Find research datasets worth reusing

Figure 4 in The circulatory system and its spatial relations to other major organ systems in Spelaeogriphacea and Mictacea (Malacostraca, Crustacea) - a three-dimensional analysis

Figure 4. Three-dimensional reconstructions of a semithin cross section series of Mictocaris halope. A–E, surface renderings of the main organ systems in the cephalothorax. A, three aspects of the digestive system. B, three aspects of the central nervous system. C, D, The haemolymph vascular system (HVS) in the cephalothorax. C, frontal aspect. D, lateral aspect. E, antero-dorsal aspect. F, three virtual sections of the whole image stack. Same colour codes as in the surface renderings. See the Appendix for a list of abbreviations used.

Figure 6 in The circulatory system and its spatial relations to other major organ systems in Spelaeogriphacea and Mictacea (Malacostraca, Crustacea) - a three-dimensional analysis

Figure 6. Schematic diagrams of the pumping function of the 'myoarterial formation a' in Mictocaris halope. A, diastole. B, systole. Arrows depict flow directions of the haemolymph. See the Appendix for a list of abbreviations used.

Figure 3 in The circulatory system and its spatial relations to other major organ systems in Spelaeogriphacea and Mictacea (Malacostraca, Crustacea) - a three-dimensional analysis

Figure 3. Major organ systems in Mictocaris halope. A, Schematic diagram of the haemolymph vascular system (HVS) and digestive system in M. halope. Arteries running into the antennae are depicted by broken lines. B, C, photographs of live animals showing the digestive system and the gonads (courtesy of Peter Parks, Image Quest 3D). B, dorsal aspect. C, lateral aspect. See the Appendix for a list of abbreviations used.

Figure 1 in The circulatory system and its spatial relations to other major organ systems in Spelaeogriphacea and Mictacea (Malacostraca, Crustacea) - a three-dimensional analysis

Figure 1. Reconstructions of the major organ systems in Spelaeogriphus lepidops. A, Schematic diagram of the haemolymph vascular system (HVS) and digestive system. Arteries running into the antennae are depicted by broken lines. B, C, three-dimensional reconstructions (surface rendering) of a horizontal semithin section series showing the HVS in the cephalothorax. B, antero-lateral aspect. C, Lateral aspect. D, E, three-dimensional reconstructions (surface rendering) of a horizontal semithin section series showing the central nervous system (CNS) in the cephalothorax. D, dorsal aspect. E, frontal aspect. F, three-dimensional reconstructions (surface rendering) of a horizontal semithin section series showing the major organ systems in the cephalothorax. Green, digestive system; red, HVS; yellow, CNS. The CNS is shown as slightly transparent. See the Appendix for a list of abbreviations used.

opencc-by-4.0Nov 2022View details →

Spatial analysis of Measles Vaccination Coverage in the State of São Paulo

Dataset for the article Spatial Analysis of Measles Vaccination Coverage in the State of São Paulo. The aim of the present study was to evaluate first and second dose of measles vaccine coverage (VC) in the cities of São Paulo and its spatial dynamics between 2015 and 2020. Method: It is a mixed-type ecological study After calculating the VC, the following four categories were created: very low, low, adequate, and high, and the spatial autocorrelation of VC was analyzed using the Global and Local Moran’s statistics. The dataset is divided by year and by vaccine type.

opencc-by-4.0Feb 2023View details →

Codes in R for spatial statistics analysis, ecological response models and spatial distribution models

In the last decade, a plethora of algorithms have been developed for spatial ecology studies. In our case, we use some of these codes for underwater research work in applied ecology analysis of threatened endemic fishes and their natural habitat. For this, we developed codes in Rstudio® script environment to run spatial and statistical analyses for ecological response and spatial distribution models (e.g., Hijmans & Elith, 2017; Den Burg et al., 2020). The employed R packages are as follows: caret (Kuhn et al., 2020), corrplot (Wei & Simko, 2017), devtools (Wickham, 2015), dismo (Hijmans & Elith, 2017), gbm (Freund & Schapire, 1997; Friedman, 2002), ggplot2 (Wickham et al., 2019), lattice (Sarkar, 2008), lattice (Musa & Mansor, 2021), maptools (Hijmans & Elith, 2017), modelmetrics (Hvitfeldt & Silge, 2021), pander (Wickham, 2015), plyr (Wickham & Wickham, 2015), pROC (Robin et al., 2011), raster (Hijmans & Elith, 2017), RColorBrewer (Neuwirth, 2014), Rcpp (Eddelbeuttel & Balamura, 2018), rgdal (Verzani, 2011), sdm (Naimi & Araujo, 2016), sf (e.g., Zainuddin, 2023), sp (Pebesma, 2020) and usethis (Gladstone, 2022). It is important to follow all the codes in order to obtain results from the ecological response and spatial distribution models. In particular, for the ecological scenario, we selected the Generalized Linear Model (GLM) and for the geographic scenario we selected DOMAIN, also known as Gower's metric (Carpenter et al., 1993). We selected this regression method and this distance similarity metric because of its adequacy and robustness for studies with endemic or threatened species (e.g., Naoki et al., 2006). Next, we explain the statistical parameterization for the codes immersed in the GLM and DOMAIN running: In the first instance, we generated the background points and extracted the values of the variables (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code2_Extract_values_DWp_SC.R?versionId=c1ea0c61-53fe-4f95-ab88-0c1cb28399cb">Code2_Extract_values_DWp_SC.R</a>). Barbet-Massin et al. (2012) recommend the use of 10,000 background points when using regression methods (e.g., Generalized Linear Model) or distance-based models (e.g., DOMAIN). However, we considered important some factors such as the extent of the area and the type of study species for the correct selection of the number of points (Pers. Obs.).  Then, we extracted the values of predictor variables (e.g., bioclimatic, topographic, demographic, habitat) in function of presence and background points (e.g., Hijmans and Elith, 2017). Subsequently, we subdivide both the presence and background point groups into 75% training data and 25% test data, each group, following the method of Soberón & Nakamura (2009) and Hijmans & Elith (2017). For a training control, the 10-fold (cross-validation) method is selected, where the response variable presence is assigned as a factor. In case that some other variable would be important for the study species, it should also be assigned as a factor (Kim, 2009). After that, we ran the code for the GBM method (Gradient Boost Machine; <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code3_GBM_Relative_contribution.R?versionId=1656bbae-66aa-409e-bb91-d8007dee8f95">Code3_GBM_Relative_contribution.R</a> and <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code4_Relative_contribution.R?versionId=0e1d9352-e6b2-43da-984b-d6853a914258">Code4_Relative_contribution.R</a>), where we obtained the relative contribution of the variables used in the model. We parameterized the code with a Gaussian distribution and cross iteration of 5,000 repetitions (e.g., Friedman, 2002; kim, 2009; Hijmans and Elith, 2017). In addition, we considered selecting a validation interval of 4 random training points (Personal test). The obtained plots were the partial dependence blocks, in function of each predictor variable. Subsequently, the correlation of the variables is run by Pearson's method (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code5_Pearson_Correlation.R?versionId=275f8dd4-b056-44d2-bfe5-f6264bc3298b">Code5_Pearson_Correlation.R</a>) to evaluate multicollinearity between variables (Guisan & Hofer, 2003). It is recommended to consider a bivariate correlation ± 0.70 to discard highly correlated variables (e.g., Awan et al., 2021). Once the above codes were run, we uploaded the same subgroups (i.e., presence and background groups with 75% training and 25% testing) (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code6_Presence&backgrounds.R?versionId=d797b528-782f-4a19-bd61-cfb197f38513">Code6_Presence&backgrounds.R</a>) for the GLM method code (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code7_GLM_model.R?versionId=e4aca276-d601-49ec-a62c-a9223b05a7ed">Code7_GLM_model.R</a>). Here, we first ran the GLM models per variable to obtain the p-significance value of each variable (alpha ≤ 0.05); we selected the value one (i.e., presence) as the likelihood factor. The generated models are of polynomial degree to obtain linear and quadratic response (e.g., Fielding and Bell, 1997; Allouche et al., 2006). From these results, we ran ecological response curve models, where the resulting plots included the probability of occurrence and values for continuous variables or categories for discrete variables. The points of the presence and background training group are also included. On the other hand, a global GLM was also run, from which the generalized model is evaluated by means of a 2 x 2 contingency matrix, including both observed and predicted records. A representation of this is shown in Table 1 (adapted from Allouche et al., 2006). In this process we select an arbitrary boundary of 0.5 to obtain better modeling performance and avoid high percentage of bias in type I (omission) or II (commission) errors (e.g., Carpenter et al., 1993; Fielding and Bell, 1997; Allouche et al., 2006; Kim, 2009; Hijmans and Elith, 2017). Table 1. Example of 2 x 2 contingency matrix for calculating performance metrics for GLM models. A represents true presence records (true positives), B represents false presence records (false positives - error of commission), C represents true background points (true negatives) and D represents false backgrounds (false negatives - errors of omission). <table align="center"> <tbody> <tr> <td>                                      </td> <td> Validation set </td> </tr> <tr> <td> Model </td> <td> True </td> <td> False </td> </tr> <tr> <td> Presence </td> <td> A </td> <td> B </td> </tr> <tr> <td> Background </td> <td> C </td> <td> D </td> </tr> </tbody> </table> We then calculated the Overall and True Skill Statistics (TSS) metrics. The first is used to assess the proportion of correctly predicted cases, while the second metric assesses the prevalence of correctly predicted cases (Olden and Jackson, 2002). This metric also gives equal importance to the prevalence of presence prediction as to the random performance correction (Fielding and Bell, 1997; Allouche et al., 2006). The last code (i.e., <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code8_DOMAIN_SuitHab_model.R?versionId=d951a8f2-d3a4-4804-b862-1b2762061876">Code8_DOMAIN_SuitHab_model.R</a>) is for species distribution modelling using the DOMAIN algorithm (Carpenter et al., 1993). Here, we loaded the variable stack and the presence and background group subdivided into 75% training and 25% test, each. We only included the presence training subset and the predictor variables stack in the calculation of the DOMAIN metric, as well as in the evaluation and validation of the model. Regarding the model evaluation and estimation, we selected the following estimators: 1) partial ROC, which evaluates the approach between the curves of positive (i.e., correctly predicted presence) and negative (i.e., correctly predicted absence) cases. As farther apart these curves are, the model has a better prediction performance for the correct spatial distribution of the species (Manzanilla-Quiñones, 2020). 2) ROC/AUC curve for model validation, where an optimal performance threshold is estimated to have an expected confidence of 75% to 99% probability (De Long et al., 1988).

opencc-by-4.0Feb 2023View details →

A Spatial Analysis of Food Insecurity and Body Mass Index with Income and Grocery Store Density in a Diverse Sample of Adolescents and Young Adults

Abstract: Food insecurity occurs when a household lacks consistent access to food and is more prevalent in ethnic and racial minoritized populations. While there has been a proliferation of research linking food insecurity to obesity, these findings are mixed. It may be helpful to consider some additional geographic factors that may be associated with both factors including socioeconomic status and grocery store density. The purpose of the current study aimed to examine spatial relationships between food insecurity and SES/store density and BMI and SES/store density in a diverse sample of adolescents and young adults across two studies in a large, urban city. GIS analysis revealed that participants with the highest food insecurity (larger symbols) tend to live in the zip codes with the lowest median income. There did not appear to be clear a relationship between food insecurity and store density. Participants with the highest BMI tend to live in zip codes with lower median income and participants with higher BMI tended to live further away from downtown, which has the highest concentration of grocery stores in the city. Our findings may help to inform future interventions and policy approaches to addressing both obesity and food insecurity in areas of higher prevalence.

opencc-by-4.0Apr 2022View details →

Scripts of data selection and analysis: role of community size in driving spatial variation in riverine fish metacommunities around the world

Here we describe how we obtained and analyzed data for the manuscript: High compositional dissimilarity among small communities is decoupled from environmental variation, accepted for publication in Oikos. (10.1111/oik.09802). A preprint is also available: https://doi.org/10.32942/osf.io/vngse We investigated the role of community size in mediate the strength of ecological drift and environmental selection in driving community spatial variation in metacommunities.

opencc-by-4.0Apr 2023View details →

Data for 'Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis'

Additional data for 'Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis'

Fig. 6 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (central Bulgarian Black sea coast). Part III. Spatial distribution and gradient analysis

Fig. 6. PCA distribution of the sampling sites and the subclasses of life forms (according to Sharova 1981): Z_Phytob – Zoophagous phytobionts; Z_Strat – Zoophagous stratobionts; M_Strat – Mixophytophagous stratobionts; M_Short – Mixophytophagous stratohortobionts; M_Geoh – Mixophytophagous geobionts.

Fig. 7 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (central Bulgarian Black sea coast). Part III. Spatial distribution and gradient analysis

Fig. 7. PCA distribution of the sampling sites and categories of life forms (according to SHAROVA 1981): Life form class 1. Zoophagous. Life form subclass: 1.1 – Phytobios; 1.2 – Epigeobios; 1.3 – Stratobios; 1.4 – Geobios. Life form groups: 1.1.2 – stemdwelling hortobionts; 1.1.3 – leaf-dwelling dendrohortobionts; 1.2.2 – large walking epigeobionts; 1.2.2(1) – large walking dendroepigeobionts; 1.2.3 – running epigeobionts; 1.2.4 – flying epigeobionts; 1.3(1) – series crevice-dwelling stratobionts; 1.3(1).1 – surface & litter-dwelling; 1.3(1).2 – litter-dwelling; 1.3(1).3 – litter & crevice-dwelling; 1.3(1).4 – endogeobionts; 1.3(1).5 – litter & bark-dwelling; 1.3(1).6 – bothrobionts; 1.3(2).1 – litter & soil-dwelling; 1.4.2(1) – large digging geobionts. Life form class 2. Mixophytophagous. Life form subclass: 2.1 – Stratobios; 2.2 – Stratohortobios; 2.3 – Geohortobios. Life form groups: 2.1.1 – crevice-dwelling stratobionts; 2.2.1 – stratohortobionts; 2.3.1 – harpaloid geohortobionts; 2.3.1(1) – crevice-dwelling harpaloid geohortobionts; 2.3.2 – zabroid geohortobionts; 2.3.3 – dytomeoid geohortobionts.

Fig. 4 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (central Bulgarian Black sea coast). Part III. Spatial distribution and gradient analysis

Fig. 4. Ordination of the sampling sites in relation to the humidity and vegetation. The calculations were performed by the use of the results from all of the sampling sites and all of the catches, standardized through the recalculation of the data as number of specimens per 100 trapdays.

Fig. 3 in Ground beetles (Coleoptera: Carabidae) from the region of Cape Emine (central Bulgarian Black sea coast). Part III. Spatial distribution and gradient analysis

Fig. 3. Distribution of the permanent species in relation to the humidity and vegetation. The analysis included only the permanent species – those with a frequency above 50% (see TEOFILOVA 2015): A aenea – Amara aenea; A anth – Amara anthobia; A famil – Amara familiaris; Ac megac – Acinopus megacephalus; Agon sp – Agonum (Europhilus) sp.; Br crep – Brachinus crepitans; Br expl – Brachinus explodens; C ambig – Calathus ambiguus; C cinct – Calathus cinctus; C fuscip – Calathus fuscipes; Car conv – Carabus convexus; Car cor – Carabus coriaceus; Car ullr – Carabus ullrichi; Chl nit – Chlaenius nitidulus; H dimid – Harpalus dimidiatus; H dist – Harpalus distinguendus; H flavic – Harpalus flavicornis; H rubrip – Harpalus rubripes; H tardus – Harpalus tardus; Laem ter – Laemostenus terricola; Lei ruf – Leistus rufomarginatus; M maurus – Microlestes maurus; M minut – Microlestes minutulus; Myas ch – Myas chalybaeus; N brevic – Nebria brevicollis; O azur – Ophonus azureus; Par mend – Parophonus mendax; Ps rufip – Pseudoophonus rufipes; Pt melas – Pterostichus melas; Tr q – Trechus quadristriatus.

opencc-by-4.0Jul 2023View details →

Dissecting glial scar formation by spatial point pattern and topological data analysis

These data were generated by the Laboratory of Neurovascular Interactions (https://elalilab.com/) at University Laval (Quebec, Canada), and reported in "Dissecting glial scar formation by spatial point pattern and topological data analysis".  Please refer to the Open Science Framework (OSF) repository (https://osf.io/3vg8j/) or GitHub (https://github.com/elalilab/GlialScar_PPA-TDA_2022) to see the processing pipeline. AUTHORS Manrique-Castano, Daniel; Bhaskar, Dhananjay; ElAli, Ayman KEYWORDS Stroke, cerebral ischemia, brain injury, glial scar, reactive astrocytes, reactive microglia,  1. STUDY DESCRIPTION                                                              This research provides a quantitative analysis of reactive glia and glial scar formation in a mouse model of cerebral ischemia. The dataset in this repository consists of raw widefield microscopy images from healthy and ischemic animals.     2. EXPERIMENTAL CONDITIONS Six-month-old C57BL/6 mice were subjected to 30 minutes of cerebral ischemia by middle cerebral artery occlusion (MCAO). Brains were harvested at 5, 15, and 30 days post-ischemia (DPI) (see 10.5281/zenodo.3559570). 5 sham animals were included as controls. The full protocol for brain harvesting is available at 10.17504/protocols.io.4r3l27q5pg1y/v1. Brain sections were stained with NeuN, Gfap, and Iba1 antibodies to detect neurons and reactive glia after injury. Full protocol available at 10.17504/protocols.io.yxmvmk94og3p/v1                           3. FILE DESCRIPTION - GT5X_Gfap_Iba1_NeuN.rar: Contain widefield (5x magnification) .tif images grouped by animals (5-7 images per animal; see research article for further details). The images were taken with the following parameters. Objective:        Fluar 5x/0.25 M27 Scaling per pixel:    1.300 x 1.300 µm Bit depth:        16 bit     Stainings: Neun            Channel    AF647; Excitation 653; Emission    668; Exposure 3 s IBA1            Channel    AFCy3; Excitation 458; Emission    561; Exposure 4 s GFAP            Channel    AF488; Excitation 493; Emission    517; Exposure 1 s DAPI            Channel    AF405; Excitation 353; Emission    465; Exposure 50 ms We used a FIJI script to pre-process the original .czi files. The script is shared in the GitHub repository under the name GT_Exp2_5x_GenerateTiffs.jim. - GT10X_Gfap_Iba1_NeuN.rar: Contain a single widefield (10x magnification) .tif image per animal at the level of the MCA territory (see research article for further details). The images were taken with the following parameters. Objective:        ECM paln-NeoFluar 10x/0.30 M27 Scaling per pixel:    0.45 x 0.45 µm Bit depth:        16 bit     Stainings: Neun            Channel    AF647; Excitation 653; Emission    668; Exposure 200 ms IBA1            Channel    AFCy3; Excitation 458; Emission    561; Exposure 250 ms GFAP            Channel    AF488; Excitation 493; Emission    517; Exposure 100 ms DAPI            Channel    AF405; Excitation 353; Emission    465; Exposure 10 ms We used a FIJI script to pre-process the original .czi files. The script is shared in the GitHub repository under the name GT_Exp2_10x_GenerateTiffs.jim.      For 5x and 10x images, the following naming strings apply: GT5x:         Research project identifier indicating the magnification M01(n):     Animal ID 5D(n):      Days post-ischemia. 0D refers to healthy (naive) animals.  Scene1(n):     Bregma level. Scene 1 corresponds to the most anterior area sampled, while Scene 6 or 7 is the most posterior. - PointPatterns_10x.rds: 2D point patterns of GFAP, IBA1, and NeuN generated by the r-package spatstat. The observation window comprises a horizontal ROI from the ventricular area to the outer border of the dorsolateral cerebral cortex. The point patterns were generated from the files and coordinates contained in the QupathProjects_10x.rar file in this repository. To reproduce the generation of point patterns please refer to the associated GitHub repository (https://github.com/elalilab/Stroke_GlialScar_PPA-TDA).  - PointPatterns_5x.rds: 2D point patterns of GFAP, IBA1, and NeuN generated by the r-package spatstat. The observation window comprises the ischemic hemisphere. The point patterns were generated from the files and coordinates contained in the QupathProjects_5x.rar file in this repository. To reproduce the generation of point patterns please refer to the associated GitHub repository (https://github.com/elalilab/Stroke_GlialScar_PPA-TDA).  - QupathProjects_5x.rar: QuPath project folder for 5x images (GT5X_Gfap_Iba1_NeuN.rar). Each subfolder (per animal) contains the necessary files to import annotations (alignment to the Allen Brain Atlas) generated by ABBA (https://biop.github.io/ijp-imagetoatlas/). Please see the research article for further details.  **NOTE** Gfap, Iba1, and NeuN folders contain raw .tsv data originated by QuPath (cell counting). These folders are read in the R processing pipeline to extract the coordinates of each cell. Please make sure the whole folder is in the R working directory. The file "project.qpproj" in each folder opens the QuPath project in QuPath and reads the classifiers and data folders. Each folder also contains "_Alignement.json" and "_Registration_json" files generated during the alignment and annotation procedures in ABBA. However, when the route of the source images is changed, the plugin does not allow rerouting, and the files are of no practical use. The issue has been reported to the ABBA Github repository.    - QupathProjects_10x.rar: QuPath project folder for 10x images (GT5X_Gfap_Iba1_NeuN.rar). The folder contains the necessary files to import annotations (Alignment to the Allen Brain Atlas) generated by ABBA (https://biop.github.io/ijp-imagetoatlas/). Please see the research article for further details.  **NOTE** Gfap, Iba1, NeuN, and DAPI folders contain raw .tsv data originated by QuPath (cell counting). These folders are read in the R processing pipeline to extract the coordinates of each cell. Please make sure the whole folder is in the R working directory. The file "project.qpproj" opens the QuPath project in QuPath and reads the classifiers and data folders.

publicSep 2021View details →

Data and analysis scripts for: Co-occurrence patterns at four spatial scales implicate reproductive processes in shaping community assembly in clovers

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publicAug 2024View details →

Spatial survival analysis accounts for female-biased breeding dispersal and provides realistic estimates of true annual survival in migratory warblers

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publicMar 2025View details →

Data from: One-stage spatial mark-resight analysis reveals an increasing grizzly bear population with declining density near roads

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