Find research datasets worth reusing

opencc-by-4.0Mar 2024View details →

To Inform or Misinform: The Current Situation of Brazil's Information Ecosystem

Originally published on Youtube on March 12th, 2024: https://www.youtube.com/watch?v=h4LJxZjt6Mg Digitalisation has transformed the way information is created and disseminated. Currently, we are grappling with the darker side of this transformation, wherein vicious, mutually reinforcing cycles of disinformation, extreme polarization, and autocratization threaten peace, justice, and good governance. Brazil is no exception. Factors that shape information ecosystems are highly dependent on context, yet debates on how to counter the "information disorder" we are currently experiencing tend to be highly specific, preponderantly focusing on the regulation of online content. This webinar invites participants to look at the bigger picture. Bringing together disinformation experts focused on the media, politics, law, and socioeconomic roots and impacts of disinformation, the goal of this workshop is to identify risks and opportunities in Brazilian society. The larger question is how do we restore order to our information ecosystems?

opencc-by-4.0Feb 2024View details →

Supplementary information for "Reassessment of French breeding bird population sizes using citizen science and accounting for species detectability"

Reproducibility data for the manuscript "Reassessment of French breeding bird population sizes using citizen science and accounting for species detectability", it contains data and script for : <ol> <li> The R script <code>01_HDSfreq_Calibration.R</code> of the developed approach to estimate national breeding bird population size using Hierarchical Distance Sampling (HDS) and the secondary candidate set model selection method (Morin et al., 2020) </li> <li> The R script <code>02_pglmm_figures.R</code> for the calibration of the Phylogenetic Generalised Mixed Model (PGLMM) used in the manuscript to compare previous population size estimates to ones modelled using <code>01_HDSfreq_Calibration.R</code>, while accounting for species phylogenetic relatedness </li> <li> The R script <code>03_results_tables.R</code>, used to generate supplementary tables S2.1-3 and S6.1-2. </li> </ol> <ul> <li> Column names are highlighted in italics. </li> </ul> <h2>Data description</h2> <h5>A. BirdPhylo_Burleigh_et_al.tre</h5> A phylogenetic tree from Burleigh et al., 2015. Phylogenetic distances are used as random effect for the PGLMM in script <code>02_pglmm_figures.R</code> <h5>B. Conservation_status.txt</h5> A <code>.txt</code> file of the conservation status for France (Statut_FR) and Europe (Statut_EU) for the studied species retrieved from (UICN France et al., 2016). Only Statut_FR is used for the table S6.1. <h5>C. FBBS_trends_20122023.txt</h5> A <code>.txt</code> file containing species trend of the French Breeding Bird Survey data from 2012 to 2023. <ul> <li> Species names (English, French) associated with FBBS trend estimated using data collected from 2012 - 2023 </li> <li> Hab_specialization, determined from Julliard et al. 2006 approach </li> <li> infPrec, supPerc, estimate, se, pval : Species trends over 2012-2023 period in % | lower and upper confidence intervals, mean, standard error and significance </li> </ul> <h5>D. PrepData_HDS.RData</h5> A file containing <code>.RData</code> environment required to run <code>01_HDSfreq_Calibration.R</code> script, it contains : <ul> <li> ATLAS12 : A dataframe with breeding status information from 2012 breeding bird atlas (used to restrict model prediction grid, in regard of 2012 known breeding locations) </li> <li> ConcordTBL : A concordance table for species names (English, French and scientific notation) </li> <li> EPOC_ODF : observation dataset, each line corresponds to detected individuals <ul> <li> UUID, Ref, ID_liste, ID, ID_place, Grid_10x10 : Columns used to identify observations, lists, sites, locations, 10x10 grids </li> <li> ID_species_Biolovision, Nom_espece, english_name, scientific_name : Species ID and names </li> <li> Date, Day, Month, Year, Julian_date, Obs_hour, Hour_list, Complete_checklist, Commentary, Project_name, Scheme, Observer, List_time, List_diversity, List_abundance : Lists and Observation related effort covariates and metadata </li> <li> X_Lambert93_m, Y_Lambert93_m : Observation locations in <code>(crs = 2154)</code> </li> <li> GPS_loc_observer : Logical, TRUE : location of observers corresponds to true GPS information ; FALSE : observer's location approximated as the barycenter of observations </li> <li> X_barycentre_L93, Y_barycentre_L93 : Observers location in <code>(crs = 2154)</code> </li> <li> Use_distance_sampling, Observation_distance_m, Distance_bin_logical, Distance_class_0_25, Distance_class_25_100, Distance_class_100_200, Distance_class_200_more : Distance sampling related informations </li> <li> Abudance_brut, Estimate, Number, Nb_male_identified, Nb_female_identified, Nb_juvenile_identified, Nb_grounded, Nb_flying, Nb_auditory, Nb_NA : Observation metadata, used in case of a priori filter over male detection. </li> </ul> </li> <li> grid_pred_envvar : Prediction grid with environmental covariates, see appendix S3 of the manuscript, covering metropolitan France </li> <li> grid_pred.sf : corresponding sf object </li> <li> L93_10x10 : sf object corresponding to 10x10 grid used in 2012 atlas </li> <li> ObsVar_EPOCODF : dataframe specifying lists effort covariates </li> <li> OCCU_EPOC_ODF : Environmental covariate agregated over lists </li> <li> OCCU_EPOC_ODF_sites_envvar : Environmental covariate agregated over sites </li> <li> table.pheno : species table specifying related phenology filter </li> </ul> <h5>E. ReadOutput_HDSfreq_comparison.csv</h5> A <code>.csv</code> table of species population size estimated using 2021-2023 EPOC-ODF data over areas determined as breeding in the 2012 atlas. Predictions were restrained over location known as breeding in 2012 for the sake of comparison. <ul> <li> HDS_estimUnfenced_XXX : average pop. size estimated with confidence interval before prediction post-treatment (describe in fig 2. of the manuscript) </li> <li> HDS_estim_ExtrapolFence_XXX : average pop. size estimated with confidence interval after prediction post-treatment </li> <li> NB_data_calib : Number of observations (distance data, not sites) used for calibration </li> <li> MALE_FILTERING : (logical) indicating if female individuals could be detected in the same proportion of males during list recording. FALSE : we considered that estimated pop.size corresponded to the number of individuals leading to a division by 2 for the comparison with the previous atlas (in pairs). (cf . line 88-90 in <code>02_pglmm_figures.R</code>) </li> <li> EcartDTF_filtrage_maleOnly : If MALE_FILTERING == T, proportion of the remaining data used for calibration after removal of list with individual tagged as female/juvenile (in %) </li> <li> Max_dist_breaks : Maximal distance for detection function, after right-side truncation of 5% </li> <li> Chat : Coefficient of overdisperion of the best model in the second candidate set </li> <li> MED_MEAN_Prob_Detect (.._SE) : weighted averaged median of intercept from the availability state from HDS models, weigthed AICc-wise </li> <li> MED_MEAN_Density : weighted averaged median of intercept from the abundance state from HDS models, weigthed AICc-wise </li> <li> Significant_phi/lambda : Categorial (Significant/Near/Not), are availability/abundance intercepts significatively different from 0 (significant : alpha = 0.05, near : alpha = 0.1) </li> <li> KeyFun_used : Key function used for distance sampling </li> <li> Mixtured_used : Mixture used in the abundance state for HDS </li> </ul> <h5>F. ReadOutput_HDSfreq_comparison_20212022.csv</h5> A <code>.csv</code> table of species population size estimated using 2021-2022 EPOC-ODF data over areas determined as breeding in the 2012 atlas. Used for the robustness analysis of HDS estimated population size, see appendix S2 and table S2.2 of the manuscript. <h5>G. ReadOutput_HDSfreq_EstimMetropole.csv</h5> A <code>.csv</code> table of species population size estimated using 2021-2023 EPOC-ODF data over metropolitan France. <ul> <li> HDS_estimUnfenced_XXX : average pop. size estimated with confidence interval before prediction post-treatment (describe in fig 2. of the manuscript) </li> <li> HDS_estim_ExtrapolFence_XXX : average pop. size estimated with confidence interval after prediction post-treatment </li> <li> MALE_FILTERING : (logical) indicating if female individuals could be detected in the same proportion of males during list recording. FALSE : we considered that estimated pop.size corresponded to the number of individuals leading to a division by 2 for conversion to pop. size in breeding pairs </li> <li> Chat : Coefficient of overdisperion of the best model in the second candidate set </li> <li> KeyFun_used : Key function used for distance sampling </li> <li> Mixtured_used : Mixture used in the abundance state for HDS </li> </ul> <h5>H. TABLE_SpeciesFilters_and_2012Estimates.txt</h5> A <code>.txt </code>table containing species names (English, French and scientific notation), filters and 2012 French atlas pop. size estimates <ul> <li> debut_jour : starting day of the month for phenology filter </li> <li> debut_mois : starting month for phenology filter </li> <li> fin_jour : ending day of the month for phenology filter </li> <li> fin_mois : ending month for phenology filter </li> <li> Estim_low/up_Atlas2012 : Lower and Upper interval of estimated pop. size in 2012 (number in breeding pairs) </li> <li> gregarious : logical (0,1) specifying if the species is considered gregarious during its breeding season </li> </ul> <h5>I. sessionInfo_script_XX</h5> User R session information, obtained from <code>sessionInfo()</code> R function, used for running R script. <h2>Code</h2> <h5>A. <code>01_HDSfreq_Calibration.R</code></h5> R script showcasing data formatting and model calibration of the HDS based upon frequentist aproach from <code>unmarked</code> R package. For more details of the model calibration approach, see appendix S4 of the manuscript. <h5>B. <code>02_pglmm_figures.R</code></h5> Script for the calibration of the PGLMM and generation of figure 5 of the manuscript. <h5>C. <code>03_results_tables.R</code></h5> Script to generate tables depicted in Appendices S2 (S2.1-3) and S6 (S6.1-2) <h5>D. <code>HDS_functions.R</code></h5> R script called in <code>01_HDSfreq_Calibration.R</code>, contains 2 functions: <ul> <li> <code>Try_HDS()</code> : Function implementing a try-catch permitting calibration of multiple species in a loop. <ul> <li> Species with non convergent models are skipped sending a notification to the user R interface. </li> <li> Used in all sub-candidate sets (i.e. "null", "p", "phi", "lambda") </li> <li> When phase="ALL" corresponding to the second candidate set (i.e. ensemble of best model candidates, with delta_AIC <= 10, from previous sub-candidate sets), it permits the use of previous sub-candidates set coefficients as starting values, with <code>StartValues </code>argument </li> <li> Later part of the function hack the call of the unmarkedFit class, in order to accommodate from calibrating a gdistsamp using characters formulas </li> </ul> </li> <li> <code>fitstats()</code> : Function from unmarked::parboot(), available with <code>help(parboot)</code>. Allow estimation of multiple goodness-of-git statistic (Freeman-Tukey, Chi-squared and Sum of Squared Estimate of errors) through parametric bootstrap. In the manuscript, only chi-squared metric is used. </li> </ul> <h5>E. <code>dsmextra_modif_function.R</code></h5> R script called in <code>01_HDSfreq_Calibration.R</code>. Miscellaneous adjustment of core function from <code>dsmextra </code>package (main change being the integration of tolerance argument (<code>tol</code>) in the chain of function. <h5>F. <code>misc_unmarked.R</code></h5> R script called in <code>01_HDSfreq_Calibration.R</code>. modify Setmethods for unmarked function, in particular for <code>unmarked::parboot</code>, allowing parallelization of parametric bootstrap with prior unmarked version (<code>unmarked < 1.3.0</code>). <h2>References</h2> Data was derived from the following sources: <ul> <li> Burleigh, J.G., Kimball, R.T., Braun, E.L., 2015. Building the avian tree of life using a large-scale, sparse supermatrix. Molecular Phylogenetics and Evolution 84, 53–63. <a href="https://doi.org/10.1016/j.ympev.2014.12.003">https://doi.org/10.1016/j.ympev.2014.12.003</a> </li> </ul> Other sources : <ul> <li> Julliard, R., Clavel, J., Devictor, V., Jiguet, F., Couvet, D., 2006. Spatial segregation of specialists and generalists in bird communities. Ecology Letters 9, 1237–1244. <a href="https://doi.org/10.1111/j.1461-0248.2006.00977.x">https://doi.org/10.1111/j.1461-0248.2006.00977.x</a> </li> <li> Morin, D.J., Yackulic, C.B., Diffendorfer, J.E., Lesmeister, D.B., Nielsen, C.K., Reid, J., Schauber, E.M., 2020. Is your ad hoc model selection strategy affecting your multimodel inference? Ecosphere 11, e02997. <a href="https://doi.org/10.1002/ecs2.2997">https://doi.org/10.1002/ecs2.2997</a> </li> <li> UICN France, MNHN, LPO, SEOF, ONCFS, 2016. La Liste rouge des espèces menacées en France - Chapitre Oiseaux de France métropolitaine. Paris, France. </li> </ul>

opencc-by-4.0Jun 2024View details →

Open Data in German Forest Information Systems: Towards an EU Forest Resilience Monitor (Original dataset on Forest Resilience Indicators and their compliance with Open data criteria)

This dataset represents the original analysis on which my Master's Thesis in the pioneer master programme at the Universities of Münster, Tallinn (Taltech) and Leuven (KUL), titled "Open Data in German Forest Governance: Towards an EU Forest Resilience Monitor".  <ul> <li>The first sheet contains the coding on information systems on bird species occurrence and their compliance with Open data criteria, with justifications, links or further information speciefied in comments, where necessary. </li> <li>The second sheet contains the coding on information systems on tree species distribution and their compliance with Open data criteria, with justifications, links or further information speciefied in comments, where necessary. </li> <li>The third sheet contains the coding on information systems on soil water conditions and their compliance with Open data criteria, with justifications, links or further information speciefied in comments, where necessary. </li> <li>The fourth sheet contains the coding on information systems on canopy cover and their compliance with Open data criteria, with justifications, links or further information speciefied in comments, where necessary. </li> <li>The fifth sheet contains the coding on information systems on carbon sequestration and their compliance with Open data criteria, with justifications, links or further information speciefied in comments, where necessary. </li> <li>The sixth sheet contains the data on the individual scores per policy level/state per indicator group and the respective averages. More information on the operationaliation can be found in the methodology section of the thesis. </li> <li>The seventh sheet contains the data on the individual scores per policy level/state per indicator group and the respective averages, ranked from highest to lowest compliance. More information on the operationaliation can be found in the methodology section of the thesis. </li> <li>The last sheet gives information on the coding. </li> </ul>

opencc-zeroJun 2022View details →

Reading Information Technology and Libraries (volume 41, number 2, June 2022)

Today, for a good time, I applied my Reader Toolbox to the latest issue of ITAL for the two-fold purposes of: 1) just seing whether the Toolbox could function, and 2) determine the degree I could extract meaningful themes from the issue. Well, the Toolbox functioned, in that it did not crash nor output invalid data, and I do believe I could pull out themes, in that each issue's authors wrote about something distinctive, and I could identify those things. Below describes my process.

opencc-by-4.0Jun 2024View details →

Supporting Information Proteins Manuscript (Irfan Khawar)

This file is basically a Supporting information (SI) of my research paper. It consists of all the tables of the datasets for Chicken protein-water, Fish protein-water, Combined Muscle protein-water, and Bovine serum albumin-water partitioning. It also consists of the R codes used to generate figures and results. Supporting information is available free of charge (section 3 in SI).

Figure 4 in Do tracks yield reliable information on gaits? - Part 1: The case of horses

Figure 4. Typical tracks produced in the walk. The faster the walk (and the longer the limbs and the shorter the trunk), the greater is the distance (d) between the ipsilateral front- and hind hooves (i.e. the degree of overstepping increases); horizontal axis: distance covered in m. (a) Slow walk and (b) Fast walk of a German warmblood.

Figure 8 in Do tracks yield reliable information on gaits? - Part 1: The case of horses

Figure 8. Typical footfall pattern of the canter (German: Galopp) in its three varieties. (a) Right lead of a Paso Fino at slow speed; (b) Left lead of a German warmblood horse at normal speed; (c) Right lead of a medium sized German saddle horse at fast speed. The stride length increases with increasing speed. The Paso Fino places the hind hooves between the imprints of the fore hooves, because of slow speed. The warmblood has the same limb length as the Paso Fino, but it is placing the hind hooves beneath the prints of the fore hooves, because of higher speed. The German saddle horse is medium sized and places the hind hooves in front of the fore hooves. With increasing speed in the canter, the separation between all four hoofprints becomes clearer (in the example of right lead the group HL, HR, FL, FR.).

Figure 1 in Do tracks yield reliable information on gaits? - Part 1: The case of horses

Figure 1. Horse hooves. (a) Hind hoof and fore hoof of a horse seen from below; (b) Longitudinal section through the mechanically relevant elements of the autopodium. The hoof is shown during the middle of the stance phase, while highest loads are acting. Dots at the tips of the hooves are indicating the points used for track measurement. The difference between the imprints of hind hoof and fore hooves is not obvious, so that both are hardly discernible in most tracks.

Figure 3 in Do tracks yield reliable information on gaits? - Part 1: The case of horses

Figure 3. Relationship between trunk length and length of the limbs. The extremities are reduced to their "functional limb lengths". Step length (s) is the product of excursion angles (α or β) and limb lengths (for example s sin αl αĮ sin αĮ l). The = fa + fr longer the limbs, the lower the ground level below the animal, and the greater the distance (s) covered during each step, without any change of trunk length. The uppermost ground level indicates a lagging of the hind hoof behind the imprint of the fore hoof; the middle level indicates capping; the lowermost indicates overstepping. Excursion angles (α and β) are determined by the resultant GRF. Among living mammals, α usually is greater than αĮ, while β is commonly smaller than β Į; l – left forelimb in anteversion; l – fa fr left forelimb in retroversion; lha – left hind in anteversion; lhr – left hind in retroversion.

Figure 7 in Do tracks yield reliable information on gaits? - Part 1: The case of horses

Figure 7. Part of the original tracks comparing fast running pace (a) and fast trot (b). In the running pace the contralateral hoofprints are grouped together with overstepping of the fore hoof over the contralateral hind hoof. In the trot the ipsilateral hoofprints are grouped with an overstepping of the front hoof over the ipsilateral hind hoof. The horizontal axis shows the distance covered in cm.

Figure 2 in Do tracks yield reliable information on gaits? - Part 1: The case of horses

Figure 2. Raw data of two randomly chosen trackways; horizontal axis: distance covered in cm. (a) Slow tölt (i.e. amble); (b) Fast tölt of an Icelandic horse; FR – front right; HR – hind right; FL – front left; HL – hind left.

Figure 6 in Do tracks yield reliable information on gaits? - Part 1: The case of horses

Figure 6. Typical tracks produced in the trot of a German warmblood. With higher speed, the overstepping (d) of the ipsilateral hind hoof is increasing. (b) Slow trot: the hind hoof is placed right on top of the fore hoof imprint (capping); (a) fast (extended) trot, which leads to marked overstepping. A third possibility is the placing of the hind hoof in front of the fore hoof at very slow speed (this is rarely done and not shown here).

Figure 5 in Do tracks yield reliable information on gaits? - Part 1: The case of horses

Figure 5. Typical track produced in the tölt (amble) of an Icelandic horse. In the amble the overstepping (d1) is greater than in the walk and the contralateral hoofprints are close to each other at fast speeds (d2). This is similar to the pace. (a) Slow tölt; (b) fast tölt.

opencc-by-4.0May 2024View details →

Disentangling the influence of reservoir abundance and pathogen shedding on zoonotic spillover of the Leptospira agent in urban informal settlements

The following datasets were used to carry out the analyses in "Disentangling the influence of reservoir abundance and pathogen shedding on zoonotic spillover of the Leptospira agent in urban informal settlements".  rat_abundance.csv - dataset for rat abundance model. rat_shedding.csv - dataset for individual rat shedding model. human_infections.csv - dataset for human infections (coordinates and valley removed for anonymity). prediction_grid.csv - prediction dataset. Codebook for repository data.pdf - codebook for the included datasets.

opencc-by-sa-3.0Jun 2024View details →

IXI – Information eXtraction from Images | Cortical Volume

This dataset contains cortical volume data, computed from the T1-w images present in the original IXI dataset. Structural pre-processing was conducted using FreeSurfer 6.0, with default parameters, and includes the following steps: <ul> <li>motion correction</li> <li>skull stripping</li> <li>removal of cerebellum and brain stem</li> <li>intensity correction</li> <li>segmentation</li> <li>tassellation</li> <li>smoothing </li> <li>topology correction</li> </ul> Cortical volume was produced for 68 brain regions from the Desikan-Killiany brain atlas (34 per hemisphere, measured in mm3) The original data has been collected as part of the project: IXI – Information eXtraction from Images (EPSRC GR/S21533/02) Information and data for the original IXI dataset can be found <a href="https://brain-development.org/ixi-dataset/" target="_blank" rel="noopener">here</a>. This data is made available under the Creative Commons CC BY-SA 3.0 license. If you use the IXI data please acknowledge the source of the IXI data.

Fig. 8 in Impact of increasing morphological information by micro-CT scanning on the phylogenetic placement of Darwin wasps (Hymenoptera, Ichneumonidae) in amber

Fig. 8 Holotype of Rhyssa gulliveri sp. nov. A Habitus of specimen, lateral view. B Rugae dorsally on mesoscutum. C Face, anterior view, partially hidden by spider inclusion and milky coatings. D Face, more laterally with visible mandibles. E First tergite on metasoma, lateral view. F Head and mesoscutum, dorsal view. G Interpretative drawing with an additional drawing of the propodeum and T1 in dorsal view, where photos and micro-CT scan were used as templates. Scale bar A: 2 mm, B and C: 1 mm, D: upper 1 mm, lower 2 mm

Fig. 7 in Impact of increasing morphological information by micro-CT scanning on the phylogenetic placement of Darwin wasps (Hymenoptera, Ichneumonidae) in amber

Fig. 7 Holotype of Firkantus freddykruegeri gen. et sp. nov. A Habitus of specimen, lateral view. B Anterior view of face, right side with facial structures indicated. C Fore wing with folds indicating wing venation. D Anterior part of metasoma, dorsal view. E Posterior part of metasoma, with parameters and aedeagus. F Interpretative drawing with an additional drawing of the propodeum and T1 in dorsal view, where photos and micro-CT scan were used as templates. Scale bar A: 1 mm, B: 0.5 mm, F: lower 1 mm, right 0.5 mm

Fig. 3 in Impact of increasing morphological information by micro-CT scanning on the phylogenetic placement of Darwin wasps (Hymenoptera, Ichneumonidae) in amber

Fig. 3 RoguePlot placement of Pimplinae fossil Firkantus freddykruegeri gen. et sp. nov. before and after micro-CT scanning. The plots include all branches from the majority-rule consensus tree where the attachment probability was higher than 1%. A Firkantus freddykruegeri gen. et sp. nov. with colours indicating newly revealed body characteristics. Blue colouration represents newly added measurements; orange highlights either newly coded characters or characters where states could be reduced after the CT scan. B Placement before CT scanning. C Placement after CT scanning

Fig. 6 in Impact of increasing morphological information by micro-CT scanning on the phylogenetic placement of Darwin wasps (Hymenoptera, Ichneumonidae) in amber

Fig. 6 Holotype of Triclistus levii sp. nov. A Partial fore wing. B Metasoma, posterior end with the parameres. C Habitus of specimen, lateral view. D Head and mesoscutum, dorsal view. E Head. F Interpretative drawing with an additional drawing of the propodeum and T1, in dorsal view, where photos and micro-CT scan were used as templates. Scale bars A: 1 mm, B: 0.5 mm C: 1 mm F: bottom 1 mm, top right 0.5 mm

Fig. 9 in Impact of increasing morphological information by micro-CT scanning on the phylogenetic placement of Darwin wasps (Hymenoptera, Ichneumonidae) in amber

Fig. 9 Holotype Magnocula sarcophaga gen. et sp. nov. A Habitus of specimen, ventral view. B Habitus of holotype, lateral view. CT scan of C head and mesoscutum in dorsal view, D face in anterior view, and E last tergites with ovipositor and sheaths. F Photo of a partial fore wing, in top left is T2 with its rugopunctate to striate structure. G Interpretative drawing with an additional drawing of the propodeum and T1 in dorsal view, where photos and micro-CT scan were used as templates. Scale bar A: 1 mm, F: 0.5 mm G: lower 1 mm, right 0.5 mm