Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,049
datasets available to search
ShareScore release 0.9.0
Dataset results
1,049 results for “Robustness”
Data from: Butterflies are not a robust bioindicator for assessing pollinator communities, but floral resources offer a promising way forward
<p>Monitoring pollinators is crucial for the evaluation of biodiversity and potential pollination services. Yet, efficiently monitoring multiple taxa over large areas can be costly. An alternative approach is using simple species bioindicators that represent the entire pollinator community. One of the requirements of a good bioindicator is that it can be easily identified to lower taxonomic levels and be sensitive to changes in habitat. This is the case for butterflies, a taxon for which many countries have a country-wide long-term monitoring scheme. We tested whether butterfly diversity can be used to predict diversity of bees and hoverflies both spatially and temporally. We surveyed 42 transects of the Dutch Butterfly Monitoring Scheme in 2020, to record species richness and abundance of butterflies, bees and hoverflies. We also recorded flower area and richness in the pollinator transects. To test whether pollinators with similar functional traits are more closely correlated than the entire pollinator community, we categorized bee and butterfly species according to their diet breadth (polyphagous vs. non-polyphagous), nitrogen-affinity (nitrophobous vs. nitrophilous larval resources) and body size. We used the same methods to test for temporal correlations over seven years for one site in Spain. Butterfly richness was not spatially correlated with bee richness (Pearson's r = 0.13), nor were the two taxa temporally correlated (Pearson's r = 0.02). Interestingly, hoverfly richness was spatially correlated with butterfly richness (Pearson's r = 0.43) and with bee richness (Pearson's r = 0.36) in the Netherlands and, hence, hoverflies might be slightly more suitable as a bioindicator of pollinator diversity in this area. Abundance of all three taxa showed no significant inter-correlation, except for correlations between diet specialist bees and butterflies (Pearson's r = 0.39). Importantly, all three taxa were strongly correlated with flower richness, but they varied in their preferences for host plant families. This is in line with 75% of the plant-pollinator studies finding significant positive relations. For monitoring schemes to be effective in informing better pollinator conservation, they should expand to include bees and hoverflies as well as simple indicators of habitat quality such as floral resources.</p>
Supplementary information: How robust is the ligand binding transition state?
<p>We have used the REVO weighted ensemble approach followed by Markov state models to identify the ligand unbinding transition states for five ligands unbinding from the enzyme soluble epoxide hydrolase (sEH). This repo provides the <em><strong>counts matrices, properties and state (cluster) labels</strong></em> of the markov state models. The counts matrices can be converted to conformation space networks using CSNAnalysis software (<a href="https://github.com/ADicksonLab/CSNAnalysis">https://github.com/ADicksonLab/CSNAnalysis</a>). The <em><strong>networks</strong></em> are also provided in the gexf formatted files to be visualized in gephi (<a href="https://github.com/gephi/gephi">https://github.com/gephi/gephi</a>).</p>
Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
<p>Data used to test the robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data, described in <a href="https://doi.org/10.1186/s13059-020-1949-z">Holland et al. 2020</a>.</p> <p>The folder <em>data </em>contains<em> </em>raw data and the folder <em>output</em> contains intermediate and final results of all analyses. </p> <p>The associated analyses code and more information are available on <a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq">GitHub</a>.</p> <p> </p> <p><strong>Abstract</strong></p> <p><strong>Background</strong></p> <p>Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way.</p> <p><strong>Results</strong></p> <p>To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community.</p> <p><strong>Conclusions</strong></p> <p>Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.</p> <p> </p> <p>For questions related to the data please write an email to christian.holland@bioquant.uni-heidelberg.de or use the <a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq/issues">GitHub issue system</a>.</p>
Emergence of cooperative bistability and robustness of gene regulatory networks
<p>Simulation and analysis source codes and obtained data set for "Emergence of cooperative bistability and robustness of gene regulatory network" (<a href="https://doi.org/10.1371/journal. pcbi.1007969">PLoS Comput Biol 16 (2020) e1007969</a> and <a href="https://arxiv.org/abs/1907.12030">arXiv:1907.12030</a>) by Nagata and Kikuchi. </p> <p>Source codes and figures are compiled in Jupyter notebook. Detailed discription of data sets is found in "readme.txt" file.</p> <p> </p>
Robust step detection from different waist-worn sensor positions – implications for clinical studies
<p>The dataset contains tri-axial acceleration and gyroscope data (100 Hz sampling) from walks from 19 healthy volunteers, each walking up to three times a parcours of 20 meters with self-selected speed, slow speed or with five soft turns at self-selected speed. Each participant wore 11 time-synchronized sensors during these tests: left/right foot, 5 around waist, non-dominant wrist and upper arm and collar and pocket. In addition to the sensor recordings each 20 meter walk was timed with a stop-watch. </p> <p>Also see: <a href="https://doi.org/10.1159/000511611">https://doi.org/10.1159/000511611</a></p>
MAGIC Deliverable 5.5 - Datasets - Report on the Quality Check of the Robustness of the Narrative behind the Common Agricultural Policy (CAP)
<p>This repository contains datasets used in the production of figures contained in MAGIC Deliverable 5.5</p> <p>Matthews K.B., Blackstock K.L., Waylen K.A., Juarez-Bourke A., Miller D.G., Wardell-Johnson D.H., Rivington M. (2018) Report on the Quality Check of the Robustness of the Narrative behind the Common Agricultural Policy (CAP).</p> <p>MAGIC (H2020-GA 689669) Project Deliverable 5.5 - 29th November 2018</p> <p><a href="https://magic-nexus.eu/documents/d55-report-narratives-behind-cap">MAGIC Deliverable 5.5</a></p>
Evolution enhances mutational robustness and suppresses the emergence of a new phenotype
<p>Source codes and data sets for arXiv:2012.03030. For details, see readme.txt file.</p>
Image 10 in Robust Trapdoor Tarantula Haploclastus validus Pocock, 1899: notes on taxonomy, distribution and natural history (Araneae: Theraphosidae: Thrigmopoeinae)
Image 10. Habitat destruction at Aarey Milk Colony for removal of soil for brick making. Note the exposed burrow due to this practice in the inset
Image 3 in Robust Trapdoor Tarantula Haploclastus validus Pocock, 1899: notes on taxonomy, distribution and natural history (Araneae: Theraphosidae: Thrigmopoeinae)
Image 3. Haploclastus validus female from Aarey Milk Colony (Mumbai, Maharashtra) depicting coloration in life. Not collected
Figures 8–11. 8 in Robust Trapdoor Tarantula Haploclastus validus Pocock, 1899: notes on taxonomy, distribution and natural history (Araneae: Theraphosidae: Thrigmopoeinae)
Figures 8–11. 8 - Spermathecae; 9 - Male palp, prolateral view; 10 - Male palp, retrolateral view; 11 - Male palp, ventral view (scale 1mm)
Figures 1–7. 1 in Robust Trapdoor Tarantula Haploclastus validus Pocock, 1899: notes on taxonomy, distribution and natural history (Araneae: Theraphosidae: Thrigmopoeinae)
Figures 1–7. 1 - Dorsal view of spider, scale 0.5mm; 2 - Eye, scale 1mm; 3 - Sternum, maxillae, labium, scale 0.5mm; 4 - Chelicerae, scale 1mm; 5 - Chelicerae teeth, scale 1mm; 6 - Maxillae, scale 1mm; 7 - Spinnerets, scale 1mm
Robustness of massively parallel sequencing platforms
<p>The improvements in high throughput sequencing technologies (HTS) made clinical sequencing projects such as ClinSeq and Genomics England feasible. Although there are significant improvements in accuracy and reproducibility of HTS based analyses, the usability of these types of data for diagnostic and prognostic applications necessitates a near perfect data generation. To assess the usability of a widely used HTS platform for accurate and reproducible clinical applications in terms of robustness, we generated whole genome shotgun (WGS) sequence data from the genomes of two human individuals in two different genome sequencing centers. After analyzing the data to characterize SNPs and indels using the same tools (BWA, SAMtools, and GATK), we observed significant number of discrepancies in the call sets. As expected, the most of the disagreements between the call sets were found within genomic regions containing common repeats and segmental duplications, albeit only a small fraction of the discordant variants were within the exons and other functionally relevant regions such as promoters. We conclude that although HTS platforms are sufficiently powerful for providing data for first-pass clinical tests, the variant predictions still need to be confirmed using orthogonal methods before using in clinical applications. </p>
Test Collection Reliability: A Study of Bias and Robustness to Statistical Assumptions via Stochastic Simulation
<p>This archive contains the simulated collections, their diagnosis data, and the estimates of accuracy. For the full code and description, please refer to https://github.com/julian-urbano/irj2015-reliability</p>
PRIMPOL ensures robust handoff between on-the-fly and post-replicative DNA lesion bypass.
<p><strong>Supplementary Table 6. CRISPR screen raw sgRNA counts.</strong></p><p>Excel sheet 'Mellor et al Table S6_sgRNA_count.xlsx'.</p><p>Output of the MAGeCK count command aligning Illumina sequencing reads to the sgRNA sequences within the Human Improved Genome-wide Knockout CRISPR library, following CRISPR/Cas9 screens in WT and <i>primpol</i> TK6 cells.</p><p> </p><p><strong>Supplementary Table 7. CRISPR screen untreated summary.</strong></p><p>Excel sheet 'Mellor et al Table S7_untreated_summary.xlsx'.</p><p>Output of the MAGeCK test command comparing sgRNA abundance between the sequencing libraries produced following a CRISPR/Cas9 knockout screen in WT versus <i>primpol</i> TK6 cells in untreated conditions.</p><p> </p><p><strong>Supplementary Table 8. CRISPR screen cisplatin treated summary.</strong></p><p>Excel sheet 'Mellor et al Table S8_cddp_treated_summary.xlsx'.</p><p>Output of the MAGeCK test command comparing sgRNA abundance between the sequencing libraries produced following a CRISPR/Cas9 knockout screen in WT versus <i>primpol</i> TK6 cells challenged with 7 days continuous 0.25 μM cisplatin treatment.</p><p> </p><p><strong>readme.xlsx</strong></p><p>Tab 1: List of files</p><p>Tab 2: Summary of CRISPR screen samples & conditions</p><p> </p><p><strong>Raw sequencing data (zipped fastq files)</strong></p><p>WT_un_1.fastq.gz Wild type untreated replicate 1<br>WT_un_2.fastq.gz Wild type untreated replicate 2<br>WT_cis_1.fastq.gz Wild type cisplatin-treated replicate 1<br>WT_cis_2.fastq.gz Wild type cisplatin-treated replicate 2<br>Pp_un_1.fastq.gz <i>primpol </i>untreated replicate 1<br>Pp_un_2.fastq.gz <i>primpol </i>untreated replicate 2<br>Pp_cis_1.fastq.gz <i>primpol </i>cisplatin-treated replicate 1<br>Pp_cis_2.fastq.gz <i>primpol </i>cisplatin-treated replicate 2</p><p> </p>
Determining non-significant bits on a C++ implementation of the LeNet-5 convolutional neural network to be used for storing error correcting codes to protect weights and biases. Robustness assessment of the network after integrating the proposed codes.
<p>The architecture of the LeNet-5 convolutional neural network (CNN) was defined by LeCun in its paper "Gradient-based learning applied to document recognition" (<a href="https://ieeexplore.ieee.org/document/726791">https://ieeexplore.ieee.org/document/726791</a>) to classify images of hand written digits (MNIST dataset).</p><p>This architecture has been customized to use Rectified Linear Unit (ReLU) as activation functions instead of Sigmoid.</p><p>It consists of the following layers:</p><ul><li><strong>conv1</strong>: Convolution 2D, 1 input channel (28x28), 3 output channels (28x28), kernel size 5, stride 1, padding 2.</li><li><strong>relu1</strong>: Rectified Linear Unit (3@28x28).</li><li><strong>max1</strong>: Subsampling buy max pooling (3@14x14).</li><li><strong>conv2</strong>: Convolution 2D, 3 input channels (14x14), 6 output channels (14x14), kernel size 5, stride 1, padding 2.</li><li><strong>relu2</strong>: Rectified Linear Unit (6@14x14).</li><li><strong>max2</strong>: Subsampling buy max pooling (6@7x7).</li><li><strong>fc1</strong>: Fully connected (294, 147)</li><li><strong>fc2</strong>: Fully connected (147, 10)</li></ul><p>The fault hypotheses for this work include the occurrence of:</p><ul><li><strong>S0</strong>/<strong>S1</strong>: multiple adjacent stuck-at-0 and stuck-at-1 faults to determine the least significant bits of weights and biases that could be used to store the proposed error correcting codes.</li><li><strong>BF</strong>: single, double, and triple bit-flip faults to assess the robustness of the considered CNN</li></ul><p>In the memory cells containing all the parameters of the CNN: </p><ul><li><strong>w</strong>: weights (float32)</li><li><strong>b</strong>: biases (float32)</li></ul><p>All the images (10000) from the MNIST dataset have been used as workload.</p><p>The weights and biases of the LeNet-5 architecture have been protected using six different error correcting codes that have been deployed in the least significant bits of these elements.</p><p>The parity check matrices (H = P I) that define these ECCs are:</p><ul><li><strong>SEC(32, 26)</strong> (Hamming) under a <i>classic policy </i>(see methodology below):</li></ul><p><i> 11010010001000011101101000 100000</i></p><p><i> 10101001000100011011010100 010000</i></p><p><i> 01100100100010010110110010 001000</i></p><p><i> 00011100010001001110001101 000100</i></p><p><i> 00000011110000100001111011 000010</i></p><p><i> 00000000001111100000000111 000001</i></p><ul><li><strong>SEC(23, 18)</strong> (Hamming) under a <i>conservative policy</i> (see methodology below):</li></ul><p><i> 111100001111000000 10000</i></p><p><i> 110011101000111000 01000</i></p><p><i> 101011010100100110 00100</i></p><p><i> 010110110010010101 00010</i></p><p><i> 001101110001001011 00001</i></p><ul><li><strong>SEC(13, 9)</strong> (Hamming) under an <i>aggressive policy </i>(see methodology below):</li></ul><p><i> 110111000 1000</i></p><p><i> 101100110 0100</i></p><p><i> 011010101 0010</i></p><p><i> 111001011 0001</i></p><ul><li><strong>DEC(32, 21)</strong> (low redundancy and reduced overhead DEC) under a <i>classic policy </i>(see methodology below):</li></ul><p><i> 111000011001010010000 10000000000</i></p><p><i> 110110000011101000000 01000000000</i></p><p><i> 101011000110000010001 00100000000</i></p><p><i> 100101101000110001000 00010000000</i></p><p><i> 011010101100100000100 00001000000</i></p><p><i> 010101010100001001010 00000100000</i></p><p><i> 001100110010010100100 00000010000</i></p><p><i> 000011110001000110010 00000001000</i></p><p><i> 000000001111001101001 00000000100</i></p><p><i> 000000000000111100111 00000000010</i></p><p><i> 000000000000000011111 00000000001</i></p><ul><li><strong>DEC(28, 18)</strong> (low redundancy and reduced overhead DEC) under a <i>conservative policy </i>(see methodology below):</li></ul><p><i> 111111000000000000 1000000000</i></p><p><i> 110100111100000000 0100000000</i></p><p><i> 110000100011110000 0010000000</i></p><p><i> 001110010011001100 0001000000</i></p><p><i> 101100001010101010 0000100000</i></p><p><i> 010001001101010110 0000010000</i></p><p><i> 001011000101101001 0000001000</i></p><p><i> 101000011000110101 0000000100</i></p><p><i> 010001110000011011 0000000010</i></p><p><i> 000010100110000111 0000000001</i></p><ul><li><strong>DEC(17, 9)</strong> (low redundancy and reduced overhead DEC) under an <i>aggressive policy </i>(see methodology below):</li></ul><p><i> 111110000 10000000</i></p><p><i> 111001100 01000000</i></p><p><i> 110101010 00100000</i></p><p><i> 101010110 00010000</i></p><p><i> 101101001 00001000</i></p><p><i> 100110101 00000100</i></p><p><i> 100011011 00000010</i></p><p><i> 110000111 00000001</i></p><p>This dataset contains the raw data obtained from:</p><ul><li>running exhaustive fault injection campaigns for increasingly multiple stuck-at faults in the least significant bits of all weights and biases (simultaneously) and for all the images in the workload.</li><li>running statistical fault injection campaigns for single, double, and triple bit-flip faults, randomly targeting the considered locations and images in the workload.</li></ul><h3>Files information</h3><ul><li><i>no_ecc </i>folder: Results obtained for the original (not protected) version of the CNN.<ul><li><i>golden_run.csv</i>: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults.</li><li><i>sampling_SBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for single bit-flip faults.</li><li><i>sampling_DBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults.</li><li><i>sampling_TBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for triple bit-flip faults.</li><li><i>locating_sensitive_bits </i>folder: Prediction obtained for all the images considered in the workload in presence of stuck-at-0/stuck-at-1 faults that simultaneously target the N least significant bits of all weights and biases. There is one file for each parameter of type of fault and range of targeted bits. Files for bits in the range [11, 0] are not included as they obtain eactly the same results as the Golden Run (faults do not alter the behaviour of the network).</li></ul></li><li><i>sec/classic</i>, <i>sec/conservative</i>, and <i>sec/aggressive</i> folders: They contain the results obtained for the CNN protected by SEC(32, 26), SEC(23, 18), and SEC(13, 9), respectively.<ul><li><i>golden_run.csv</i>: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults. It must be noted that this file could be different that the golden_run.csv file for the original version of the CNN, as deploying the ECC in the weights and biases may have affected the behaviour of the network.</li><li><i>sampling_SBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for single bit-flip faults. They should all be tolerated by the definition of the ECC.</li><li><i>sampling_DBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults. They could be more harmful than for the unprotected version of the CNN, as the ECC may erroneously flip correct bits.</li></ul></li><li><i>dec/classic</i>, <i>dec/conservative</i>, and <i>dec/aggressive </i>folders: They contain the results obtained for the CNN protected by DEC(32, 21), DEC(28, 18), and DEC(17, 9), respectively.<ul><li><i>golden_run.csv</i>: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults. It must be noted that this file could be different that the golden_run.csv file for the original version of the CNN, as deploying the ECC in the weights and biases may have affected the behaviour of the network.</li><li><i>sampling_DBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults. They should all be tolerated by the definition of the ECC.</li><li><i>sampling_TBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for triple bit-flip faults. They could be more harmful than for the unprotected version of the CNN, as the ECC may erroneously flip correct bits.</li></ul></li></ul><h3>Methodology information</h3><p>First, the CNN was used to classify all the images of the workload in the absence of faults to get a reference to determine the impact of faults. This is <i>golden_run.csv</i> file.</p><p>To locate non-significant bits in weights and biases, fault injection experiments were executed targeting all elements of all parameters of the CNN using the following procedure:</p><ul><li>The initial mask targeted only the least significant bit</li><li>Until the mask targets all bits of the elements (32 bits as they are single-precision floating point values):<ul><li>Affect the bits (setting them to 0 or 1 in case of stuck-at-0 or stuck-at-1 faults) identified by the mask for all elements of all parameters.</li><li>Classify all the images of the workload in the presence of this fault. The obtained output was stored in a given .csv file.</li><li>Remove the fault from the CNN by restoring the affected bits to its previous value.</li><li>Add the next adjacent bit to the mask, so it targets an additional least significant bit.</li></ul></li></ul><p>The analysis of the obtained results may help in determining which bits can be used to store an ECC:</p><ul><li>which bits never affect the behaviour of the CNN, as the predicted classification is exactly the same than in the absence of faults.</li><li>which bits midly affect the behaviour of the CNN, as although the predicted classifications differ from those in the absence of faults, the accuracy of the network is barely affected.</li><li>which bits greatly affect the behaviour of the CNN, as the accuracy of the network is significantly affected.</li></ul><p>Accordingly, three different policies have been identified for deploying an ECC using these bits:</p><ul><li><strong>Classic policy</strong>: The ECC protects as much bits as possible.</li><li><strong>Conservative policy</strong>: The ECC protects all those bits that may affect the prediction of the network.</li><li><strong>Aggressive policy</strong>: The ECC protects only those bits that significantly affect the accuracy of the network.</li></ul><p>After designing and deploying a single ECC and a double ECC for each of the identified policies, fault injection experiments were executed to verify their behaviour in the presence of faults.</p><p>Single and double ECCs were tested against single and double bit-flip, respectively (all faults should be tolerated,) and double and triple bit-flips, respectively (a correct bit could be erroneously flipped.)</p><p>Due to the heavy computational load of the decoders, statistical injection was used to run the required fault injection campaigns with a sample size (number of experiments) of 10000.</p><p>Each experiment consisted in:</p><ul><li>Randomly selecting the image to process, and the parameter, element, and bits (mask) to be targeted by the fault.</li><li>Affecting the bits (inverting them) identified by the mask.</li><li>Classifying the selected image of the workload in the presence of this fault. The obtained output was stored in a given .csv file.</li><li>Removing the fault from the CNN by restoring the affected bits to its previous value.</li></ul><h3>List of variables (Name : Description (Possible values))</h3><ul><li><strong>IMGID</strong>: Integer number identifying the considered image (1-9999).</li><li><strong>TENSORID</strong>: Integer number identiying the parameter affected by the fault (0 - No fault, 1 - conv1.w, 2 - conv1.b, 3 - conv2.w, 4 - conv2.b, 5 - fc1.w, 6 - fc1.b, 7 - fc2.w, 8 - fc2.b).</li><li><strong>ELEMID</strong>: Integer number identiying the element of the parameter affected by the fault (-1 - No fault, [0-2] - conv1.b, [0-74] - conv1.w, [0-5] - conv2.b, [0-149] - conv2.w, [0-146] - fc1.b, [0-43217] - fc1.w, [0-9] - fc2.b, [0-1469] - fc2.w).</li><li><strong>MASK</strong>: 8-digit hexadecimal number identifying those bits affected by the fault ([00000000 - No fault, FFFFFFFF - all 32 bits faulty]).</li><li><strong>FAULT</strong>: String identiying the type of fault (NF - No fault, BF - bit-flip, S0 - Stuck-at-0, S1 - Stuck-at-1).</li><li><strong>SOFTMAX</strong>: 10 decimal numbers obtained after applying the softmax function to the provided output. They represent the probability of the image of belonging to the corresponding category for classification.</li><li><strong>PRED</strong>: Integer number representing the category predicted for the processed image.</li><li><strong>LABEL</strong>: integer number representing the actual category for the processed image.</li></ul>
Early-season biomass and weather enable robust cereal rye cover crop biomass predictions
<p>Farmers need accurate estimates of winter cover crop biomass to make informed decisions on termination timing or to estimate potential release of nitrogen from cover crop residues to subsequent cash crops. Utilizing data from an extensive experiment across 11 states from 2016 to 2020, this study explores the most reliable predictors for determining cereal rye cover crop biomass at the time of termination. Our findings demonstrate a strong relationship between early-season and late-season cover crop biomass. Employing a random forest model, we predicted late-season cereal rye biomass with a margin of error of approximately 1,000 kg ha<sup>-1</sup> based on early-season biomass, growing degree days, cereal rye planting and termination dates, photosynthetically active radiation, precipitation, and site coordinates as predictors. Our results suggest that similar modeling approaches could be combined with remotely sensed early-season biomass estimations to improve the accuracy of predicting winter cover crop biomass at termination for decision support tools.</p>
Data and code for: The centrality of the Huanan market among early COVID-19 cases is robust to fundamental misconceptions about epidemiology and misrepresentations of Worobey et al. (2022)
<p>Data and R code for <br>The centrality of the Huanan market among early COVID-19 cases is robust to fundamental misconceptions about epidemiology and misrepresentations of Worobey et al. (2022)</p>
Accompanying data for the paper "Robustness of the Data-Driven Identification algorithm with incomplete input data"
<h2>Links</h2> <ul> <li>isSupplementTo <em>publication-article</em> <a href="https://doi.org/10.46298/jtcam.12590">https://doi.org/10.46298/jtcam.12590</a></li> <li>isNewVersionOf <em>dataset</em> <a href="../records/10090469">https://zenodo.org/records/10090469</a></li> </ul> <h2>Authors</h2> <ul> <li><strong>Leygue, Adrien</strong>, Ecole Centrale de Nantes, ORCID: <a href="https://orcid.org/0000-0003-0714-822X">0000-0003-0714-822X</a></li> </ul> <h2>Language</h2> <ul> <li>English</li> </ul> <h2>License</h2> <ul> <li>Creative Commons Attribution 4.0</li> </ul> <h2>Funding sources</h2> <ul> <li>This work was performed by using HPC resources of Centrale Nantes Supercomputing Center on the cluster Liger, granted and identified D1705030 by the High Performance Computing Institute(ICI).</li> </ul> <h2>Data structure and information</h2> <p>Synthetic data used in the case study (section 3) of the paper.</p> <p>The data in XDMF (Milou.xdmf ) + hdf5 (Milou.hdf5) format comprises:</p> <ol> <li>The 2D computational mesh with triangular linear elements</li> <li>The nodal Forces for all loading steps (nodal quantity)</li> <li>The displacement for all loading steps (nodal quantity)</li> <li>Cauchy stress fields for all loading steps (cell quantity)</li> </ol>
FIGURE 3 in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr
FIGURE 3 Mass–standard length (M–LS) relationships (MLR) determined for exercised () and control () Salmo trutta cohorts over 0–32 weeks from treatment initiation. Each cohort included LS00 individuals (n = 6) as a common origin
FIGURE 1 in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr
FIGURE 1 (a) Landmark positions () on Salmo trutta parr that were digitised twice and then averaged to minimize measurement error. (b) Shape changes associated with principal components (PCs) 1–3. PCs were derived from a between-group PC analysis of Procrustes superimposed landmarks., Consensus shape with numbered landmark positions;, Shape changes associated with each PC. Shape changes are scaled to observed PC scores: Left hand side shape changes (back outlines) are scaled to the minimum value observed across the sample on each respective PC (shown below the image) and right hand side shape changes (black outlines) are scaled to the maximum value observed across the sample on each respective PC. PC1 describes a change in head size, PC2 describes dorso-ventral arching of the body and PC3 describes changes in overall robustness and body depth
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.