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294 results for “data discovery”

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zenodo36/100

CESSDA Webinar: Data Discovery

Recording from CESSDA Webinar on Data Discovery Data discovery is a crucial stage in the research process, especially in the social sciences and humanities as many valuable studies have originated from secondary data. Speakers from the Czech Social Science Data Archive (<a href="https://www.cessda.eu/About/Consortium/CESSDA-Countries/CESSDA-Members/Czech-Republic">CSDA</a>), Johana Chylíková and Martin Vávra, took participants on a tour through five core elements of the discovery process. These elements are 1) identification of the purpose of the specific data use intended, 2) finding an appropriate data resource, 3) setting up a search query, 4) selecting the data and finally 5) evaluation of the data quality. The guest speaker, sociologist Kristýna Ba&scaron;ná from the Institute of Sociology, Czech Academy of Sciences, presented her personal experience with discovering data in the context of her research on civic culture and democracy.

opencc-by-sa-4.0Apr 2019View details →

zenodo36/100

Traversing Chemical Space with Active Deep Learning for Low-data Drug Discovery

Raw and processed data from LitPCBA used in the paper "Traversing Chemical Space with Active Deep Learning for Low-data Drug Discovery"

opencc-by-4.0Aug 2024View details →

zenodo36/100

Training data composition determines machine learning generalization and biological rule discovery

Github: https://github.com/csi-greifflab/negative-class-optimization Preprint: https://www.biorxiv.org/content/10.1101/2024.06.17.599333v1

opencc-by-4.0May 2024View details →

zenodo36/100

Data-driven Discovery of Snow Cover Parameterization

All data were derived from SNOTEL, Version 1, and were preprocessed for training symbolic regression models in convenience. Train and test data are range from water years of 2001-2009, and of 2010-2018. All NaN value were removed from data and shape as [Sample, feat]. The dataset contains preprocessed dimensonless features: The name and unit of each feature were listed in sequence as follows:     1. Snow depth (mm)     2. Snow water equivalent (mm)     3. Standard deviation of sub-grid topography (m)     4. Air temperature (K)     5. Precipitation (mm/day)     6. 1/snow density (mm/mm)     7. 1/Standard deviation of sub-grid topography (m^-1) The name and unit of target were listed as follows:      1. Snow cover fraction [%] Some own defined constant: <ol> <li>surface roughness (0.1 m)</li> <li>0 degree of temperature (273.16 K)</li> <li>own defined std threshold (200 m)</li> <li>mean SWE (122.3 mm)</li> </ol>

opencc-by-4.0Apr 2024View details →

zenodo36/100

Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling (Source code and data)

This dataset contains <ul> <li>the source code</li> <li>the data and examples</li> <li>the material subroutine with examples of uniaxial strain and stress</li> </ul> of the inelastic Constitutive Artificial Neural Network (iCANN) enhanced by the concept of homeostatic surfaces to discover tensional homeostasis. The corresponding publication is: Holthusen, H., Brepols, T., Linka, K., & Kuhl, E.. Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling.   Standalone_Materialroutine <ul> <li>00_Materialroutine: Contains the material subroutine implemented in FORTRAN</li> <li>01_uniaxial_strain: Example of the material subroutine in a uniaxial strain driven manner</li> <li>02_uniaxial_stress: Example of the material subroutine in a uniaxial stress driven manner</li> </ul>   TensorFlow <ul> <li> iCANN: <ul> <li> 01_Biax/biax_l1: Keras/TensorFlow implementation of the iCANN. Example of the cross specimen with L1 (Lasso) regularization </li> <li> 01_Biax/biax_l2: Keras/TensorFlow implementation of the iCANN. Example of the cross specimen with L2 (ridge) regularization </li> <li> 02_Uniax/uniax_l1: Keras/TensorFlow implementation of the iCANN. Example of the stripe specimen with L1 (Lasso) regularization </li> <li>02_Uniax/uniax_l1: Keras/TensorFlow implementation of the iCANN. Example of the stripe specimen with L2 (ridge) regularization</li> </ul> </li> <li> iCANN_ABS_activation: Same four examples as above, however, with the absolute value activation function </li> <li> installed_packages: File containing a list of installed Python modules used to implement the iCANN </li> </ul> The TensorFlow implementations in all 01_Biax/ and 02_Uniax/ sub-directories are the same. The implementation in iCANN_ABS_activation is different with respect to the activation functions of the pseudo potential.   The experimental data for the cross and stripe specimen are taken from the literature: Eichinger, J. F., Paukner, D., Szafron, J. M., Aydin, R. C., Humphrey, J. D., & Cyron, C. J. (2020). Computer-controlled biaxial bioreactor for investigating cell-mediated homeostasis in tissue equivalents. Journal of biomechanical engineering, 142(7), 071011. <a href="https://doi.org/10.1115/1.4046201">https://doi.org/10.1115/1.4046201</a>

opencc-by-4.0Oct 2024View details →

dryad36/100

Data from: A RAD-sequencing approach to genome-wide marker discovery, genotyping, and phylogenetic inference in a diverse radiation of primates

Until recently, most phylogenetic and population genetics studies of nonhuman primates have relied on mitochondrial DNA and/or a small number of nuclear DNA markers, which can limit our understanding of primate evolutionary and population history. Here, we describe a cost-effective reduced representation method (ddRAD-seq) for identifying and genotyping large numbers of SNP loci for taxa from across the New World monkeys, a diverse radiation of primates that shared a common ancestor ~20-26 mya. We also estimate, for the first time, the phylogenetic relationships among 15 of the 22 currently-recognized genera of New World monkeys using ddRAD-seq SNP data using both maximum likelihood and quartet-based coalescent methods. Our phylogenetic analyses robustly reconstructed three monophyletic clades corresponding to the three families of extant platyrrhines (Atelidae, Pitheciidae and Cebidae), with Pitheciidae as basal within the radiation. At the genus level, our results conformed well with previous phylogenetic studies and provide additional information relevant to the problematic position of the owl monkey (Aotus) within the family Cebidae, suggesting a need for further exploration of incomplete lineage sorting and other explanations for phylogenetic discordance, including introgression. Our study additionally provides one of the first applications of next-generation sequencing methods to the inference of phylogenetic history across an old, diverse radiation of mammals and highlights the broad promise and utility of ddRAD-seq data for molecular primatology.

opencc-zeroDec 2017View details →

zenodo36/100

Data from "Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR" manuscript

Data from "Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR" manuscript. Full results for applications 1 and 2.

opencc-by-4.0Dec 2020View details →

zenodo36/100

ESPRESSO: Robust discovery and quantification of transcript isoforms from error-prone long-read RNA-seq data (repository for simulated ONT RNA-seq data)

Simulated ONT direct RNA and 1D cDNA sequencing data of varying sequencing depths (0.5 million, 1 million, 3 million, and 5 million simulated reads) used for benchmark evaluations of transcript discovery and quantification in our paper "ESPRESSO: Robust discovery and quantification of transcript isoforms from error-prone long-read RNA-seq data". All details can be found in the Materials and Methods section of the paper.  HEK293T_DirectRNA.transcriptome_quantification.tsv and HEK293T_DirectRNA.transcriptome_quantification.tsv are tab-separated files containing estimated raw read counts and normalized abundance values (in TPM) of transcripts annotated in GENCODE v34lift37. Transcript quantification was done using NanoSim (version 3.1.0).  HEK293T_DirectRNA.NanoSim_500k.fastq.gz, HEK293T_DirectRNA.NanoSim_1M.fastq.gz, HEK293T_DirectRNA.NanoSim_3M.fastq.gz, and HEK293T_DirectRNA.NanoSim_5M.fastq.gz are gzip compressed FASTQ files containing 0.5 million, 1 million, 3 million, and 5 million simulated ONT direct RNA sequencing reads respectively.  HEK293T_1DcDNA.NanoSim_500k.fastq.gz, HEK293T_1DcDNA.NanoSim_1M.fastq.gz, HEK293T_1DcDNA.NanoSim_3M.fastq.gz, and HEK293T_1DcDNA.NanoSim_5M.fastq.gz are gzip compressed FASTQ files containing 0.5 million, 1 million, 3 million, and 5 million simulated ONT 1D cDNA sequencing reads respectively.

opencc-by-4.0Oct 2022View details →

dryad36/100

Data from: Phylogeography, hybridization, and species discovery in the Etheostoma nigrum complex (Percidae: Etheostoma: Boleosoma)

The history of riverine fish diversification is largely a product of geographic isolation. Physical barriers that reduce or eliminate gene flow between populations facilitate divergence via genetic drift and natural selection, eventually leading to speciation. For freshwater organisms, diversification is often the product of drainage basin rearrangements. In young clades where the history of isolation is the most recent, evolutionary relationships can resemble a tangled web. One especially recalcitrant group of freshwater fishes is the Johnny Darter (Etheostoma nigrum) species complex, where traditional taxonomy and molecular phylogenetics indicate a history of gene flow and conflicting inferences of species diversity. Here we assemble a genomic dataset using double digest restriction site associated DNA (ddRAD) sequencing and use phylogenomic and population genetic approaches to investigate the evolutionary history of the complex of species that includes E. nigrum, E. olmstedi, E. perlongum, and E. susanae. We reveal and validate several evolutionary lineages that we delimit as species, highlighting the need for additional work to formally describe the diversity of the Etheostoma nigrum complex. Our analyses also identify gene flow among recently diverged lineages, including one instance involving E. susanae, a localized and endangered species. Phylogeographic structure within the Etheostoma nigrum species complex coincides with major geologic events, such as parallel divergence in river basins during Pliocene inundation of the Atlantic coastal plain and multiple northward post-glacial colonization routes tracking river basin rearrangements. Our study serves as a nuanced example of how low dispersal rates coupled with geographic isolation among disconnected river systems in eastern North America have produced one of the world's freshwater biodiversity hotspots.

opencc-zeroOct 2022View details →

zenodo36/100

Data for HydrAMP - a deep generative model for antimicrobial peptide discovery

<ul> <li>data- training data for peptides < 25 AA (16.8 MB)</li> <li>models - checkpoints of HydrAMP, PepCVAE, and Basic models for every training epoch (466 MB)</li> <li>results - dumped generation results for every model. Required for running comparison notebooks (832 MB)</li> <li>wheels - custom TensorFlow packages (1 GB)</li> </ul>

openmit-licenseDec 2022View details →

dryad36/100

Data from: Exceptional levels of species discovery ameliorate inferences of the biogeography and diversification of an Afrotropical catfish family

Endeavours in species discovery, particularly the characterisation of cryptic species, have been greatly aided by the application of DNA molecular sequence data to phylogenetic reconstruction and inference of evolutionary and biogeographic processes. However, the extent of cryptic and undescribed diversity remains unclear in tropical freshwaters, where biodiversity is declining at alarming rates. To investigate how data on previously undiscovered biodiversity impacts inferences of biogeography and diversification dynamics, we generated a densely sampled species-level family tree of Afrotropical Mochokidae catfishes that was ca. 70% complete. This was achieved through extensive continental sampling specifically targeting the genus Chiloglanis a specialist of the relatively unexplored fast-flowing lotic habitat. Applying multiple species-delimitation methods, we report exceptional levels of species discovery for a vertebrate genus, conservatively delimiting a staggering ca. 50 putative new Chiloglanis species, resulting in a near 80% increase in species richness for the genus. Biogeographic reconstructions of the family identified the Congo Basin as a critical region in the generation of mochokid diversity, and our study further revealed complex scenarios for the build-up of continental assemblages of the two most species-rich mochokid genera, Synodontis and Chiloglanis. While Syndontis showed most divergence events within freshwater ecoregions consistent with largely in situ diversification, Chiloglanis showed much less aggregation of freshwater ecoregions, suggesting dispersal as a key diversification process in this older group. Despite the significant increase in mochokid diversity identified here, diversification rates were best supported by a constant rate model consistent with patterns in many other tropical continental radiations. While our findings highlight fast-flowing lotic freshwaters as potential hotspots for undescribed and cryptic species diversity, a third of all freshwater fishes are currently threatened with extinction, signifying an urgent need to increase exploration of tropical freshwaters to better characterise and conserve its biodiversity.

opencc-zeroMar 2023View details →

zenodo36/100

Original data for "Unsupervised Analysis of Optical Imaging Data for the Discovery of Reactivity Patterns in Metal Alloy" article

This upload includes both original and supplementary data for the publication "Unsupervised Analysis of Optical Imaging Data for the Discovery of Reactivity Patterns in Metal Alloy" by R. Li, A. Makogon, T. Galochkina, J-F. Lemineur, F. Kanoufi, and V. Shkirskiy, published in the Small Methods journal. The preprint version of the paper is available on ChemRxiv (<a href="https://doi.org/10.26434/chemrxiv-2023-sgvt0">https://doi.org/10.26434/chemrxiv-2023-sgvt0</a>). The file "Data_processing.zip" contains the original optical image of the interface, maps of film evolution rates in acidic and salt environments, COMSOL data output in acidic and salt environments, as well as a Jupyter Lab file that demonstrates how to process this data. The file "Comparison_of_SEM_images.zip" includes the original SEM images used in the current study and in our previous work (<a href="https://doi.org/10.26434/chemrxiv-2022-rn77b-v3">https://doi.org/10.26434/chemrxiv-2022-rn77b-v3</a>), along with a Jupyter Lab file that illustrates the data processing procedure. The file "Original_data_in_npy_format.zip" contains all original data from SEM/EDX and RM experiments. We recommend opening the Jupyter Lab files in a Python 3 environment. PDF files in the root directory provide outputs of all uploaded Jupyter Lab files.

opencc-by-4.0Jun 2023View details →

ClinicalTrials.gov36/100

DISCOVERY: Diagnostic Data and Genetic Polymorphisms in ICD Patients.

ClinicalTrials.gov study NCT00478933. IPD Sharing: NO. Countries: 0. Publications: 1.

closedIPD-NOFeb 2026View details →

dryad36/100