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Survey of Wild Bee Pollinators on Nyssa Sylvatica at Harvard Forest since 2021
Black gum (Nyssa sylvatica) is amongst the latest blooming canopy species to produce vast numbers of flowers and abundant nectar and pollen within forests of the Northeastern United States, a position previously held by the American Chestnut (Castanea dentata). Prior research indicates N. sylvatica is insect pollinated and wild bees have been observed visiting flowers; we are unaware, however, of any detailed surveys and/or characterization of the Nyssa-associated wild bee community in the Northeastern United States. Wild bee species frequent the canopy from early to late spring, presumably to forage, prior to being found in blooming crops such as apple and strawberry later in the season. The late bloom time of N. sylvatica (in early June) may extend floral resource availability in the temperate forest canopy and support forest-associated wild bee communities prior to the bloom of summer-flowering plant species.
Smartphone sensor data (accelerometer, virtual keyboard) collected in-the-wild by Parkinson's Disease patients and Healthy Controls
<p>For detailed description of the dataset see the relevant <a href="https://www.nature.com/articles/s41598-020-78418-8">journal article</a>.</p> <p>Python code for model inference and training is available <a href="https://github.com/alpapado/deep_pd">here</a>.</p> <p> </p> <p><strong>DESCRIPTION</strong></p> <p>The dataset contains accelerometer recodings and keyboard typing data contributed by Parkinson's Disease patients and Healthy Controls. Accelerometer data consists of acceleration values recorded during phone calls and typing data consist of virtual keyboard press and release timestamps. The dataset is divided into two parts: the first part, called SData, contains data from a small, medically evaluated, set of users, while the second part, called GData, contains recordings from a large body of users with self-reported PD labels.</p> <p>The dataset is organized into 5 pickle files:</p> <p>1. <strong>imu_sdata.pickle</strong>: Contains the tri-axial accelerometer recordings for the SData part of the dataset in the form of a list of python dictionaries, one for each participating subject. Accelerometer data have been pre-processed to a sampling frequency of 100Hz and come segmented into non-overlapping 5 second windows. Hence, a segment's dimension will be 500 x 3 samples.</p> <p>Sample Python code for accessing the acceleration data of a subject</p> <pre><code class="language-python">sdata = pickle.load(open('imu_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] # subject_data is a list of length 4 ## The actual data is in the last element of the list acc_segments = subject_data[-1] num_acc_sessions_for_subject = len(acc_segments) acc_segments_for_first_session = acc_segments[0] acc_segments_for_second_session = acc_segments[1] # ..etc In: print(acc_segments_for_first_session.shape) Out: (3, 500, 3) ## The first accelerometer session for this subject consists of 3 five-second segments. In: print(acc_segments_for_second_session.shape) Out: (8, 500, 3) ## The second accelerometer session for this subject consists of 8 five-second segments.</code></pre> <p>2. <strong>imu_gdata.pickle</strong>: Same layout as imu_sdata.pickle but with data ffrom GData subjects.</p> <p>3. <strong>typing_sdata.pickle</strong>: This files contains the typing data originating from the SData part of the dataset. It is a list of dictionaries with one entry per subject. The typing data are given in the form of concatenated hold time (the time elapsed between press and release of the virtual key) and flight time (the time between releasing a key and press the next) histograms, computed over 10ms bins in the range of [0, 1]s for hold time and [0, 4]s for flight time (an additional bin that contains the values in the (1, +oo) and (4, +oo) intervals is also used). So, the total length of the concatenated histogram is 1000/10 + 1 + 4000/10 + 1 = 502.</p> <p>Sample Python code for accessing the typing data of a subject:</p> <pre><code class="language-python">sdata = pickle.load(open('typing_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] ## The actual data is in the first element of the list typing_histograms = subject_data[0] num_typing_sessions_for_subject = len(typing_histograms) typing_hist_for_first_session = typing_histograms[0] typing_hist_for_second_session = typing_histograms[1] # ..etc In: print(typing_hist_for_first_session.shape) Out: (502, ) ht_hist = typing_hist_for_first_session[:101] # Hold time histogram of the session ft_hist = typing_hist_for_first_session[101:] # Flight time histogram of the session</code></pre> <p>4. <strong>typing_gdata.pickle</strong>: Same layout as typing_sdata.pickle but with data from GData subjects.</p> <p>5. <strong>subject_metadata.pickle</strong>: A list of dictionaries with one entry per subject containing demographic information. The relevant demographic fields have the following interpretation:<br> 'age': Year of birth,<br> 'gender_id': 0 indicates male, 1 indicates female<br> 'healthstatus_id': 0 indicates PD patient, 1 indicates Healthy with PD family history, 2 indicates Healthy without PD family history</p> <p>In the case of SData subjects, there is also symptom UPDRS scores from one or two medical examinations. These are ncoded in the fields med_eval_1 and med_eval_2.</p> <p> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected is a multi-center study approved in each country available (for more info visit: <a href="http://www.i-prognosis.eu/?page_id=3606">http://www.i-prognosis.eu/?page_id=3606</a>). Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu/">i-prognosis.eu</a>).</p> <p> </p> <p><strong>CORRESPONDANCE</strong></p> <p>Any inquiries regarding this dataset should be adressed to:</p> <p>Mr. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate)</p> <p>Multimedia Understanding Groupmug<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996359, 996365 <br> Fax: +30 2310 996398<br> E-mail: alpapado@mug.ee.auth.gr</p> <p> </p> <p><br> </p> <p> </p>
Ecology and environment predict spatially stratified risk of H5 highly pathogenic avian influenza clade 2.3.4.4b in wild birds across Europe
<p>The data in this repository were used to conduct the analysis outlined in the following bioRxiv preprint:</p> <ul> <li>Sarah Hayes, Joe Hilton, Joaquin Mould-Quevedo, Christl Donnelly, Matthew Baylis, Liam Brierley (2025) "Ecology and environment predict spatially stratified risk of H5 highly pathogenic avian influenza clade 2.3.4.4b in wild birds across Europe" <em>bioRxiv</em> doi:10.1101/2024.07.17.603912</li> </ul> <p>The codes used for the analyses are available at https://github.com/sarahhayes/avian_flu_sdm/ </p> <p>The following lookup table can be used to cross-reference between the variable descriptions in Tables 1 and 2 of the preprint and the files in this repository:</p> <p> </p> <table> <tbody> <tr> <td> <h3> Variable description </h3> </td> <td> <h3> Filename </h3> </td> </tr> <tr> <td> Minimum elevation (metres above sea level) </td> <td> elevation_min_10kres.tif </td> </tr> <tr> <td> Maximum elevation (metres above sea level) </td> <td> elevation_max_10kres.tif </td> </tr> <tr> <td> Difference between minimum and maximum elevation </td> <td> elevation_diff_10kres.tif </td> </tr> <tr> <td> Modal elevation (metres above sea level) </td> <td> elevation_mode_10kres.tif </td> </tr> <tr> <td> Normalised Difference Vegetation Index (NDVI) </td> <td> ndvi_*_quart_2022_eco_rasts.tif </td> </tr> <tr> <td> Land cover </td> <td> landcover_output_full_2022_10kres.tif </td> </tr> <tr> <td> Distance to coast </td> <td> dist_to_coast_10kres.csv </td> </tr> <tr> <td> Distance to inland water </td> <td> dist_to_water_output_10kres.csv </td> </tr> <tr> <td> Relative humidity </td> <td> mean_relative_humidity_q*_10kres_eco_quarts.tif </td> </tr> <tr> <td>Seasonal weighted mean of the month-wise difference in the minimum temperature and maximum temperature (degrees Celsius) </td> <td> mean_diff_*_quart_eco_rasts.tif </td> </tr> <tr> <td>Seasonal weighted mean of monthly mean temperatures (degrees Celsius) (Mean monthly temperature for each month calculated using: Mean temperature = Minimum temperature + diurnal range/2)</td> <td> mean_mean_*_quart_eco_rasts.tif </td> </tr> <tr> <td>Seasonal temperature variation (degrees Celsius)<br>(Difference between the maximum and minimum of<br>mean monthly temperature values across months<br>majority-represented within the season)</td> <td> variation_in_quarterly_mean_temp_q*_eco_rasts.tif </td> </tr> <tr> <td> Precipitation </td> <td> mean_prec_*_quart_eco_rasts.tif </td> </tr> <tr> <td>Seasonal mean of daily zero-degree isotherm (metres<br>above sea level) </td> <td> isotherm_mean_q*_eco_rasts.tif </td> </tr> <tr> <td>Number of days the zerodegree isotherm was below 1 metre at midday at Coordinated Universal Time (UTC) </td> <td> isotherm_midday_days_below1_q*_eco_quarts.tif </td> </tr> <tr> <td> Chicken density </td> <td> chicken_density_2010_10kres.tif </td> </tr> <tr> <td> Duck density </td> <td> duck_density_2010_10kres.tif </td> </tr> <tr> <td> <em>Anatinae</em> (dabbling ducks) </td> <td> anatinae_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Anserinae</em> (swans and geese) </td> <td> anserinae_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Ardeidae</em> (herons) </td> <td> ardeidae_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Arenaria/Calidris</em> (turnstones and sandpipers) </td> <td> arenaria_calidris_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Aythyini</em> (diving ducks)</td> <td> aythyini_rast_eco_bds.tif</td> </tr> <tr> <td> Laridae (gulls) </td> <td> laridae_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage time spent feeding within 2m of water surface </td> <td> around_surf_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage time spent feeding >2m below water surface </td> <td> below_surf_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet plants </td> <td> plant_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet scavenging </td> <td> scav_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet endothermic vertebrates </td> <td> vend_rast_eco_bds.tif </td> </tr> <tr> <td> Congregative </td> <td> cong_rast_eco_bds.tif </td> </tr> <tr> <td> Migratory </td> <td> migr_rast_eco_bds.tif </td> </tr> <tr> <td> Below threshold phylogenetic distance to known host species </td> <td> host_dist_rast_eco_bds.tif </td> </tr> <tr> <td> Species richness </td> <td> species_richness_rast_eco_bds.tif </td> </tr> </tbody> </table>
Wild for Orchids Citizen Science Campaign Records 2020 - 2023
<p>This dataset represents the geographic distribution of wild orchids in the Maltese Islands, as recorded by the Wild For Orchids Citizen Science Initiative between January 2020 and December 2023. It includes data obtained through citizen science contributions and has undergone rigorous two-stage quality control for species identification and GPS accuracy. Species identification was carried out according to Mifsud (2018). The location data of each records is provided as a shapefile format projected in ETRS89-extended / LAEA Europe (EPSG:3035), and exact GPS location were transformed in 1 km square grid according to the <span>European Forum for Geography and Statistics (EFGS).<br></span></p> <p><span>Wild for Orchids is a Citizen Science Initiative designed and managed by Green House Malta.</span></p>
Grapegenomics.com: a web portal with genomic data and analysis tools for wild and cultivated grapevines
<p><a href="https://grapegenomics.com">Grapegenomics.com</a> is a web portal that provides public access to genome references for grapevine cultivars (<em>Vitis vinifera</em> ssp. <em>vinifera</em>), wild grapevines (<em>Vitis vinifera</em> ssp. <em>sylvestris</em>), various wild grape species (<em>Vitis</em> spp. and <em>Muscadinia</em> spp.), and major fungal pathogens affecting grapes.</p> <p>All genomes are accessible through dedicated genome browsers, and published genomes are available for complete <a href="https://www.grapegenomics.com/download.php">download</a>.</p> <p>The site hosts all genomes produced by the laboratory of Dario Cantù in the Department of Viticulture and Enology at the University of California, Davis, along with published genome references generated by others, such as PN40024 and Pinot noir ENTAV115. Instructions for genome submission are provided <a href="https://www.grapegenomics.com/submit.php">here</a>. The portal is maintained by Noé Cochetel (ndcochetel[at]ucdavis.edu). In this version 2.0, all genome browsers utilize <a href="https://jbrowse.org/jb2/">jbrowse 2</a>. <br><br>Link to the website: <a href="https://www.grapegenomics.com">https://www.grapegenomics.com</a> </p>
FASTA file containing to the MYB encoding gene Ant1 genomic sequences corresponding to wild and cultivated tomato accessions
<p>Fasta sequence correspond to the MYB encoding gene <em>An2-like</em>. The genomic sequences correspond to <em>Solanum galagpagnese</em> accession LA1141 (this study), <em>S. lycopersicum</em> variety OH8245 (this study), <em>S. lycopersicum</em> variety Heinz 1706 reference genome, and 84 tomato accessions published as part of The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Local sequences databases were made and retrieved using BLAST version/2018-08 for 84 accessions from The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Sequences corresponding to Heinz 1706 (Hosmani et al., 2018), were accessed using the Basic Local Alignment Search Tool (BLAST) tool available from the Sol Genomics Network (SGN) (available at <a href="https://solgenomics.net/tools/blast/">https://solgenomics.net/tools/blast/</a>).</p>
FASTA file containing the MYB encoding gene An2-like genomic sequences corresponding to wild and cultivated tomato accessions
<p>FASTA sequence corresponds to the MYB encoding gene <em>An2-like</em>. The genomic sequences correspond to <em>Solanum galagpagnese</em> accession LA1141 (this study), <em>S. lycopersicum</em> variety OH8245 (this study), <em>S. lycopersicum</em> variety Heinz 1706 reference genome (Hosmani et al., 2019), <em>S. lycopersicum </em>variety Indigo Rose (Yan et al., 2020), <em>S. lycopersicum</em> accession LA1996 [MN242011.1 (Colanero et al., 2020)], <em>S. chilense </em>accession LA1930 [MN242012.1 (Colanero et al., 2020)], and 84 tomato accessions published as part of The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Local sequences databases were made and retrieved using BLAST version/2018-08 for 84 accessions from The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Sequences corresponding to Heinz 1706 (Hosmani et al., 2018), Indigo Rose [MN433087 (Yan et al., 2020)], <em>S. lycopersicum </em>accession LA1996 [MN242011.1, EF433417.1 (Sapir et al., 2008; Colanero et al., 2020)], <em>S. chilense</em> accession LA1930 [MN242012.1 (Colanero et al., 2020)] were accessed using the Basic Local Alignment Search Tool (BLAST) tool available from the Sol Genomics Network (SGN) (available at <a href="https://solgenomics.net/tools/blast/">https://solgenomics.net/tools/blast/</a>) and the National Center for Biotechnology Information (NCBI)(available at NCBI: <a href="https://www.ncbi.nlm.nih.gov">https://www.ncbi.nlm.nih.gov</a>).</p>
FASTA file containing the MYB encoding genes at the Aft locus with genomic sequences corresponding to wild and cultivated tomato accessions
<p>FASTA sequences correspond to the MYB encoding genes <em>An2-like </em>and <em>Ant1</em>. The genomic sequences were combined correspond to <em>Solanum galagpagnese</em> accession LA1141 (this study), <em>S. lycopersicum</em> variety OH8245 (this study), <em>S. lycopersicum</em> variety Heinz 1706 reference genome (Hosmani et al., 2019), LA1996 [MN242011.1, EF433417.1(Sapir et al., 2008; Colanero et al., 2020)], and 84 tomato accessions published as part of The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Local sequences databases were made and retrieved using BLAST version/2018-08 for 84 accessions from The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Sequences corresponding to Heinz 1706 (Hosmani et al., 2018), <em>S. lycopersicum </em>accession LA1996 [MN242011.1, EF433417.1 (Sapir et al., 2008; Colanero et al., 2020)], <em>S. chilense</em> accession LA1930 [MN242012.1 (Colanero et al., 2020)] were accessed using the Basic Local Alignment Search Tool (BLAST) tool available from the Sol Genomics Network (SGN) (available at <a href="https://solgenomics.net/tools/blast/">https://solgenomics.net/tools/blast/</a>) and the National Center for Biotechnology Information (NCBI) (available at NCBI: <a href="https://www.ncbi.nlm.nih.gov/">https://www.ncbi.nlm.nih.gov</a>).</p>
Manipulating a host-native microbial strain compensates for low microbial diversity by increasing weight gain in a wild bird population
<h1>Manipulating a host-native microbial strain compensates for low microbial diversity by increasing weight gain in a wild bird population</h1> <h1> </h1> <p>These files contain data on bacteria present in the guts of wild great tit (Parus major) obtained from faecal samples and sequenced using Illumina MiSeq. These data resulted from an experiment which provided supplementary mealworms at the nest during the breeding season at number of woodland sites in Cork, Ireland. Approximately half of these nests were given mealworms covered in a freeze dried bacterial powder containing the bacteria Lactobacillus kimchicus, which had been isolated from great tit faeces from the previous season. This treatment aimed to disrupt the gut microbiota of the treatment birds in order to provide evidence for the gut microbiotas role in birds health and fitness. Included here are the 3 elements necessary to create a 'phyloseq object' containing the sample metadata, ASV (Amplicon Sequence Variant) count table and a taxonomy table. The metadata file includes the alpha diversity scores for each individual. The data include all negative control samples taken during sample collection and library preparation, which were removed before the main analyses. All analyses, except for the beta-diversity analyses, were conducted in R. All R code is available on GitHub (https://github.com/shan-e-s\). Raw Sequence data are available in the European Nucleotide Archive under access number PRJEB74941, and ERS18960426-ERS18960697.</p> <h2> </h2> <h2>## Description of the data and file structure </h2> <p>Taxonomy, ASV and metadata files required to create a phyloseq object in R. metadata.csv file contains data on individual birds (i.e. individual samples). The metadata includes descriptions of the bird itself and it's environment, namely:</p> <ul> <li>Rownames: unique sample ID for each sample, corresponds with asvTable.csv. </li> <li>Nest: unique identifier for the nest box associated with the bird being sampled. </li> <li>Sample.ID: unique identifier for the faecal sample or control sample.</li> <li>Bird.ID: Identity of the bird the sample came from, note some individuals sampled twice so some bird.ID's may reoccur in metadata with different Sample.ID.</li> <li>Date: Date the sample was taken dd/mm/yyyy.</li> <li>Day: Date the sample was taken, in days since 1st March.</li> <li>Ring.Mark: British Trust for Ornithology (BTO) metal ring ID where applicable. Birds only ringed at D15 so some young birds do not have IDRings.</li> <li>Site: ID of woodland site that bird was sampled at.</li> <li>Chick.LetterID: ID letter differentiates between different birds from the same nest. Either 'A'-'F' for nestlings, 'Fe' for females or 'M' for males.</li> <li>Age.code: BTO age code.</li> <li>Age.category: Age category that bird is in. D8 = 8 days post hatching, D15 = 15 days post hatching, adult = 1+ years post hatching.</li> <li>Sex: Bird's sex, only determined for adult birds. Fe = Female, M = Male.</li> <li>Wing_mm: Wing length in mm.</li> <li>Tarsus_mm: minimum tarsus length of bird in mm.</li> <li>Weight_g: bird's weight in grams.</li> <li>Faecal.Sample: bird's age at sampling.</li> <li>newRing: whether bird was fitted with a new BTO ring. Only relevant to adults.</li> <li>Treatment: the experimental treatment group that the bird was in. Either 'Treatment' when nest given L. kimchicus treated mealworms or 'Control' when nest given plain mealworms.</li> <li>Notes: field notes.</li> <li>Main.sample: indicates whether this sample was the main sample to be used for analysis, an alternative sample taken as a backup.</li> <li>Plate: the ID of the PCR plate which the sample was amplified on.</li> <li>Azenta_noPeriod: sample ID given to sequencing facility without special characters. Corresponds to fastq files and ASV table counts.</li> <li>Qubit_prePool: samples qubit score before pooling.</li> <li>Date_extracted: date the sample was extracted on dd/mm/yyyy.</li> <li>SampleType: whehther the sample was a 'main' sample intended for downstream analysis, a 'control' sample for detecting contamination during library preparation, a 'duplicate' for detecting PCR issues, a 'label_error' where sample was suspected of being mislabelled at some point, a 'repeat' sample intended to detect errors or issues, a 'contam' sample which was suspected of being contaminated, a 'common' sample used across different PCR plates to detect issues. Extraction_notes: notes regarding the DNA extraction of the sample. </li> <li>LibPrep_notes: notes regarding the library preparation of the sample.</li> <li>Ring.Mark.lab: the ring or sample ID written on the sample tube, recorded to help detect mislabelling.</li> <li>Post_lab_notes: notes regarding issues found post sequencing.</li> <li>NumberOfReads: number of sequence reads associated with the sample. </li> <li>DistanceToEdge: distance between nest and woodland edge in metres. </li> <li>BroodSize.D8: number of nestlings in the nest at day-8 post hatching. </li> <li>BroodSize.D15: number of nestlings in the nest at day-15 post hatching.</li> <li>firstEggLayDate: Date the first egg in the clutch was laid, in days since 1st March.</li> <li>lastEggLayDate: Date the last egg in the clutch was laid, in days since 1st March.</li> <li>Observed: number of unique ASV's (or taxa) detected in the sample.</li> <li>Chao1: Chao1 diversity of the sample.</li> <li>Shannon: Shannon diversity of the sample.</li> </ul> <p>The file 'taxonomy.csv' contains the taxonomic breakdown of each bacterial Amplicon Sequence Variant (ASV) found in the dataset from Phylum to Species. Obtained by using the Naive Bayes Classifier against the Silva (v138) taxonomic database.</p> <p>The file 'asvTable.csv' contains counts of each amplicon sequence variant's occurrence for each individual sample. Samples are rows and taxa are columns.</p> <p> </p> <h2>Sharing/Access information </h2> <p>All R code is available on GitHub (https://github.com/shan-e-s\). Raw Sequence data are available in the European Nucleotide Archive under access number PRJEB74941, and ERS18960426-ERS18960697.</p>
Supplemented material to "Mycobacteriosis in various pet and wild birds from Germany: pathological findings, coinfections, and characterisation of causative Mycobacteria."
<p>This is the supplemented material to the publication "Mycobacteriosis in Various Pet and Wild Birds from Germany: Pathological Findings, Coinfections, and Characterization of Causative Mycobacteria". <br>The causative agents and confounding factors of mycobacteriosis in a set of pet (n=45) and some wild birds (n=5) from Germany were examined in this study. Not only Mycobacterium genavense (Mg), but also M. avium subsp. avium (Maa) and M. avium subsp. hominissuis (Mah), contributed to mycobacteriosis in these birds. The isolates were characterized by a combination of different typing methods. The genetic diversity of isolates belonging to Mg, Maa and Mah differed. Various coinfections by viruses, endoparasites, fungi and other bacterial species did not affect the manifestation of mycobacteriosis. Cross pathological fidings were more often seen in mycobacteriosis caused by Ma compared to Mg suggesting a different pathogenicity of the two species. New genotypes of Mah were identified in these birds that is important for epidemiological studies and for understanding the zoonotic role of this pathogen, as the subsp. hominissuis represents an increasing public health concern. The study provides some evidence of correlation between individual Maa genotypes and virulence which will have to be confirmed by broader studies.</p>
IMU data captured unobtrusively and in-the-wild by Parkinson's disease patients and healthy controls
<p><strong>DATASET</strong></p> <p>The dataset contains IMU signals captured in-the-wild via the accelerometer sensor embedded in modern smartphones, for the purpose of detecting tremorous episodes, related to Parkinson's Disease (PD). A group of 31 PD patients and 14 Healthy controls contributed accelerometer data using their personal smartphones, for a period spanning many months.Tri-axial acceleration values were recorded automatically whenevera phone call was realized. The recording lasted for 75 seconds at the most. Each phone call thus resulted in one recorded accelerometer signal, also referred to as session. Each subject contributed a different amount of sessions depending on the number of phone calls they realized during the data collection period as well as their participation time (they were free to drop-out at any time). A detailed description of the capturing process as well as analysis results, can be found in the related research article.</p> <p>The data is presented as a list of python dictionaries, stored in a pickle file. Each dictionary in the list, corresponds to one subject and containes the following fields:</p> <p>1. subject_id: scalar<br> A numerical value that uniquely identifies the subject.</p> <p>2. subject_sessions: list of numpy.array<br> A list of numpy arrays of shape (N, 4) that contains the tri-axial accelerometer sessions that the subject contributed. N denotes the total length of the session in samples (which varies from session to session) Column 0 of the array contains the timestamps of the accelerometer samples. Columns 1-3 contain the acceleration values across the x,y,z directions.</p> <p>3. session_datetimes: list of datetime objects <br> A list of datetime objects that denote the capturing date and time of the corresponding entries in the subject_sessions field.</p> <p>4. annotation: dict<br> A dictionary containing the following tremor-related annotation values:<br> * updrs16: scalar int<br> The value related to tremor as described in item 16 of the part II of the MDS-UPDRS scale, as reported by the subject.</p> <p>* updrs20_right: scalar int in range [0, 4]<br> The value related to rest tremor in the right hand as described in item 20 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist.</p> <p>* updrs20_left: scalar int in range [0, 4]<br> Same as above but for left hand.</p> <p>* updrs21_right: scalar int in range [0, 4]<br> The value related to action/postural tremor in the right hand as described in item 21 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist.</p> <p>* updrs21_left: scalar int in range [0, 4]<br> Same as above but for left hand.</p> <p>* sp_expert: scalar int in range [0, 1]<br> A binary tremor annotation created by a group of signal processing experts, upon visually examining the contributed signals in both time and frequency domain and taking into consideration the UDPRS scores of each subject. This was necessary due to the intermittent nature of tremor, as well as a number of considerations related to the in-the-wild nature of the data capturing process. For more details, we refer the reader to the dataset description in the related research article.<br> A '1' value indicates that the subject has tremor.<br> A '0' value indicates that the subject doesn't have tremor.</p> <p>* pd_status: scalar int in range [0, 1]<br> A '1' value indicates that the subject is a PD patient.<br> A '0' value indicates that the subject is a Healthy Control</p> <p>Note: Each annotation value refers to the subject as a whole, and not in any one session.<br> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected is a multi-center study approved in each country available (for more info visit: <a href="http://www.i-prognosis.eu/?page_id=3606">http://www.i-prognosis.eu/?page_id=3606</a>). Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu/">i-prognosis.eu</a>).</p> <p> </p> <p><strong>CORRESPONDANCE</strong></p> <p>Any inquiries regarding this dataset should be adressed to:</p> <p>Mr. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate)</p> <p>Multimedia Understanding Groupmug<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996359, 996365 <br> Fax: +30 2310 996398<br> E-mail: alpapado@mug.ee.auth.gr</p> <p> </p> <p><strong>LICENSE</strong></p> <p>This is an open access dataset, licensed under Creative Commons Attribution 4.0 International (<a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>).</p> <p> </p> <p><strong>WARRANTY</strong></p> <p>This dataset comes without any warranty. Administrators of this dataset can not be held accountable for any damage (physical, financial or otherwise) caused by the use of this dataset. </p>
OpenForensics: Multi-Face Forgery Detection And Segmentation In-The-Wild Dataset [V.1.0.0]
<p>OpenForensics is the first large-scale dataset posing a high level of challenges. This dataset is designed with face-wise rich annotations explicitly for face forgery detection and segmentation. With its rich annotations, OpenForensics dataset has great potentials for research in both deepfake prevention and general human face detection. Project Page: https://sites.google.com/view/ltnghia/research/openforensics</p>
Kawe Gidaa-naanaagadawendaamin Manoomin Tribal-University Research Collaborative, University of Minnesota, Manoomin / Psiη (Wild Rice) Density Survey for Northern Minnesota and Wisconsin Waters
Wild Rice (Ojibwemowin: Manoomin; Dakodiapi: Psiŋ; Latin: Zizania palustris) abundance, harvest, and water level data, across the upper Great Lakes region collected by tribal organizations.
From A to Z: Projective coordinates leakage in the wild: research data and tooling
<p>Description</p> <p>This dataset and software tool are for reproducing the research results related to CVE-2020-10932 and CVE-2020-11735, resulting from the article "From A to Z: Projective coordinates leakage in the wild" (to appear at CHES 2020). The data was used to carry out the attack in Section 6 of the article.</p> <p>Data format</p> <p>txt files</p> <p>The <code>[int].txt</code> files contain an encoded page-fault trace prefixed by <code>trace:</code>.</p> <p>A trace represents the sequence of tracked memory pages that were executed during the generation of an ECDSA signature. The trace is encoded using ASCII characters for better visualization.</p> <p>The encoding follows this table:</p> <pre><code class="language-markdown">| Functions | Symbol | Page offset | | ---------------------- |:------:|:-------:| | _gcry_ecc_ecdsa_sign | T | 0xa1000 | | _gcry_mpi_invm | . | 0xcf000 | | _gcry_mpi_set | S | 0xd5000 | | _gcry_mpi_add | A | 0xcd000 | | _gcry_mpih_sub_n | - | 0xd8000 | | _gcry_mpih_rshift | - | 0xd8000 |</code></pre> <p><code>_gcry_ecc_ecdsa_sign</code> is the highest level function tracked in the attack. This allows to differentiate different calls to the <code>_gcry_mpi_invm</code> function which contains an insecure version of a Binary Extended Euclidean Algorithm (BEEA).</p> <p>Using these pages it is possible to locate the execution of <code>_gcry_mpi_invm</code> corresponding to the computation of <code>Z mod p</code> during projective to affine coordinates conversion (see <code>preprocess_trace</code> function).</p> <p>It can be seen, that <code>_gcry_mpih_sub_n</code> and <code>_gcry_mpih_rshift</code> shares a page. However, they can be differentiated using mainly the caller memory page. This sharing, instead of being a drawback, allows a straightforward recovery of BEEA execution flow (see <code>extract_Zi</code> and <code>extract_Xi</code> functions in <code>recover_z.py</code>).</p> <p>dat files</p> <p>The format of the <code>[int].dat</code> files is as follows.</p> <ul> <li><code># X [hex]</code>: Ground truth projective output of scalar multiplication, before affine conversion</li> <li><code># Y [hex]</code>: Ground truth projective output of scalar multiplication, before affine conversion</li> <li><code># Z [hex]</code>: Ground truth projective output of scalar multiplication, before affine conversion</li> <li><code># curve_name [str]</code>: The curve (P256)</li> <li><code># h [hex]</code>: Hash of the message to be signed</li> <li><code># k [hex]</code>: Ground truth ECDSA nonce</li> <li><code># q [hex]</code>: Curve order</li> <li><code># r [hex]</code>: First component of the ECDSA signature</li> <li><code># s [hex]</code>: Second component of the ECDSA signature</li> <li><code># x [hex]</code>: Ground truth ECDSA private key</li> <li><code># y [hex] [hex]</code>: Public key coordinates</li> <li><code># leak_pad [int],[int],[int]</code>: Leakage recovered during backtracking. Example: <code>0,4,15 => 0 = k % 2**4 = k & 15</code></li> </ul> <p>Tooling</p> <p>The <code>recover_z.py</code> script</p> <ul> <li>Loads a trace.</li> <li>Recovers the corresponding Z coordinate from the trace data.</li> <li>verifies the recovered Z matches the ground truth Z.</li> </ul> <p>Example</p> <p>Unpack the data:</p> <pre><code>tar xf traces.tar.gz</code></pre> <p>Run the tooling on trace index 123:</p> <pre><code>$ python2 recover_z.py 123 INFO:recovered Z:65b9b7006bc7b030218bef1b6e569f9f7acaee059b53d669388c6b860f67e213 INFO: real Z:65b9b7006bc7b030218bef1b6e569f9f7acaee059b53d669388c6b860f67e213</code></pre> <p>The output demonstrates the recovered Z coordinate is correct, i.e. matches the ground truth.</p> <p>Credits</p> <p>Authors</p> <ul> <li>Alejandro Cabrera Aldaya (Tampere University, Tampere, Finland)</li> <li>Cesar Pereida García (Tampere University, Tampere, Finland)</li> <li>Billy Bob Brumley (Tampere University, Tampere, Finland)</li> </ul> <p>Funding</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 804476).</p> <p>License</p> <p>This project is distributed under MIT license.</p> <p> </p>
WCSdb: A database of Wild Coffea Species.
<p>This the data table of the WCSdb: A database of Wild <em>Coffea</em> Species web site. The photo Gallery associated to this web site are available at <a href="https://dataverse.ird.fr/dataset.xhtml?persistentId=doi:10.23708/JZA8I2">https://doi.org/10.23708/JZA8I2</a> and the sequencing data at <a href="https://doi.org/10.23708/KWRIJJ%22%20%5Ct%20%22_blank">https://doi.org/10.23708/KWRIJJ</a></p> <p>Coffee is a beverage enjoyed by millions of people worldwide and an important commodity for millions of people. Beside the two cultivated species (<em>Coffea arabica</em> and <em>Coffea canephora</em>), the 139 wild coffee species belong to the <em>Coffea</em> genus are largely unknown to coffee scientists and breeders although these species may be crucial for future coffee crop development to face climate changes. Here we present the Wild Coffee Species database (WCSdb) hosted by Pl@ntNet platform (<a href="http://publish.plantnet-project.org/project/wildcofdb_en">http://publish.plantnet-project.org/project/wildcofdb_en</a>), providing information for 140 coffee species, for which 84 contain a photo gallery and 82 contain sequencing data (GBS, chloroplast or whole genome sequences). The objective of this database is to better understand and characterize the species (identification, morphology, biochemical compounds, genetic diversity, sequence data) in order to better protect and promote them. </p>
What do studies in wild mammals tell us about human emerging viral diseases in Mexico? database
<p>The database used in the article "<strong>What do studies in wild mammals tell us about human emerging viral diseases in Mexico?</strong>". It contains all available records of viral zoonotic and potential zoonotic species in Mexican wild mammals.</p> <p>The first file is a .csv file and the second one is .xls</p>
Seed mass data for crop species and wild progenitors
<p>Data supporting a comparison of seed masses in crop species and their wild progenitors.</p> <p>The data files which fed into the analysis reported in the paper are:</p> <ul> <li>Grass_crops_raw.csv</li> <li>Legume_crops_raw.csv</li> <li>Veg crops combined.csv</li> <li>Beet_seeds_dissected.csv - mass of true seeds of beet; the data in the main 'Veg crops' file are for beet seed capsules, which are easier to collect and weigh.</li> <li>Cassava_EMBRAPA_*.csv - extra seed mass data from cassava, analysed separately from the main data, and described in supplementary material 2.</li> </ul> <p>Scripts used in processing the data (*.py) and a Makefile controlling some processing steps are included.</p> <p>There is also some extra data collected on other species of vegetable crops: <em>Brassica </em>spp., Chicory & Endive (<em>Cichorium </em>spp.), Leek (<em>Allium ampeloprasum</em>), and on fibre crops: Kenaf & Roselle (<em>Hibiscus </em>spp.), Jute (<em>Corchorus olitorius</em>), Hemp (<em>Linum</em> spp.). The data available for these was not sufficient to include them in our analysis, but they are provided here anyway.</p>
Supplementary materials for: How effective are insect aposematism and Batesian mimicry in deterring a wild avian predator?
<p><strong><a name="_Hlk180513890"></a>Acoustic_parameters_10cm.txt</strong><br>Acoustic parameters of the sounds produced by insects in flight, species recorded at 10 cm from the microphone.</p> <p><strong>Acoustic_parameters_5cm.txt<br></strong>Acoustic parameters of the sounds produced by insects in flight, species recorded at 5 cm from the microphone.</p> <p><strong>Robin_behaviours.txt</strong> <br><em>Erithacus rubecula</em> reactions measured in Boris software from videos of behavioural experiments.</p> <p><strong>Rscript_robin_behaviours.R</strong><br>R script used for the analysis of robins’ behaviours (using Robin_behaviours.txt).</p> <p><strong>Rscript_acoustical_analyses.R</strong><br>R script used for the analysis of acoustical parameters of insect buzzing sounds (using Acoustic_parameters_10cm.txt and Acoustic_parameters_5cm.txt).</p> <p><strong>Robin_experiment_video.mp4<br></strong>Behavioural experiment with a wild European robin (<em>Erithacus rubecula</em>) in its habitat. A freshly defrosted insect specimen and the corresponding buzzing sound of each recorded hymenopteran model and lepidopteran mimic (plus a housefly as a control) were presented in random order to the robin at a feeder with <em>Tenebrio molitor</em> larvae.</p> <p><strong>Robin_experiment_video_Metadata.docx</strong><br><span>Metadata for the supplementary video Robin_experiment_video.mp4.</span></p>
Growth of wild cucumber (Echinocystis lobata) in three variants of support (5, 20, 50 cm) and shoot geometry
<p><span>The experiments included three variants of support: 5 cm, 20 cm, and 50 cm. </span><span>The data was collected in the years 2020-2023. After developing their first pair of mature leaves, when the plants were about 20-35 cm in length, the plants were photographed. The shutter was released every 15 minutes. The resulting images were combined into a 10 fps video. The following were used for the analysis: 13 recordings of the 5 cm variant and 12 recordings of the 20 and 50 cm variants.</span></p> <p><span>Next, an analysis was performed based on the resulting video using Tracker</span><span> for kinetic analysis of video objects. The recordings were used to measure the plant growth parameters. The tape measure and point mass tools were used to determine the length of the shoots and to change the position of the apex relative to the areas of the X- and Y-axis photographed over time. To compensate for this, a trend line was drawn (polynomial of the 2nd degree, due to the very good fit, R<sup>2</sup> = 0,975–0,995) and new plant length parameters were calculated. </span></p> <p><span>To enhance the research, cross-sections of the shoots of ten plants were scanned. Samples were taken every 5 cm from the base of the plant. Using ImageJ</span><span> the following parameters were measured: shoot cross-sectional area, tissue area, perimeter, and circularity. </span></p>
Supplementary datasets for manuscript titled: Seasonal tissue-specific gene expression in wild crown-of-thorns starfish reveals reproductive and stress-related transcriptional systems
<p>Supplementary datasets for manuscript titled: Seasonal tissue-specific gene expression in wild crown-of-thorns starfish reveals reproductive and stress-related transcriptional systems</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research 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.
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.
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.
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.
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.