median.WT.c1)  </div> <div>   </div> <div> sec_fc_exp1$WT.drought <- foldchange(sect$X72.WT..drought, sect$median.WT.c1)  </div> <div> sec_fc_exp1$WT.drought1 <- foldchange(sect$X72.WT..drought.1, sect$median.WT.c1)  </div> <div> sec_fc_exp1$WT.drought2 <- foldchange(sect$X72.WT..drought.2, sect$median.WT.c1)  </div> <div>   </div> <div> sec_fc_exp1$WT.control <- foldchange(sect$X72.WT..control, sect$median.WT.c1)  </div> <div> sec_fc_exp1$WT.control1 <- foldchange(sect$X72.WT..control.1, sect$median.WT.c1)  </div> <div> sec_fc_exp1$WT.control2 <- foldchange(sect$X72.WT..control.2, sect$median.WT.c1)  </div> <div>   </div> <div>   </div> <div> write.csv(sec_fc_exp1, "sec_fc_exp1.csv")  </div> <div>   </div> <div> #all compounds for RNAsq comarision  </div> <div>   </div> <div> all_R <-read.csv("all_RNAsq.csv", sep=";", fileEncoding='latin1',check.names=F, header=T)  </div> <div>   </div> <div> all_fc_exp2 <- all_R[1]  </div> <div>   </div> <div> all_fc_exp2$drought <- foldchange(all_R$bri1_drought, all_R$WT_drought)  </div> <div>   </div> <div> all_fc_exp2$control <- foldchange(all_R$bri1_control, all_R$WT_control)  </div> <div>   </div> <div> write.csv(all_fc_exp2, "all_foldchange_exp2.csv")  </div> <div>   </div> <div> #students t test  </div> <div>   </div> <div> secfc_2_t <-read.csv("sec_exp1_logfc.csv", sep=";", fileEncoding='latin1',check.names=F, header=T)  </div> <div>   </div> <div> secfc_2_t <- secfc_2_t[-2]  </div> <div> secfc_2_t <- secfc_2_t[-(14:25)]  </div> <div>   </div> <div> st2<- secfc_2_t %>% gather("compound", "value", 6:171)  </div> <div> stat.test1 <- st2 %>%  </div> <div>   group_by(compound) %>%  </div> <div>   t_test(value ~ group)  </div> <div> add_significance()  </div> <div> stat.test1  </div> </div>

opencc-by-4.0Jul 2024View details →

zenodo36/100

Investigating Temperature Tolerance in Mosquito Disease Vectors Across a Land-Use Gradient

Description: Mosquito larval survey and thermotolerance dataProject: This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/172">Investigating Temperature Tolerance in Mosquito Disease Vectors Across a Land-Use Gradient</a>XML metadata: GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=82">here</a>Data worksheets: There are 4 data worksheets in this dataset:<ol><li>Field microclimate data (Worksheet Microclimate)Dimensions: 6917 rows by 11 columnsDescription: Data was recorded using EasyLog USB dataloggers. They were put out either hung on a tree, or suspended off the ground (in the absense of trees) and covered to avoid direct sunlight.Fields: <ul><li>Block: SAFE Project sampling block (Field type: Location)</li><li>Plot: Sample site (Field type: Location)</li><li>Site: SAFE Project sampling block (Field type: ID)</li><li>Point: SAFE Project sample location (Field type: ID)</li><li>DATE: Date of the measurement (Field type: Date)</li><li>TIME: Time of the measurement (Field type: Time)</li><li>Temp: Air temperature (Field type: Numeric)</li><li>RelHumid: Relative humidity (Field type: Numeric)</li><li>DewPoint: The temperature at which the air would condense and dew would form (Field type: Numeric)</li><li>SerialNumber: Serial number of the microclimate datalogger used to collect the data (Field type: ID)</li></ul> </li><li>Thermal tolerance (Worksheet CTmax)Dimensions: 317 rows by 6 columnsDescription: Thermal tolerance experiments on mosquito larvaeFields: <ul><li>Date: The Date the CT max value was taken (Field type: Date)</li><li>VialNumber: the vial that the individual came from, and how it is referred to in my field notebook (Field type: ID)</li><li>LandType: Habitat type from which the individual was collected (Field type: Categorical)</li><li>CriticalMax: The temperature in celcius that the individual became unresponsive to stimulus (Field type: Numeric)</li><li>GivenSpecies: Identity of the individual being tested (Field type: Taxa)</li></ul> </li><li>Site x species data (Worksheet SpeciesData)Dimensions: 192 rows by 9 columnsDescription: Field observations of field mosquito communitiesFields: <ul><li>Block: SAFE Project sampling block (Field type: Location)</li><li>Plot: Sample site (Field type: Location)</li><li>Site: SAFE Project sampling block (Field type: ID)</li><li>Point: SAFE Project sample site (Field type: ID)</li><li>Replicate: is the replicate of sampling each data collection took place. There were three weeks of sampling so the only values are 1, 2, or 3. (Field type: Replicate)</li><li>CollectionType: Method of collection (Field type: Categorical)</li><li>Count: Total number of individuals sampled (Field type: Abundance)</li><li>GivenSpecies: Identity of the individual(s) (Field type: Taxa)</li></ul> </li><li>Forest canopy measurements (Worksheet Densiometer)Dimensions: 36 rows by 12 columnsDescription: Densiometer estimates of tree canopy coverFields: <ul><li>Block: SAFE Project sampling block (Field type: Location)</li><li>Plot: Sample site (Field type: Location)</li><li>Site: SAFE Project sampling block (Field type: ID)</li><li>Point: SAFE Project sample site (Field type: ID)</li><li>Val1: Number of quartersquares that lack canopy cover in one of the four cardinal directions. The maximum value is 96 if there is no canopy cover. (Field type: Numeric)</li><li>Val2: Number of quartersquares that lack canopy cover in one of the four cardinal directions. The maximum value is 96 if there is no canopy cover. (Field type: Numeric)</li><li>Val3: Number of quartersquares that lack canopy cover in one of the four cardinal directions. The maximum value is 96 if there is no canopy cover. (Field type: Numeric)</li><li>Val4: Number of quartersquares that lack canopy cover in one of the four cardinal directions. The maximum value is 96 if there is no canopy cover. (Field type: Numeric)</li><li>AveVal: Average densiometer reading (max = 96) (Field type: Numeric)</li><li>AdjustedVal: Average canopy openness (Field type: Numeric)</li><li>CanopyCover: Average canopy cover (Field type: Numeric)</li></ul> </li></ol>Date range: 2017-03-20 to 2017-09-04Latitudinal extent: 4.6314 to 4.7436Longitudinal extent: 117.4556 to 117.6249Taxonomic coverage: All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.<div>Animalia &ensp;-&ensp;Arthropoda &ensp;-&ensp;&ensp;-&ensp;Insecta &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Diptera &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Culicidae &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Aedes &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Aedes albopictus &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Anopheles &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;[Anoph] &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Armigeres &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;[Armigeres] &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Culex &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;[Culex1] &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;[Culex2] &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;[Culex3] &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;[CulexOP] &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;[Species7] &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;[Species8] &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;[Species9] &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Uranotaenia &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;[Urano1] &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;[Urano2] &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Zeugnomyia &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Zeugnomyia gracilis </div>

opencc-by-4.0Mar 2018View details →

zenodo36/100

Figure 5 in Tolerance limit of physicochemical water parameters in giant freshwater prawn (Macrobrachium rosenbergii) in a captive condition

Figure 5. Variation of different pH levels (7.02, 3.50, 2.02, and 1.98) in the experimental tank.

opencc-by-4.0Sep 2023View details →

zenodo36/100

Fig 5 in First record of Raorchestes longchuanensis Yang and Li, 1978 (Anura: Rhacophoridae) from northeastern Bangladesh suggests wide habitat tolerance

Fig 5. Map showing the previously reported and present records of Raorchestes longchuanensis.

opencc-by-4.0Mar 2020View details →

zenodo36/100

Data files and computer code scripts for reproducing the results of a manuscript on the measurement and ranking of cotton drought tolerance capacity

This upload contains the data files and computer code scripts for reproducing the main results, esp. figures,  of the manuscript entitled "Rapid measurement and statistical ranking of leaf drought tolerance capacity in cotton," by X. Dong, D. A. Mott, J. Garg, Q. Zhou, J. Sunoj V. S., and B. M. McKnight. The manuscriptt is currently under peer review.

opencc-by-4.0Aug 2024View details →

zenodo36/100

Data: Social tolerance and role model diversity increase tool use learning opportunities across chimpanzees ontogeny.

Open the record for dataset details and reuse information.

opencc-by-4.0Oct 2024View details →

zenodo36/100

Mildenberger - Upper thermal tolerances of three east Texas freshwater mussels

These data and analyses are from a study on 3 species of East Texas freshwater mussels, which determined each species' upper lethal thermal tolerances. The study also included water temperature measurements, expanded with hindcasting code, and a uniform continuous above-threshold (UCAT) analysis on extreme exceedances.

opencc-by-4.0Nov 2024View details →

dryad36/100

Individual fish infection data from metapopulation epidemics used to calculate resistance, tolerance and competence

Host competence, defined as the likelihood that a host will transmit infection, may be affected by an individual's resistance to infection and its ability to withstand damage caused by infection (tolerance). Host competence may therefore be one of the most important factors to impact host-parasite dynamics, yet the relationships among resistance, tolerance, and competence are poorly understood. The objective of the present study was to determine whether individual host resistance (ability to resist or minimize infection) and/or tolerance (ability to withstand or minimize reduction in fitness due to infection) contributed to the competence (ability to spread infection) of hosts using guppies infected with the ectoparasite, Gyrodactylus turnbulli. This individual-fish level analysis used data collected from a previous metapopulation experiment that had tracked host-parasite dynamics at the metapopulation scale using individually marked guppies that were moved among experimental tanks within replicate metapopulations. Fish tolerance was measured as the residual from a fish's expected survival post-infection for a given parasite burden. Fish resistance was measured as the peak parasite load ( - log-transformed). Host competence was measured as the incidence (number of new infections over two days after the arrival of a fish to a tank) weighted by the density of available uninfected fish in the tank. In contrast to the assumption of a trade-off between resistance and tolerance, individual fish tolerance and resistance were both negatively associated with competence. Connectivity (the number of fish with which an individual came into contact) was not associated with competence. Our results indicate that resistance and tolerance are both important to disease spread. These findings highlight the importance of understanding how individual defence against parasites may contribute to its competence as a host, and therefore impact metapopulation-level dynamics.

opencc-zeroJul 2021View details →

zenodo36/100

Disentangling the roles of social and individual effects on cadmium tolerance in the ant Temnothorax nylanderi.

Data from the paper 'Disentangling the roles of social and individual effects on cadmium tolerance in the ant Temnothorax nylanderi.'

opencc-by-4.0Jul 2021View details →

dryad36/100

Interindividual plasticity in metabolic and thermal tolerance traits from populations subjected to recent anthropogenic heating

To better understand temperature's role in the interaction between local evolutionary adaptation and physiological plasticity, we investigated acclimation effects on metabolic performance and thermal tolerance among natural Fundulus heteroclitus populations from different thermal environments. F. heteroclitus populations experience large daily and seasonal temperature variations, as well as local mean temperature differences across their large geographic cline. In this study, we focus on three populations: one locally heated (32°C) by thermal effluence (TE) from the Oyster Creek Nuclear Generating Station, NJ and two nearby reference populations that do not experience local heating (28°C). After acclimation to 12°C or 28°C, we quantified whole animal metabolic rate (WAM), critical thermal maximum (CTMax) and substrate specific cardiac metabolic rate (CaM, substrates: glucose, fatty acids, lactate plus ketones plus ethanol, and endogenous [i.e., no added substrates]) in ~160 individuals from these three populations. Populations showed few significant differences due to large interindividual variation within each population and variation in acclimation response within any single trait. In general, for WAM and CTMax the interindividual variation in acclimation response (log2 ratio 28°C/12°C) was a function of performance at 12°C with greater acclimation response for individuals that had lower 12°C performance. In contrast, for CaM the rates when acclimated and assayed at 12°C or 28°C were nearly identical. The small differences in CaM between 12°C and 28°C temperature were partially explained by cardiac remodeling where individuals acclimated to 12°C had larger hearts than individuals acclimated to 28°C, resulting in a higher CaM rate per unit heart mass at 28°C than 12°C. Correlation among physiological traits were dependent on acclimation temperature. For example, WAM was negatively correlated with CTMax at 12°C but positively correlated at 28°C. Additionally, glucose substrate supported higher cardiac metabolism than fatty acid, and fatty acid supported higher cardiac metabolism than LKA or endogenous. However, these responses were highly variable with some individuals using much more FA than glucose. These data suggest a complex relationship between specific, temperature-dependent physiological traits.

opencc-zeroJul 2021View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated datasets

Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record

Find research datasets worth reusing

Dataset results

Scanning electron microscopy (SEM) images of single bacterial-Pb tolerant isolates

Data from: dispersal restriction and facilitation in species with differing tolerance to development: a landscape genetics study of native and introduced lizards

Quantifying intraspecific variation in host resistance and tolerance to a lethal pathogen

Dataset and scripts from: Predicting organismal response to marine heatwaves using dynamic thermal tolerance landscape models

Data from: Frequency-dependent tolerance to aircraft disturbance drastically alters predicted impact on shorebirds

StArt Protocol for systematic review: Use of CRISPR technology in gene editing for tolerance to biotic factors in plants: A systematic review.

Exposure to teflubenzuron reduces drought tolerance of collembolans

Figure 2 in Response and tolerance mechanism of food crops under high temperature stress: a review

No evidence of adaptive tolerance of parasitism in a cavity-nesting brood parasite host

Fig. 1. A in Tolerance to Anhydrobiotic Conditions Among Two Coexisting Tardigrade Species Differing in Life Strategies.

Untargeted mutagenesis of brassinosteroid receptor SbBRI1 confers drought tolerance by altering phenylpropanoid metabolism in Sorghum bicolor

Investigating Temperature Tolerance in Mosquito Disease Vectors Across a Land-Use Gradient

Figure 5 in Tolerance limit of physicochemical water parameters in giant freshwater prawn (Macrobrachium rosenbergii) in a captive condition

Fig 5 in First record of Raorchestes longchuanensis Yang and Li, 1978 (Anura: Rhacophoridae) from northeastern Bangladesh suggests wide habitat tolerance

Data files and computer code scripts for reproducing the results of a manuscript on the measurement and ranking of cotton drought tolerance capacity

Data: Social tolerance and role model diversity increase tool use learning opportunities across chimpanzees ontogeny.

Mildenberger - Upper thermal tolerances of three east Texas freshwater mussels

Individual fish infection data from metapopulation epidemics used to calculate resistance, tolerance and competence

Disentangling the roles of social and individual effects on cadmium tolerance in the ant Temnothorax nylanderi.

Interindividual plasticity in metabolic and thermal tolerance traits from populations subjected to recent anthropogenic heating

Understand access before you commit

Allen Brain Atlas

Annotated Behaviour and Observability Dataset (ABODe)

DANDI Archive for NWB datasets

International Brain Laboratory public data

OpenNeuro

Your academic CV and NIH biosketch