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Data and Code: Spatiotemporal Variability of Fin Whale and Blue Whale Calls Detected by Land Seismometers in the Lower St. Lawrence Seaway

<h3>Code</h3> MATLABWhaleDetectionCodeNWA.zip The code used in the manuscript Spatiotemporal Variability of Fin Whale and Blue Whale Calls Detected by Land Seismometers in the Lower St. Lawrence Seaway (Goblot et al., in review) to detect whale calls in seismic waveforms was written by Alexandre Plourde and uploaded here with permission. This .zip file contains 3 folders, ‘Numerical Methods’, ‘Signal Processing’ and ‘Whale Subroutines’, as well as 3 main scripts (x2 for fin and blue whales).  <ul> <li>Pre-processing: <ul> <li>Convert .mseed to SAC files and rename with the format: YYYY.MM.DD.NETWORK.STATION..CHANNEL.SAC. Each SAC file must then be placed in a folder named with the format YYYYMMDD, referred to as events (evs) in the code. Each of these folders must then be placed into a single folder known as the events directory (drE) in the code.</li> </ul> </li> </ul> <ul> <li>Processing: Fin whales <ul> <li>Run ‘RecordWhaleNoiseLSZ.m’ for fin whales. This reads through daily SAC files, and computes fin whale power ratio every 120 s. The folder ‘FinWhalePower’ (drP) should now be created. These contain text files with the power ratios (W) for every window at each station.</li> <li>Run ‘createFinWhaleDectectionList.m' to check which 120s time windows have W > threshold (3.0 in our case). These time windows are stored in the matrix FWD.</li> <li>Run 'RecordFinWhaleCallsLSZ.m' to identify individual whale calls within each of the 120s time segments in the FWD matrix. The labelled call times are stored in the matrix FWC.</li> </ul> </li> </ul> <ul> <li>Processing: Blue whales <ul> <li>Run ‘RecordBlueWhalesLSZ.m’ for fin whales. This reads through daily SAC files, and computes fin whale power ratio every 120 s. The folder ‘BlueWhalePower’ (drP) should now be created. These contain text files with the power ratios (W) for every window at each station.</li> <li>Run ‘createBlueWhaleDectectionList.m' to check which 720s time windows have W > threshold (1.5 in our case). These time windows are stored in the matrix BWD.</li> <li>Run 'RecordFinWhaleCallsLSZ.m' to identify individual whale calls within each of the 720s time segments in the BWD matrix. The labelled call times are stored in the matrix BWC.</li> </ul> </li> </ul> <h3>Data</h3> The whale detection code was applied to seismic waveform data downloaded through the EarthScope Consortium Web Services (<a href="https://service.iris.edu/" target="_blank" rel="noopener">https://service.iris.edu/</a>), including the following seismic network: CN (Natural Resources Canada, 1975). All whale calls were detected using the characteristic reccurence interval method (MATLABWhaleDetectionCodeNWA.zip). Table S2 WhaleDetectionsFeb2020Jan2022LSZ.mat <ul> <li>This dataset contains the center time of fin whale and blue whale detections and calls, from land 6 seismometers (CNQ, ICQ, SMQ, SNFQ, PMAQ, RISQ) in the Lower St-Lawrence Seaway between February 2020 and January 2022. <ul> <li>FWD_ <ul> <li>Column 1: list of fin whale detections (2 minute time window with presence)</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>BWD_ <ul> <li>Column 1: list of blue whale detections (12 minute time window with presence)</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>FWC_ <ul> <li>Column 1: list of individual ~1s fin whale calls</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>BWC_ <ul> <li>Column 1: list of individual ~8s blue whale calls</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>stadir: list of stations and labels (1-6)</li> </ul> </li> </ul> Table S3 a) MonthlyBlueWhaleDetectionsOct2015Jan2022.csv and b) MonthlyFinWhaleDetectionsOct2015Jan2022.csv <ul> <li> <div> <div> <div> Number of monthly a) fin whale detections and b) blue whale detections from October 2015 to January 2022. Empty cells indicate periods when stations were not operating. Quiet day detections are included. Note the Oct 2015-Feb 2020 catalogue is from Plourde and Nedimović (2022) and includes up to 14 stations throughout this period from the following seismic networks: CN (Natural Resources Canada, 1975) and C8 (Natural Resources Canada, 2002). The Feb 2020-Jan 2022 catalogue is from Goblot et al. (in review) and includes the same stations from Table S1. </div> </div> </div> </li> </ul> <h3>Additional</h3> Movie S1 LSLSWhales.mp4 <ul> <li>This movie contains an audiovisual representation of a series of fin whale calls and blue whale calls detected by 2 land seismometers in the Lower St-Lawrence Seaway. The fin whale detection was recorded by land seismometer CNQ (Côte-Nord) on Dec 12 2021 from 18:19:40 to 18:21:40. The blue whale detection was recorded by land seismometer SNFQ (Sainte-Félicité) on Aug 22 2021 from 05:36:00 to 05:48:00.</li> <li>The MATLAB soundsc(x) function was applied to seismic waveform data with fin whale and a blue whale calls. The signal with the fin whale detection is bandpassed from 18-21 Hz and the blue whale signal is bandpassed from 16-18 Hz. These signals were sped up 1000x in order to make them audible.</li> <li>The audio file was then uploaded to veed.io to produce a frequency response visualization of the whale calls.</li> <li>More audiovisuals can be viewed <a href="https://seismicsoundscapes.myportfolio.com/" target="_blank" rel="noopener">here</a>.</li> </ul>

Dataset: Drone orthomosaics for remote detection of Pacific oysters (Kingsbridge Estuary, UK)

A field campaign was carried out at two distinctive intertidal cases at Kingsbridge Estuary, UK. Drone images were collected around low tide on each site of the estuary selected by the high number of Pacific oysters reported and their very different characteristic landscapes of mudflats and rocky shore. Drone images were collected using a DJI Phantom4 Pro v2.0 in a pre-planned grid mission using the ”DJI GS Pro” mission planner software. Photos were acquired at 10 m altitude (resulting in a pixel size of &sim;0.3 px/cm) with a front overlap ratio of 80% and a side overlap of 70%. The two sites included in this work are: • Site A: Collapit Creek mudflats. An area of approximately 1.67 ha intertidal mudflat was surveyed on 13th May 2022 (lat, lon = 50.259245, -3.771041). • Site B: Scoble Point rocky shore. The intertidal region that follows the shoreline for approximately 250 m was surveyed on 16th May 2022, Figure 3 (50.240164, -3.755421).

opencc-by-4.0Apr 2024View details →

Dataset of chemicals of emerging concern detected in the marine environment in central and northern Patagonia in Chile

This repository encompasses the environmental concentrations of chemicals of emerging concern (CECs), such as pesticides, pharmaceuticals, and industrial chemicals, co-occurring in surface water, porewater, and sediment throughout central and northern Patagonia, Chile.

openmit-licenseMay 2024View details →

Widespread Error Detection in Large Scale Continuous Integration Systems

Dataset of 5000 json documents describing verification process of <a href="https://github.com/facebook/react">React</a> project collected between 2023 and 2024. Errors recorded in this dataset were used in the presentation at CCIW workshop. Abstract: Continuous Integration systems are widely used in the software industry to validate and integrate code changes into central repositories. Their effectiveness can be impacted by non-deterministic tests which can fail in the absence of any regression. Integration tests which depend on external services are particularly prone to this problem. We present a system which allows us to reduce the impact of non-deterministic failures by detecting widespread errors. The key assumption, which works well in practice, is that developers tend not to make identical mistakes simultaneously. If we observe a widespread error, it strongly suggests there is a problem with upstream services and not with the code change being evaluated. The detection algorithm consists of three main phases. First, the error text gets extracted from logs using predefined heuristics or automated methods. Then, this text gets fuzzy matched against a database of recently observed errors. Finally, statistics get checked to determine if they meet the criteria for a widespread error. When an error meets the criteria it either gets demoted to a warning or it gets enriched with information about an ongoing incident.

Detection of abrupt changes in East Asian monsoon from Chinese loess and speleothem records

There is a great interest concerning recent occurrences of tipping points in the climate system and great concern about those that could occur in the near future as a result of anthropogenic forcing. A lot of attention has been devoted to the study of past Dansgaard-Oeschger events, abrupt warmings of about 12°C on a time-scale of about 50 yrs that occurred during the last glacial period. Great effort is also dedicated to understanding the Atlantic Meridionnal overturning circulation and the Amazon forest dieback, which are already entering an unstable regime leading to tipping behavior. Instead, here we focus on the study of critical transitions in the SE Asian Monsoon that have occurred in the past 3.6 Myrs by a novel combination of advanced statistical tools (KS-test, recurrence quantification analysis). The SE Asian Monsoon is characterized by variations in the grain size with the occurrence of coarse material characterizing a strong winter monsoon mechanism with grains transported from the Chinese northern deserts by strong winds generated by the Siberian High located northward. By contrast intervals of fine grain size characterized periods during which the summer monsoon was rather reinforced. We have analyzed high-resolution grain-size datasets derived from Chinese loess sequences, i.e. the CHILOMOS and the LGS640 datasets, that we compare with the Chinese composite speleothem d18O records that arguably provide one the best representation of the Earth’s climate in the last 650 kyrs. Although visually observed rapid grain-size variations were previously interpreted as representing millennial-scale variations, our statistical analysis shows that both winter and summer monsoons co-varied at glacial-interglacial to millennial timescales. Analyzing a third dataset, i.e., MQSG, our statistical analysis shows that both winter and summer monsoon variations reflect a three-stage evolution of increasing intensity: (1) from 3.6 Ma to 2.6 Ma, (2) from 2.6 Ma to 1.2 Ma, and (3) from 1.2 Ma to present, with the winter monsoon strength increasing over these3 main steps. List of tables Table 1 KS test of the NGRIP and Hulu cave d18O for the last climate cycle. Comparison of the dates of abrupt warmings/moistening (left) and cooling/drying transitions (right). Labels for NGRIP according to Rasmussen et al. (2014) and for Hulu cave according to Wang et al. (2008). Table 2 KS test of the Chinese speleothem d18O and of the CHILOMOS grain-size composite for the last two climate cycles. Comparison of the dates of abrupt moistening (left) and drying transitions (right). Labels for the moistening transitions in the Chinese speleothem are from Wang et al. (2008) and in CHILOMOS from Yang and Ding (2014). The labels of the drying in CHILOMOS are from the present study Table 3 RQA of the Chinese speleothem d18O and of the CHILOMOS grain-size composite for the last two climate cycles. Dates of the minima are identified by the RR prominence, shown together with the equivalent transitions detected by the KS method. For easier reading, the dates have been re-ordered from younger to older. The most significant minima are highlighted in yellow. The original ranking is given in Suppl. Tab 1. Table 4 KS test of the Chinese speleothem  d18O and of the LGS640 dataset for the last 640 Myrs. Comparison of the dates of abrupt moistening and drying transitions in both records. The dates found in both records are highlighted in red and in blue for drying or moistening events respectively. Table 5 RQA of the LGS640 grain-size composite for the last seven climate cycles. Dates of the minima are identified by the RR prominence. The most significant minima (RR prominence >0.5) are highlighted in yellow.  Table 6 KS test and RQA of MGSQ grain dataset for the last 3.6 Myrs. In this analysis the moistening and drying transitions is labeled as warming and cooling. On the left, KS results with the corresponding marine isotope stage (MIS) boundaries. On the right, RQA results with minima ordered according their prominence value. Dates with RR>0.6 are highlighted in yellow. Table Supp.1. Abrupt transitions over the past 130 kyrs BP from the NGRIP, Chinese Speleothem and CHILOMOS records. Identification of the common abrupt warmings or moistenings on the left, and abrupt coolings or dryings on the right.  Differences between the highest and lowest transition dates. Indication of the NGRIP and Chinese interstadials and stadials (GI-GS and A-SA respectively). Table Supp.2. RQA of the 250 kyrs Chinese speleothem and CHILOMOS records ranked according the RR prominence, the chronology. Indication of the time difference between the identified transitions. Table Supp.3. Comparison of the KS-test results from the LGS 640 and the Chinese speleothem over the past 650 kyrs. Indication of the Marine isotope stratigraphy and the number of cool and warm transitions and the percentage of drying events per climate cycle

Data to accompany the outlier-waveform-detection Github repository (internal globus pallidus, GPi)

This repository contains data based on neuronal recordings from two monkeys (G and I, in the pre- and post-MPTP states) that serve as input to the code provided at <a href="https://github.com/turner-lab-pitt/outlier-waveform-detection">https://github.com/turner-lab-pitt/outlier-waveform-detection</a>. Text files located within that Github repository provide detailed instructions on how these data may be used with that code.  As described in those text files, extra data are provided for Monkey G, in the pre-MPTP state. The data-description.txt file provides detailed information regarding the contents of each zipped tar archive. Briefly, the most important components of the files are the "snips" (individual spike waveforms) from the two monkeys and MPTP states, as extracted for each of a series of single sorted units from the internal globus pallidus (GPi).  The additional G-Pre data provides examples of the high-pass filtered voltage signals from which these snips were extracted.  All data are stored in the Matlab .mat format. All zipped files can be decompressed with 7-zip: <a href="https://www.7-zip.org/" target="_blank" rel="noopener">https://www.7-zip.org/</a> These data and the associated Github code were used for analyses reported in an in-preparation manuscript (Kase et al., "Movement-related activity in the internal globus pallidus of the parkinsonian macaque"), and also with a preprint that is currently under review: <div> <div>Detecting rhythmic spiking through the power spectra of point process model residuals</div> </div> <div>Karin M. Cox, Daisuke Kase, Taieb Znati, Robert S. Turner</div> <div>bioRxiv 2023.09.08.556120; doi: <a href="https://doi.org/10.1101/2023.09.08.556120" target="_blank" rel="noopener">https://doi.org/10.1101/2023.09.08.556120</a></div> <div> </div> This research was funded in part by Aligning Science Across Parkinson's [ASAP-020519] through the Michael J. Fox Foundation for Parkinson's Research (MJFF). For the purpose of open access, the authors have applied a Creative Commons Attribution 4.0 International (CC BY) public copyright license to this dataset.

Data for "Spectro-photometry of Phobos simulants: I. Detectability of hydrated minerals and organic bands"

Data underlying the paper: "Spectro-photometry of Phobos simulants: I. Detectability of hydrated minerals and organic bands". The dataset contains visible, near-infrared, and mid-infrared spectra of various samples, including phyllosilicates, coals, and Phobos simulants. Measurements at different observation geometries are available for the Phobos simulants in the visible and near-infrared.

Dataset fOr herding and predatoR detectIon with the use of robotS (DORIS)

Dataset with images and annotations (semantic masks for YOLO and other architectures) of sheep, wolves, persons and depth images in order to carry out experiments of robots used for herding sheep and detect potential predators as wolves. It is divided into two different URLs since it is greater than 50GB. This dataset contains images of persons, wolves, sheep and the depth images that can be used to simulated environments and they have the depth estimated from the images using Depth Anything. We gratefully acknowledge the financial support of Grant TED2021-132356B-I00 funded by MCIN/AEI/10.13039/501100011033 and by the "European Union NextGenerationEU/PRTR". Since the original dataset exceeds 50 GB (https://doi.org/10.57967/hf/2059), there are three datasets associated: <ul> <li>a dataset that contains YOLO annotations for these images.</li> <li>a second dataset contains images of Sheep, (10.5281/zenodo.11313800)</li> <li>a third dataset contains Person, Wolf classes, and depth maps generated using Depth Anything, which can be utilized for simulating external environments or outdoor navigation. (this dataset 10.5281/zenodo.11313966)</li> </ul>

Dataset fOr herding and predatoR detectIon with the use of robotS (DORIS)

Dataset with images and annotations (semantic masks for YOLO and other architectures) of sheep, wolves, persons and depth images in order to carry out experiments of robots used for herding sheep and detect potential predators as wolves. It is divided into two different URLs since it is greater than 50GB. This dataset contains images of persons, wolves, sheep and the depth images that can be used to simulated environments and they have the depth estimated from the images using Depth Anything. We gratefully acknowledge the financial support of Grant TED2021-132356B-I00 funded by MCIN/AEI/10.13039/501100011033 and by the "European Union NextGenerationEU/PRTR". Since the original dataset exceeds 50 GB (https://doi.org/10.57967/hf/2059), there are three datasets associated: <ul> <li>a dataset that contains YOLO annotations for these images.</li> <li>a second dataset contains images of Sheep, (this dataset 10.5281/zenodo.11313800)</li> <li>a third dataset contains Person, Wolf classes, and depth maps generated using Depth Anything, which can be utilized for simulating external environments or outdoor navigation. ( 10.5281/zenodo.11313966)</li> </ul>

Fig 4 in Development of specific PCR assays for the detection of Cryptocaryon irritans

Fig 4 Agarose gel electrophoresis of PCR products amplified from the simulated water body using Cryptocaryon irritans-specific primer set P1/S15. Lanes 1 and 10 represent a DNA size marker, lanes 2 to 7 represent C. irritans from the infected water, lane 8 represents the uninfected water, and lane 9 represents no-DNA control

Fig 1 in Development of specific PCR assays for the detection of Cryptocaryon irritans

Fig 1 Agarose gel electrophoresis of PCR products amplified using conserved primer set P1/NC2 (a), Cryptocaryon irritans-specific primer set P1/S15 (b), and Ichthyophthirius multifiliis-specific primer set S01/S02 (c). Lanes 1 and 11 represent a DNA size marker. Lanes 2 to 9 represent C. irritans, Pseudokeroronpsis rubra, Pseudokeroronpsis carnae, Euplotes sp. 1, I. multifiliis, Pseudourostyla cristata, Paramecium caudaium (cf. Table 1), and host (fish) DNA, respectively. Lane 10 represents no-DNA control

Fig 2 in Development of specific PCR assays for the detection of Cryptocaryon irritans

Fig 2 Agarose gel electrophoresis of PCR products amplified from mixed genomic DNA of C. irritans and I. multifiliis. Lanes 1 and 7 represent a DNA size marker. Lane 2 presents the mixture of C. irritans and I. multifiliis DNA amplified using the primer set P1/S15 and lane 3 represents the genomic DNA of I. multifiliis. Lane 4 represents the mixed genomic DNA of C. irritans and I. multifiliis amplified using the primer set S01–S02. Lane 5 represents the genomic DNA of C. irritans using the primer set S01–S02. Lane 6 represents no-DNA control

Fig 3 in Development of specific PCR assays for the detection of Cryptocaryon irritans

Fig 3 Evaluation of the sensitivity of the specific PCR assay for Cryptocaryon irritans (primer set P1/S15) by agarose gel electrophoresis. Lanes 1 and 13 represent a DNA size marker. Lane 2 represents DNA undiluted (450 ng/µl), and lanes 3 to 11 represent C. irritans DNA diluted for 1:50 (9 ng/µl), 1:100 (4.5 ng/µl), 1:200 (2.25 ng/µl), 1:400 (1.13 ng/µl), 1:800 (0.56 ng/µl), 1:103 (0.45 ng/µl), 1:104 (45 pg/µl), 1:105 (4.5 pg/µl), and 1:106 (0.45 pg/µl), respectively. Lane 12 represents no-DNA control

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>

The Potential of Deep Learning Object Detection in Citizen-Driven Snail Host Monitoring to Map Putative Disease Transmission Sites

<a name="_Hlk158802145"></a>Schistosomiasis is a neglected tropical disease caused by parasitic flukes transmitted by freshwater snails. Despite increasing efforts of mass drug administration, schistosomiasis remains a public health concern and the World Health Organization recommends complementary snail control. To address the need of broad-scale and actual snail distribution data to guide snail control, we adopted a citizen science approach and recruited citizen scientists (CS) to perform weekly snail sampling in the endemic setting in Uganda. Snails were identified, sorted and counted according to genus, photographed and uploaded for expert-led validation and feedback. However, expert validation is time-consuming and introduces a delay in verified data output. Thus, artificial intelligence could provide a solution by means of automated detection and counting of multiple snails collected from the field. Trained on approximately 2500 citizen-collected images, the resulting model can simultaneously detect and count Biomphalaria and Radix snails with average precision of 98.1% and 98.8% respectively. The object detection model also agreed with the expert’s decision averagely for 98.8% of the test images and could be ran in real-time (24.6 images per second). We conclude that the automatic and instant detection can rapidly and reliably validate data submitted by CS in the field, ultimately minimizing the expert validation efforts and thereby facilitating the mapping of putative schistosomiasis transmission sites. An extension to a mobile application could equip citizen scientists in remote areas with instant learning opportunities and expert-like identification skills, overcoming the need for on-site training and extensive expert intervention.

RU-AI: A Large Multimodal Dataset for Machine Generated Content Detection

This repository contains all the collected and aligned data for RU-AI dataset. It is constructed based on three large publicly available datasets: Flickr8K, COCO, and Places205, by adding their corresponding machine-generated pairs from five different generative models in each modality.

opencc-by-4.0Jun 2024View details →

Improved Detection of Drug-Induced Liver Injury by Integrating Predicted in vivo and in vitro Data

This repository provides datasets for the study: https://broad.io/DILIPredictor Full Paper: https://www.biorxiv.org/content/10.1101/2024.01.10.575128v1 This work is on enhancing the early detection of Drug-Induced Liver Injury (DILI) through the integration of predicted in vivo and in vitro data. This project utilizes advanced machine learning models and chemical informatics to predict the likelihood of DILI for various compounds. For code see: <a href="https://github.com/srijitseal/DILI">https://github.com/srijitseal/DILI</a> Drug-induced liver injury (DILI) has been significant challenge in drug discovery, often leading to clinical trial failures and necessitating drug withdrawals. The existing suite of in vitro proxy-DILI assays is generally effective at identifying compounds with hepatotoxicity. However, there is considerable interest in enhancing in silico prediction of DILI because it allows for the evaluation of large sets of compounds more quickly and cost-effectively, particularly in the early stages of projects. In this study, we aim to study ML models for DILI prediction that first predicts nine proxy-DILI labels and then uses them as features in addition to chemical structural features to predict DILI. The features include in vitro (e.g., mitochondrial toxicity, bile salt export pump inhibition) data, in vivo (e.g., preclinical rat hepatotoxicity studies) data, pharmacokinetic parameters of maximum concentration, structural fingerprints, and physicochemical parameters. We trained DILI-prediction models on 888 compounds from the DILIst dataset and tested on a held-out external test set of 223 compounds from DILIst dataset. The best model, DILIPredictor, attained an AUC-ROC of 0.79. This model enabled the detection of top 25 toxic compounds compared to models using only structural features (2.68 LR+ score). Using feature interpretation from DILIPredictor, we were able to identify the chemical substructures causing DILI as well as differentiate cases DILI is caused by compounds in animals but not in humans. For example, DILIPredictor correctly recognized 2-butoxyethanol as non-toxic in humans despite its hepatotoxicity in mice models. Overall, the DILIPredictor model improves the detection of compounds causing DILI with an improved differentiation between animal and human sensitivity as well as the potential for mechanism evaluation. DILIPredictor is publicly available at <a href="https://broad.io/DILIPredictor">https://broad.io/DILIPredictor</a> for use via web interface and with all code available for download and local implementation via <a href="https://pypi.org/project/dilipred/">https://pypi.org/project/dilipred/</a>.

opencc-by-4.0Sep 2022View details →

Figure 8. F1 scores for YOLOv5 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean

Figure 8. F1 scores for YOLOv5 indicating the harmonic mean between precision and recall scores. Data indicated that detection results for both species would be best at a confidence threshold of 0.298.

opencc-by-4.0Sep 2022View details →

Figure 11. YOLOv5 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean

Figure 11. YOLOv5 precision (A), recall (B), and F1 score (C) changes as a function of Amoronthus polmeri density (plants m−2).