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Data for: A new threshold selection method for species distribution models with presence-only data: extracting the mutation point of the P/E curve by threshold regression
<p>Selecting thresholds to convert continuous predictions of species distribution models proves critical for many real-world applications and model assessments. Prevalent threshold selection methods for presence-only data require unproven pseudo-absence data or subjective researchers' decisions. This study proposes a new method, Boyce-Threshold Quantile Regression (BTQR), to determine thresholds objectively without pseudo-absence data. We summarize that the mutation point is a typical shape feature of the predicted-to-expected (P/E) curve after reviewing relevant articles. Analysis based on source-sink theory suggests that this mutation point may represent a transition in habitat types and serve as an appropriate threshold. Threshold regression is introduced to accurately locate the mutation point.</p> <p>To validate the effectiveness of BTQR, we used four virtual species of varying prevalence and a real species with reliable distribution data. Six different species distribution models were employed to generate continuous suitability predictions. BTQR and nine other traditional methods transformed these continuous outputs into binary results. Comparative experiments show that BTQR has advantages in terms of accuracy, applicability, and consistency over the existing methods.</p>
tRIBS benchmark: SNOTEL point-scale simulation, Happy Jack, AZ, USA.
<p>This repository hosts the setup for executing a point-scale simulation (i.e. single computational element) of the <a href="https://wcc.sc.egov.usda.gov/nwcc/site?sitenum=969">Happy Jack SNOTEL Site</a> in northern Arizona, USA, using the TIN-Based Real-Time Integrated Basin Simulator (<a href="https://tribshms.readthedocs.io/en/latest/">tRIBS</a>). </p> <p>This repository consists of : </p> <p>(1) A data subdirectory, which contains all the necessary data to run tRIBS at Happy Jack and includes bias corrected dataset of SWE for model comparison. </p> <p>2) A doc subdirectory, which contains a jupyter notebook for analyzing and visualizing this specific benchmark case, alongside additional documentation. </p> <p>(3) A src subdirectory that is currently empty but intended to contain source code for the tRIBS executable, which can be obtained <a href="https://github.com/tribshms/tRIBS">here</a>. </p> <p>(4) A bin subdirectory, that is currently empty but intended to store the tRIBS executable, with instructions to build the model <a href="https://tribshms.readthedocs.io/en/latest/man/Model_Execution.html#compilation-instructions">here</a>. </p> <p>(5) A results subdirectory, which contains reference model output for results comparison.</p> <p>Instructions for running this benchmark are available in the README.html file within the repository, which can be opened with your web browser, and can also be found in the latest tRIBS <a href="https://tribshms.readthedocs.io/en/latest/man/Model_Execution.html#run-instructions">documentation</a>. </p> <p>Data from this model was originally collated by Gretchen Hawkins and Josh Cederstrom for their Master’s Thesis work at Arizona State University and associated publications:</p> <p>Hawkins, G.A., Vivoni, E.R., Robles-Morua, A., Mascaro, G., Rivera, E., and Dominguez, F. 2015. A Climate Change Projection for Summer Hydrologic Conditions in a Semiarid Watershed of Central Arizona. Journal of Arid Environments. 118: 9–20. <a href="https://doi.org/10.1016/j.jaridenv.2015.02.022">https://doi.org/10.1016/j.jaridenv.2015.02.022</a> </p> <p>Cederstrom, C.J., Vivoni, E.R., Mascaro, G., and Svoma, B. 2024. Forest Thinning Effects on Watershed Responses under Warming. Water Resources Research. (In Review). </p> <p>Bias corrected SWE data is from:</p> <p>Sun N, Yan H, Wigmosta MS, Leung LR, Skaggs R, Hou Z. 2019. Regional Snow Parameters Estimation for Large-domain Hydrological Applications in the Western United States. Journal of Geophysical Research: Atmospheres. 124 : 5296–5313.</p> <p>Data for this model simulation were obtained from a number of public sources: </p> <p>Meteorological forcing was obtained from the Happy Jack SNOTEL Site (https://www.nrcs.usda.gov/wps/portal/wcc/) and NLDAS-2 (https://ldas.gsfc.nasa.gov/nldas/v2/forcing). Soil properties were obtained from the Soil Survey Geographic Database (https://nrcs.app.box.com/v/soils) with derived parameters informed from the literature and calibration process. Vegetation parameters were obtained from the LANDFIRE REMAP data set (https://landfire.gov).</p> <p> </p> <p><strong>NOTE:</strong> If you encounter a situation where, after downloading and extracting the <code>happy_jack.gz</code> file, the extracted file has no extension, please follow these steps:</p> <ol> <li> <p><strong>Rename the File</strong>: Manually rename the extracted file to <code>happy_jack.zip</code>.</p> </li> <li> <p><strong>Extract Again</strong>: After renaming, unzip the <code>happy_jack.zip</code> file to access the full folder with all the necessary files.</p> </li> </ol> <p>This issue may occur if the file extraction process doesn't automatically add the <code>.zip</code> extension. By renaming the file and unzipping it again, you should be able to access the complete set of files without any issues.</p>
Data from: Infant microbes and metabolites point to childhood neurodevelopmental disorders
<p>This study has followed a birth cohort for over twenty years to find factors associated with neurodevelopmental disorder (ND) diagnosis. Detailed, early-life longitudinal questionnaires captured infection and antibiotic events, stress, prenatal factors, family history, and more. Biomarkers including cord serum metabolome and lipidome, HLA genotype, infant microbiota, and stool metabolome were assessed. Among the 16,440 Swedish children followed across time, 1,197 developed an ND. Significant associations emerged for future ND diagnosis in general and for specific ND subtypes, spanning intellectual disability, speech disorder, attention deficit hyperactivity disorder, and autism. This investigation revealed microbiome connections to future diagnosis, as well as early-emerging mood and gastrointestinal problems. The findings suggest links to immunodysregulation and metabolism, compounded by stress, early-life infection, and antibiotics. The convergence of infant biomarkers and risk factors in this prospective, longitudinal study on a large-scale population establishes a foundation for early-life prediction and intervention in neurodevelopment.</p>
Development and Implementation of a Tool for Data Capture @ Point of Sampling
<p><span>Poster presented at EFSA Symposium “Data Readiness for Artificial Intelligence”, held on 23-24 October 2024. </span></p> <p><span> </span><span>It describes the project “Development and Implementation of a Tool for Data Capture @ Point of Sampling” coordinated by the Portuguese Economic and Food Safety Authority (ASAE) with technical support from LIACC (Artificial Intelligence and Computer Science Laboratory, University of Porto), and involving two Danish organisations (Danish Veterinary and Food Administration and Technical University of Denmark) as end users of an application designed to enhance the harmonization and robustness of data collection processes. </span></p> <p><span> </span><span>The project is focused on two key areas: the further development of the mobile tool for data capture at the point of sampling in EFSA's SSD2 format, including SSD2 catalogue integration and translation into national languages, and the installation of this tool within partner entities across participating countries. </span></p> <p><span>The new system will incorporate validation features through the integration of the SSD2 data format and the FoodEx2 classification at the point of data creation.</span></p>
FIG. 2 in L'outillage façonné utilisé en percussion lancée du site paléolithique moyen du Bois de l'Hôpital (Saint-Sulpice-la-Pointe, Tarn)
FIG. 2. — Schéma des critères de description des écailles utilisées.Infographie: C. Viallet.
An enhanced single Gaussian point continuum finite element formulation using automatic differentiation: Source code and data
<p>This dataset contains the source code and the data with an example of uniaxial strain of an enhanced single Gaussian point continuum finite elemnet formulation using automatic differentiation.</p> <p> </p> <p>This contribution presents a low-order 3D finite element formulation with hourglass stabilization using automatic differentiation. Here, the former Q1STc element formulation is enhanced by an approximation-free computation of the inverse of the Jacobian. The improved version is termed "Q1STc+."</p> <p> </p> <p>The corresponding publication is:</p> <p><br>Pacolli, N., Awad, A., Kehls, J., Sauren, B., Klinkel, S., Reese, S., Holthusen, H.<br><em>An enhanced single Gaussian point continuum finite elemnet formulation using automatic differentiation.</em></p> <p>Standalone_Elementroutine: <em>Q1STc+_Codes</em> contains:</p> <ul> <li><strong>main.f90</strong>: Standalone routine for local uniaxial strain test</li> <li><strong>Makefile</strong>: Makefile to create executable "Q1STc+"</li> <li><strong>elem40.f90</strong>: Element routine "Q1STc+" with elem_sub.f90 as the subroutine written in AceGen</li> <li><strong>mat52.f90</strong>: Elasto-plastic material routine with all subroutines written in AceGen</li> <li><strong>elem_mat_select.f90</strong>: The selected material routine (Here: mat52)</li> <li><strong>elem_subs.f90</strong>: Subroutines for elem40.f90</li> <li><strong>mat_subs.f90</strong>: Subroutines for mat52.f90</li> </ul>
Zeiss Lattice Light Sheet 7 Point Spread Functions and example dataset
<p>This repository contains some point spread functions (PSFs) distilled from imaging fluorescent beads, 200nm, <a href="https://www.thermofisher.com/order/catalog/product/F8811?SID=srch-srp-F8811" target="_blank" rel="noopener noreferrer">F8811</a> in an agarose gel, on a Zeiss Lattice Light sheet 7 microscope. The bead images from which the PSF were distilled are contained in this repo.</p> <p>Several images were acquired with varying settings of the light sheet (light sheet was always set as the 30 um x1000 nm).</p> <p>These three parameters were varied:</p> <p>FS: focus sheet (-100, -75, -25, 0)</p> <p>FW: focus waist (40, 45, 50, 55, 60, 65, 70, 75, 80, 85)</p> <p>AC: aberration control (150, 155, 160, 165, 170, 175, 180, 185, 190)</p> <p>The file psf-200nm correspond to the best settings taken between the different settings. The two other files (psf-300nm, psf-400nm) correspond to resampled versions of the psf-200nm file.</p> <p>The all-psfs.tif contains the deskewed psf ordered similarly as the poster uploaded in this version of the repository (ELMI-2024-poster.pdf).</p> <p>A sample czi file (Hela Kyoto, raw data) is provided to test deconvolution algorithms.</p> <p>Note that none of these data is deskewed.</p> <p>--------------------</p> <p>Here's a protocol to prepare a gel containing beads at a good enough density:</p> <ul> <li> <p>Material:</p> <ul> <li>Agarose 2%</li> <li>Fluorescent beads, 200nm, <a href="https://www.thermofisher.com/order/catalog/product/F8811?SID=srch-srp-F8811">F8811</a>. Stock is 1000x</li> </ul> </li> <li> <p>Protocol:</p> <ul> <li>Put the agarose to warm up at 90C.</li> <li>Vortex the beads stock.</li> <li>Dilute a 100 times the bead dilution in PBS (10 uL in 1mL), vortex.</li> <li>Prepare a clean slide, and a clean coverglass (22x22mm).</li> <li>Take 90uL of agarose, put in a 1.5mL tube, add 10uL of the diluted beads, vortex.</li> <li>Take 50uL of this agarose solution, put it on the slide, add the coverglass on top.</li> <li>Put this slide in the fridge and wait a few minutes.</li> <li>Use nailpolish to seal the coverglass. Wait long enoughfor the nailpolish to dry.</li> </ul> </li> </ul> <p>You should a gel of about 100um thickness.</p> <p>It should be possible to add a bit of free dye in the gel in order to help focusing the light sheet. Ideally a bit of far-red free dye, like Alexa 647.</p>
Boulez_dataset_PoD: Dataset containing results from the research project Points of Discontinuity concerning Pierre Boulez, Le Marteau sans Maître pour voix d'alto et 6 instruments (1953–57), no. 7: après "l'artisanat furieux". Rapide
<p>The complete datasets resulting from the research project <em>Points of Discontinuity</em> contain 23 datasets for the musical works or excerpts that were part of the online listening experiment, with each dataset containing seven or eight files (all audio files are stored in a dataset with restricted access), as well as a dataset (PoD_general_dataset) with five additional files.</p> <p>This dataset<strong> Boulez_dataset_PoD </strong>contains eight files:</p> <ul> <li>Boulez_01_ReadMe.pdf</li> <li>Boulez_02_data.xlsx (processed data for this work)</li> <li>Boulez_03_individual_data.xlsx (raw data for each participant obtained from the experiment)</li> <li>Boulez_04_audio.mp3 <strong>[non-public] </strong>(audio recording used in the experiment) [stored in the restricted dataset <a href="https://doi.org/10.5281/zenodo.13981214" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13981214</a>]</li> <li>Boulez_05_model_results.sv (graphical representation of results and the model in Sonic Visualiser) [requires audio file Berg_04_audio.mp3 to display correctly]</li> <li>Boulez_06_SV-data_model+results.zip (text files with the marker locations for all layers in Sonic Visualiser)</li> <li>Boulez_07_model+results_SV-screenshot.pdf (a screenshot of the full-screen display of the SV-file)</li> <li>Boulez_08_annotated_score.pdf (model analysis annotated in the score)</li> </ul>
Shuttle Radar Topography Mission digital elevation models, data points, radiocarbon dates, and geochemical data for the Rub' al Khali Desert
Open the record for dataset details and reuse information.
PRESCOTT/ESCOTT/iGEMME mutational effect predictions of all single point mutations for ~3000 proteins
<p>This dataset contains all necessary data to reproduce our analyses on ~3000 proteins:</p> <p>It is made up of 4 compressed datasets. </p> <ol> <li>All colabfold MSAs and structures for ~3000 proteins: colabfold-sequences-structures-3000-proteins.tar.bz2</li> <li>All escott prediction: escott-v-1-6-0-max-two-components-colabfold-msas-entire-single-point-mutations-cvRC7.tgz</li> <li>All igemme predictions: escott-v-1-6-0-tjet-only-colabfold-msas-entire-single-point-mutations.tgz</li> <li>All gnomad v4.0.0 csv files used for prescott predictions: gnomadv4-0-0-csv-files.tgz</li> </ol> <p>The dataset contains list of 500 proteins used in determining PRESCOTT coefficients in TRAINING-all_gene_names_v4_set1_no_acmg.txt. </p> <p>Furthermore, the list of 1883 proteins used to measure for testing purpose is given in TESTING-all_gene_names_v4_set1_with_acmg.txt. </p>
A point mutation and large deletion at the candidate avirulence locus AvrMlp7 in the poplar rust fungus correlate with poplar RMlp7 resistance breakdown
<p>Several studies reported the rapid evolution of avirulence (<i>Avr</i>) genes to escape <i>R</i>-mediated plant immunity and identified a variety of mechanisms leading to virulence. The poplar rust fungus <i>Melampsora larici-populina</i> is the most damaging pathogen of poplars. A major adaptive event occurred in 1994 with the breakdown of <i>RMlp7</i> resistance gene in poplar. Population genomics studies identified a locus in the genome of <i>M. larici-populina</i>, which likely corresponds to the <i>AvrMlp7</i> candidate avirulence gene. We used a population genetics approach combined with dedicated qPCR assays on a comprehensive set of 281 isolates, covering 27 years (encompassing the resistance breakdown event), to validate the candidate locus and to assess its polymorphism. We found two mechanisms, a point mutation and a deletion, that allowed the pathogen to escape <i>RMlp7</i>-mediated resistance. Six diploid genotypes were thus characterized at the candidate locus (three avirulent and three virulent). In addition, a temporal analysis revealed that the two virulence alleles pre-existed (harboured as avirulent heterozygous genotypes) since the early samplings and were found in association (as virulent genotypes) at the time of the resistance breakdown.These molecular analyses were complemented by a population genetic analysis of those temporal samples, using 22 microsatellite markers.</p>
Density, surface tension, kinematic viscosity, distillation curve, and initial boiling point data of diesel-n-butanol and diesel-ABE blends
<p>This dataset contains measurement data for density, surface tension, kinematic viscosity, distillation curve, and initial boiling point for n-butanol and ABE (acetone, n-butanol, and ethanol in a volume ratio of 3:6:1) blended with standard diesel fuel. Properties of blends were evaluated in terms of temperature and biofuel volume fraction. For a detailed description of the measurements and further information, please see the published paper in Fuel journal (<a href="https://doi.org/10.1016/j.fuel.2021.122909">https://doi.org/10.1016/j.fuel.2021.122909</a>).</p>
Exploring point defects and trap states in undoped SrTiO3 single crystals
<p>This dataset includes the raw data for the publication "Exploring point defects and trap states in undoped SrTiO3 single crystals" by Siebenhofer et. al, published in the Journal of the European Ceramic Society. The originlab file includes all raw data for the original figures presented in the main paper and the supporting information, organized in labelled folders containing tabelled data and the corresponding figure. </p>
Four-point six-loop super-Yang-Mills integrand
<p>The integrand contains data for 151653 diagrams, which are split between two types of files.<br> First is the complete list of graphs in Graphs_SYM_6L. The diagram count can quickly be verified by running the terminal command</p> <p>$ wc -l Graphs_SYM_6L</p> <p>The notation for each graph is as follows:</p> <p>g[k, v, i] -> {vertex_1, vertex_2, vertex_3, vertex_4,…}</p> <p>where each vertex is composed of a list of integers that correspond to outgoing edge (or leg) labels</p> <p>vertex_j = {edge_1, edge_2, edge_3,…}</p> <p>where edge labels that are negative correspond to incoming legs. </p> <p>The momenta of the graph edges satisfy</p> <p>p_{edge} = -p_{-edge},</p> <p>\sum_{edge \in vertex} p_{edge} = 0, </p> <p>and for convenience we only make use of the independent momenta p_1 through p_9, where p_1, p_2, p_3 are the independent external momenta, and p_4, p_5, p_6, p_7, p_8, p_9 are the independent loop momenta. (The fourth external leg is here denoted by edge = 0, and we do not use its momentum.)</p> <p>The graphs are labeled by the shorthand name g[k, v, i] where k is the (Next-to)^k-maximal-cut level that they contribute to, and v is an index that groups the graphs according to which vacuum topology they belong to, and i is an index that distinguishes graphs of the same {k,v} class.</p> <p>For cubic vertices, {edge_1, edge_2, edge_3}, the ordering of the vertex is in one-to-one correspondence with the ordering of the color factor f^{a_{edge_1} a_{edge_2} a_{edge_3}}. Whereas for quartic and higher degree vertices, the information of the color factor is included in the numerator. As a consequence, the cubic vertices are ordered, whereas the higher degree vertices are unordered.</p> <p>The numerators corresponding to the diagrams are labeled as n[k, v, i] and they are collected into separate files Numers_SYM_6L_k_v according to their {k,v} class. Verifying the total number of numerators can be accomplished via</p> <p>$ wc -l Numers_SYM_*</p> <p>where the total result should be 151653.</p> <p>All numerators contain Lorentz products between momentum, given by</p> <p>dij = p_i . p_j</p> <p>For k>= 1, the numerators also contain color factors c[vertex_i] for each higher-than-cubic vertex. Each vertex's color factors have been reduced to an arbitrary (n-2)! half-ladder (Del Duca-Dixon-Maltoni [hep-ph/9910563]) basis for that vertex.</p> <p>This description is reproduced in the "ancillary_readme" file.</p> <p>Since Zenodo does not currently provide a "Download All" button, we suggest you use a Zenodo-targeted downloading tool like <a href="https://doi.org/10.5281/zenodo.1261812">Zenodo_get</a>. We will provide a ZIPed version in a future upload.</p>
Datasets to produce figures of the manuscript "Quantum Critical Points and the Sign Problem"
<p>Datasets to produce figures of the manuscript "Quantum Critical Points and the Sign Problem"</p> <p>Contents:</p> <p>All figures are divided by folders, Main_text (Figs. 1 to 4) and Supplemental Material (Figs. S1 to S11). </p> <p>In each folder the data files (.dat) and scripts (.py) that generate the figures are included. Data files have easily readable names, with relevant parameters explicitly given.</p> <p>Using Python 3+ with an updated Matplotlib can directly reproduce the figures in the manuscript. We further include the Figures (.pdf or .png) in the corresponding folders for convenience.</p> <p>Data can be reproduced using the QUEST: QUantum Electron Simulation Toolbox, freely available at https://www.cs.ucdavis.edu/~bai/QUEST_public/</p> <p>The geometry files (.geom) and example input files (.in) for the two types of lattices used are given in this repository.</p>
3D Point Cloud Data for LiDAR-based Mobile Robot
<p>LiDAR point cloud data serves as an machine vision alternative other than image. Its advantages when compared to image and video includes depth estimation and distance measurement. Low-density LiDAR point cloud data can be used to achieve navigation, obstacle detection and obstacle avoidance for mobile robots. autonomous vehicle and drones. In this metadata, we scanned over 1400 objects and classified it into 6 groups of object namely, human, cars, motorcyclist, signboard, road divider and others.</p>
Investigating the Point of View of Project Management Practitioners on Technical Debt - A Preliminary Study on Stack Exchange
<p>Dataset used in the work 'Investigating the Point of View of Project Management Practitioners on Technical Debt - A Preliminary Study on Stack Exchange'.</p> <p>Authors not presented due to double blind review.</p>
Investigating the Point of View of Project Management Practitioners on Technical Debt - A Preliminary Study on Stack Exchange
<p>Dataset used in the work 'Investigating the Point of View of Project Management Practitioners on Technical Debt - A Preliminary Study on Stack Exchange'.</p>
mCRL2 models, requirements and test logs for the EULYNX Point interface case study
<p>mCRL2 models and mu-calculus formulas for the EULYNX Point Interface. Models and requirements are made in the context of the FormaSig project. Data is made available for replication purposes.</p> <p>REQ_P_001, REQ_P_001_1 and REQ_P_002 are requirements for the point specific mCRL2 model point_spec.mcrl2</p> <p>Remaining .mcf files are requirements for the generic PDI interface pdi_spec.mcrl2</p> <p>Artifacts relating to testing are:</p> <ul> <li>An mCRL2 model, mbt.mcrl2</li> <li>A rename file to rename internal actions to tau, rename_file.re</li> <li>Partial state space associated to mbt.mcrl2, partial_state_space.aut</li> <li>The weak-trace bisim reduced version of the state space, partial_state_space_reduced.aut</li> <li>Testing logs, test-logs.zip</li> <li>The source code of the simulator and the testing tools, Simulator code.zip</li> </ul>
MIS 5e relative sea-level index points along the Pacific coast of North America
<p>This spreadsheet contains data and metadata on relative sea-level index points and associated ages for the Pacific coast of North America. This is Version 1.1, updated after the peer-review of the associated paper.</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.