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Supplementary files for the manuscript "Assembly of Long Error-Prone Reads Using Repeat Graphs"
<p>Supplementary files for the manuscript "Assembly of Long Error-Prone Reads Using Repeat Graphs"</p> <p> </p> <p>Contents<br> ---------</p> <p>* `human_assemblues` - Flye assemblies of the human ONT sequencing data + QUAST benchmarking<br> of Flye, Canu and MaSuRCA assemblies. Scripts for assembly graph analysis are also included.</p> <p>* `nctc_assemblis` - Flye assemblies of the NCTC 21 bacterial dataset.</p> <p>* `yeast_assemblies` - working directories Flye, Canu, Falcon, Hinge and Miniasm assemblies of <br> yeast PB and ONT datasets + final assemblies + quast report. Some large files <br> (such as read alignments) were deleted.</p> <p>* `worm_assemblies` - working directories Flye, Canu, Falcon, Hinge and Miniasm assemblies of <br> the c. elegans dataset + final assemblies + quast report. Some large files <br> (such as read alignments) were deleted. `tandem_misassemblies` directory contain<br> the detailed analysis of nine tandem misassemblies. We recommend "gepard" dot-plotter for visualization.</p> <p>* `metagenome_assemblies` - Flye and Canu assemblies of a PacBio mock metagenome dataset.<br> In addition to metagenome assemblies, each bacteria was reassembled separately to<br> estimate the rate of divergence between the target genomes and the available references.</p> <p>* `simulated_data` - two assemblies of the simulated data illustrating Figure 1 (from Appendix I),<br> as well as simulated unbridged repeats benchmark.</p> <p><br> Software versions and parameters<br> --------------------------------</p> <p>* Flye - 2.3.5 (commit 20afeda)<br> * Canu - 1.7.1 (commit dfa60b8)<br> * Falcon - 0.3.0 (FALCON-Integrate commit 7498ef9)<br> * HINGE - 0.5.0 (commit 79fdf66)<br> * Miniasm - 0.2-r168-dirty (commit 40ec280) / Minimap2 2.8-r711-dirty (commit 8fc5f8d)<br> * Quast - 5.0.0 (commit de6973bb)</p> <p>Flye and Canu were run with the default parameters. The config files / scripts for<br> Falcon, HINGE and Miniasm could be found in the 'asm_config' archive folder.</p> <p>The HUMAN (but not the HUMAN+) assembly was generated with the earlier <br> Flye version 2.3.2 (released on Feb 20 2018) to provide a fair comparison <br> with the Canu and MaSuRCA assemblies (which were not updated since the release of Flye 2.3.2).<br> We note that the HUMAN assembly using the latest Flye version 2.3.5 has <br> NGA50 = 7.3 Mb and improves over the Flye 2.3.2 assembly (NGA50 = 6.3Mb). <br> HUMAN+ was assembled using the latest Flye and Canu versions (as of September 2018).</p> <p>The code for unbridged repeat resolution is currently available <br> in a separate 'flye-trestle' branch (commit 6100d32)</p>
Code and data archive for Nettle et al. 'Consequences of measurement error in qPCR telomere data: A simulation study'
<p>Code and data for Nettle et al. 'Consequences of measurement error in qPCR telomere data: A simulation study'</p> <p>Updated version of March 2019</p> <p>Main simulation functions are contained in the script ‘simulation.functions.r’. When called, these functions (listed below) return datasets with requested properties containing both the ideal values of the quantities (Cqs, TS, etc.), and their post-error measured values. This allows the user to determine the differences between ideal and measured values, and perform other analyses. All simulation parameter values are user-specifiable. The script ‘paper.results.r’ reproduces all the figures and simulation results from the main paper. 'paper.results.r' also reads in the two .csv files of empirical data (dataset1 and dataset2).</p> <p>Datasets consist of observations from <em>n</em> individuals. The steps common to all of the simulation functions are as follows:</p> <ul> <li>A vector of <em>n </em>true single copy gene abundances, <em>true.dna.scg</em> is defined, drawn from a normal distribution with mean <em>b</em> and standard deviation <em>var.sample.size</em> (<em>b </em>is a constant).</li> <li>A vector of <em>n </em>relative telomere lengths, <em>true.telo.var</em> is defined, drawn from a normal distribution with mean 1 and standard deviation <em>telomere.var.</em></li> <li>Hence, the true abundance of the telomere sequence is defined, as <em>a*true.dna.scg*true.telo.var</em>. Here, <em>a</em> is a scaling constant representing how many copies of the telomeric sequence there are per single copy gene in the average sample.</li> <li>Ideal Cq values for both reactions are defined as <em>f – log<sub>2</sub>(true.dna.scg)</em> and <em>f – log<sub>2</sub>(true.dna.telo),</em> where <em>f</em> is a constant representing the chosen fluorescence threshold.</li> <li>Measurement errors in the Cqs are generated from a normal distribution with mean 0; standard deviations given by <em>error.scg</em> and <em>error.telo</em>; and a correlation between <em>error.scg</em> and <em>error.telo</em> given by <em>error.cor</em>.</li> <li>Hence, measured Cqs are generated, which can be compared to the ideal Cq values.</li> <li>TS ratios are calculated both on the measured Cqs, and the ideal ones.</li> </ul> <p>The following functions are available. Specify desired parameter values in the parenthesis, e.g. <em>generate.one.dataset(n=10000, error.telo=0.1, error.scg=0.1, error.cor=0</em>). Default values in the simulation functions are generally those given in table 1 of the main paper.</p> <ul> <li><em>generate.one.dataset()</em> returns a simple dataset (one telomere measurement per individual) for chosen values of all the variables described in section 1. As well as ideal and measured Cqs, it returns ideal and measured TS ratios. It also returns the difference between the ideal and measured TS ratio, calculated two ways, computed (<em>error.computed</em>), and using equation (11) of online supplement 1 (<em>error.analytic</em>). Both methods produce the same number. This was included as an additional check of correctness of the simulation.</li> <li><em>generate.repeated.measure()</em> returns a dataset where telomere lengths from the same individuals are measured twice, via two independent biological samples, and the true telomere length of each individual is assumed not to have changed at all. The data frame it returns is as for <em>generate.one.dataset()</em>, except that there are two of each variable (e.g. <em>true.ts.1, true.ts.2, measured.ts.1, measured.ts.2</em>, etc.).</li> <li><em>calculate.repeatability()</em> calculates the repeatability of the measured T/S ratio (intra-class correlation coefficient) when <em>generate.repeated.measure()</em> is implemented using the given values for all the parameters. It requires prior installation of R package ‘irr’.</li> <li><em>compare.repeatability()</em> returns the repeatability of the T/S ratio and the repeatability calculated on the raw Cq for the telomere reaction, for the given parameter values.</li> </ul> <p> </p>
Citation count error data for "Data inaccuracy quantification and uncertainty propagation for bibliometric indicators"
<p>This is the original collected data on citation count errors resulting from citation matching errors in Web of Science data for the publication "Data inaccuracy quantification and uncertainty propagation for<br>bibliometric indicators". The first column, <code>CITCOUNT_ALL</code>, gives the total (corrected) citation count for a publication, which is the citation count according to WoS plus the additionally manually identified citations (missed by WoS's algorithm). The second column, <code>CITCOUNT_WOS</code>, is the WoS citation count. The numeric difference between the two column values in one row is the number of additionally manually identified citations.</p>
Linked collectors and determiners for: Correction of collecting number errors in the protologues of sixty-four taxon names from China.
Natural history specimen data linked to collectors and determiners held within, "Correction of collecting number errors in the protologues of sixty-four taxon names from China". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/057ddfc8-53dc-4dc3-89ce-058fcc84ff71">https://bionomia.net/dataset/057ddfc8-53dc-4dc3-89ce-058fcc84ff71</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/057ddfc8-53dc-4dc3-89ce-058fcc84ff71">https://gbif.org/dataset/057ddfc8-53dc-4dc3-89ce-058fcc84ff71</a>. Formatted as a Frictionless Data package.
Relative Random Errors in the Convective Atmospheric Boundary Layer Estimated by the Relaxed Filtering Method from Large Eddy Simulations
<p>Data supporting the paper "How representative are uncrewed aircraft system measurements of the convective boundary layer?" by Brian R. Greene, Leia M. Otterstatter, and Scott T. Salesky, submitted to Geophysical Research Letters in 2024. Data are postprocessed from large-eddy simulations of the convective atmospheric boundary layer that are used to produce the figures within the paper. Details on the production of these files are included in the supplementary informatin of this paper.</p>
Gradual Pressure Error in an HPLC-HRMS Measurement Run
<p>This dataset contains files of a reverse-phase HPLC-HRMS orbitrap measurement run during which the pump pressure gradually got more unstable. While a run was being recorded, the pump for the organic eluent ceased functioning slowly. This resulted in a high amount of new signals appearing in the form of "stripes" at specific retention times.</p> <p>It is intended as a control group for assigned data quality scores, with the reasoning that a highly unstable pressure in the chromatography will lead to more purely noise-induced signals. The relevant pressure profiles are also included. The mzML files were produced from the raw data using MSconvert and only contain the MS1 spectra.</p> <p>The last file in the series (<a href="https://zenodo.org/api/records/14046512/draft/files/20240805_AA_DK_Ibu_pH6_1min_20240808161549.raw/content" target="_blank" rel="noopener noreferrer">20240805_AA_DK_Ibu_pH6_1min_20240808161549</a>) remains incomplete since the instrument shut off halfway through the measurement.</p>
Relevance of predictive and postdictive error information in the course of motor learning.
<p>Dataset associated with the following publication:</p> <p>Maurer, LK, Joch, M, Hegele, M, Maurer, H, & Müller, H (2021). Relevance of predictive and postdictive error information in the course of motor learning. Neuroscience doi:10.1016/j.neuroscience.2021.05.007</p>
Tracking Data I/II of the publication "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"
<p>DATA belonging to the paper<br> "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"<br> Katharina Löffler, Tim Scherr, Ralf Mikut<br> doi: https://doi.org/10.1101/2021.03.16.435631</p> <p>-----------------------------</p> <p>To investigate the influence of different segmentation errors on the tracking performance we simulate errorneous segmentation data:<br> - under-segmentation (referred to as "merge" in the folders), over-segmentation("split"), False Negatives ("remove"), combination of the aforementioned errors ("mixed")<br> - percentages: 1,2,5,10,20 of errorneous masks per dataset<br> - runs: 5 randomly initialized runs per combination<br> - datasets: Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ each with two image sequences<br> ---> in total 4 (error types) * 5 (percentage) * 5 (runs) * 2 (data sets) * 2 (image sequences) = 400 datasets</p> <p>The datasets can be recreated by running our code https://git.scc.kit.edu/KIT-Sch-GE/2021-cell-tracking<br> ----------------------------</p> <p>RESULTS<br> We evuated the four tracking algorithms KIT-Sch-GE(1), KTH-SE, MU-Lux-CZ and our proposed algorithm on the aforementioned datasets and compare their performance using the CTC metrics DET, SEG and TRA.<br> This repository contains all metrics as xls files and all tracking results as image sequences.</p> <p><br> <strong>xls files</strong><br> -----------<br> compare_all_trackers_on_synt_bm.csv<br> Comparing the tracking algorithms MU-Lux-CZ, KTH-SE, KIT-Sch-GE(1) and the proposed tracking algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig8 and Fig9 and Supplementary Figures 3 and 4 are created from this data)</p> <p><br> compare_postprocessing_on_synth_bm.csv<br> Comparing the different post-processing strategies of the proposed tracking algorithm algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig7 and Fig8 and Supplementary Figures 1 and 2 are created from this data)</p> <p><strong>PLEASE NOTE: the folder compare_postprocessing_synth_bm is provided in the repository 10.5281/zenodo.5227610 due to size restrictions.</strong></p> <p><strong>folders </strong>(decompressed approximately 90GB of data!)<br> -----------<br> tracking_data<br> compare_all_synth_bm<br> Contains all tracking results for each tracking algorithm on the synthetically degraded datasets ()</p> <p> compare_all_synth_bm_no_error<br> Contains the tracking results for each tracking algorithm provided with the perfect ground truth segmentation data</p> <p> compare_postprocessing_synth_bm [<strong>will be stored in 10.5281/zenodo.5227610 due to size restrictions</strong>]<br> Contains all tracking resuls for each postprocessing configuration of the proposed cell tracking algorithm<br> the leaf folders are names run_xPOSTPROCESSING where x is the run number and POSTPROCESSING the postprocessing key<br> Postprocessing keys: ("no untangle" or "no masks" is indicated by an overline in the paper)<br> all ("untangle + masks" in the paper)<br> nd ("no untangle + masks")<br> nd_ns-l ("no untangle + no masks")<br> ns-l ("untangle + no masks")</p>
Data used in "Qubit Readout Error Mitigation with Bit-flip Averaging"
<p>This data supports "Qubit Readout Error Mitigation with Bit-flip Averaging" by Alistair Smith, Kiran Khosla, Chris Self, and M. S. Kim (arXiv:2106.05800). </p> <p>Included are the measurement results obtained from 1-8 qubit calibration measurements on the <em>ibmq_manhattan</em> device (on 1st Dec 2020), which were used to define the "exact" device response matrices used in the paper. Also included are the results of the numerical simulations used to generate the data for figures 3, 4, and 5.</p>
Using unoccupied aerial vehicles to estimate availability and group size error for aerial surveys of coastal dolphins
<p><span>Aerial surveys are frequently used to estimate the abundance of marine mammals, but their accuracy is dependent upon obtaining a measure of the availability of animals for visual detection. Existing methods for characterizing availability have limitations and do not necessarily reflect true availability. Here, we present a method of using small, vessel‐launched, multi‐rotor Unoccupied Aerial Vehicles (UAVs or drones) to collect video of dolphins to characterize availability and investigate errors surrounding group size estimates. We collected over 20 h of aerial video of dive‐surfacing behaviour across 32 encounters with the Australian humpback dolphin </span><em><span>Sousa sahulensis</span></em><span> off north‐western Australia. Mean surfacing and dive periods were 7.85 sec (</span><span>se</span><span> = 0.26) and 39.27 sec (</span><span>se</span><span> = 1.31) respectively. Dolphin encounters were split into 56 focal follows of consistent group composition to which example approaches to estimating availability were applied. Non‐instantaneous availability estimates, assuming a 7-sec observation window, ranged between 0.22 and 0.88, with a mean availability of 0.46 (CV = 0.34). Availability tended to increase with increasing group size. We found a downward bias in group size estimation, with true group size typically one individual more than would have been estimated by a human observer during a standard aerial survey. The variability of availability estimates between focal follows highlights the importance of sampling across a variety of group sizes, compositions, and environmental conditions. Through data re‐sampling exercises, we explored the influence of sample size on availability estimates and their precision, with results providing an indication of target sample sizes to minimize bias in future research. We show that UAVs can provide an effective and relatively inexpensive method of characterizing dolphin availability with several advantages over existing approaches. The example estimates obtained for humpback dolphins are within the range of values obtained for other shallow‐water, small cetaceans, and will directly inform a government‐run program of aerial surveys in the region.</span></p>
Data for "Understanding density driven errors via reaction barrier heights"
<p>Contains gzipped tarballs for BH76 data, inputs, and outputs computed with PySCF and the UTEP-NRLMOL code.</p> <p>Contents:</p> <ul> <li>r2SCAN_BH76_FODs.tar.gz : all relaxed Fermi orbital descriptors (FODs) computed self-consistently for r<sup>2</sup>SCAN-FLOSIC <ul> <li>All entries are space-separated</li> <li>The first line contains the number of up-spin FODs followed by the number of down-spin FODs</li> <li>The first three values in each ensuing line are the x, y, and z coordinates of the FOD</li> </ul> </li> <li>results_aug-cc-pvqz.tar.gz : all PySCF inputs/outputs for BH76 computed using the aug-cc-pVQZ basis set (spherical representation) <ul> <li>DFAs = HF/EXOA, LSDA, PBE, BLYP, SCAN, r<sup>2</sup>SCAN, M06-L, MN15-L, B3LYP, LC-wPBE</li> <li>And DFA @ DFA2, where DFA2 = HF, LSDA, PBE, LC-wPBE</li> </ul> </li> <li>results_def2-QZVP.tar.gz : all PySCF inputs/outputs for BH76 computed using the def2-QZVP basis set (spherical representation) <ul> <li>DFAs = HF/EXOA, LSDA, PBE, SCAN, r<sup>2</sup>SCAN, LC-wPBE</li> <li>And DFA @ DFA2, where DFA2 = HF, LC-wPBE</li> </ul> </li> <li>results_NRLMOL_cart.tar.gz : all PySCF inputs/outputs for BH76 computed using the default NRLMOL (density-functional optimized or DFO) basis set (Cartesian representation) <ul> <li>DFAs = HF/EXOA, LSDA, PBE, SCAN, r<sup>2</sup>SCAN, LC-wPBE</li> <li>And DFA @ DFA2, where DFA2 = HF, LC-wPBE</li> </ul> </li> <li>FLOSIC.tar.gz : data computed using the UTEP-NRLMOL code (NRLMOL basis set, Cartesian representation) <ul> <li>DFAs = LSDA, PBE, SCAN, r<sup>2</sup>SCAN</li> <li>And DFA@SCAN-FLOSIC</li> </ul> </li> <li>DC_DFT_processed_data.tar.gz : processed density-driven and functional-driven errors, and density sensitivity data</li> <li>r2SCAN_hybids.tar.gz : self-consistent r2 SCAN hybrid calculations (R2X-<em>x</em>) and DFA@R2X-<em>x</em> calculations <ul> <li>All PySCF input/output, aug-cc-pVQZ basis (spherical representation)</li> <li><em>x </em>= 0, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.75, 0.8, 0.9, 1.0</li> <li>DFA = LSDA, PBE, BLYP, SCAN, r2SCAN, B3LYP, LC-wPBE</li> </ul> </li> <li>SCAN_hybids.tar.gz : self-consistent SCAN hybrid calculations (SX-<em>x</em>) and DFA@SX-<em>x</em> calculations <ul> <li>All PySCF input/output, aug-cc-pVQZ basis (spherical representation)</li> <li><em>x </em>= 0, 0.1, 0.25, 0.5, 0.75, 1.0</li> <li>DFA = LSDA, PBE, SCAN, r2SCAN, LC-wPBE</li> </ul> </li> </ul>
Dataset for: "Quantification of Geometric Errors Made Simple: Application to Main-Group Molecular Structures"
<p>This dataset contains geometric energy offset (GEO') values for a set of density functional theory (DFT) methods for the B2se set of molecular structures. The data was generated as part of a research project aimed at quantifying geometric errors in main-group molecular structures. The dataset is in XLSX format created with MS Excel (version 16.69), and contains multiple worksheets with GEO' values for different basis sets and DFT methods. The worksheet headings, such as "AVQZ AVTZ AVDZ VQZ VTZ VDZ" represent different basis sets of Dunning theory, and the naming convention "(A)VnZ = aug-cc-pVnZ" is being used to label the worksheets. The data is organized in columns, with the first column providing the molecular ID and the names of the DFT methods specified in the first row of each worksheet. The molecular structures corresponding to each of these IDs can be found in Figure S1 of the supplementary information of the underlying publication [<a href="https://pubs.acs.org/doi/suppl/10.1021/acs.jpca.1c10688/suppl_file/jp1c10688_si_001.pdf">https://pubs.acs.org/doi/suppl/10.1021/acs.jpca.1c10688/suppl_file/jp1c10688_si_001.pdf</a>]. The data have been generated from quantum-chemical calculations from the G16 and ORCA 5.0.0 packages, with further computational details, methodology, and data validation strategies (e.g., comparisons with higher-level quantum-chemical calculations) given in the supplementary information of the underlying publication [<em>J. Phys. Chem. A</em> 2022, 126, 7, 1300–1311] and its supporting information [<a href="https://pubs.acs.org/doi/suppl/10.1021/acs.jpca.1c10688/suppl_file/jp1c10688_si_001.pdf">https://pubs.acs.org/doi/suppl/10.1021/acs.jpca.1c10688/suppl_file/jp1c10688_si_001.pdf</a>].<br> The dataset is expected to be useful to researchers in the field of computational chemistry and materials science. All values are given in kcal/mol. The data is generated by the authors of the underlying publication and it is shared under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. The data is expected to be re-usable and the quality of the data is assured by the authors. The size of the data is 71 KB.</p>
Dataset for a reduced self-calibrating method for rotary table error motions
<p><strong>Ball plate measurement dataset</strong></p> <p>Two datasets from the measurements of a ball plate for the application of a novel method for the determination of rotary table errors in six degrees of freedom on coordinate measuring machines are provided. The method is based on the measurement of an uncalibrated circular ball plate in different rotational positions on the rotary table, also referred to as the three-rosette method. The novel procedure allows to reduce the number of measurement positions significantly compared to the complete three-rosette method whereas the measurement uncertainty still remains sufficiently small. The method relies on error separation and is self-calibrating which means that no external reference is required. The sophisticated design of a new ball plate allows to determine the rotary table errors in angular steps of only 5°.</p> <p>Provided are two datasets:</p> <ul> <li>One dataset of a measurement of a ball plate with 12 equidistant balls for the detection of the rotary table errors in steps of 30 degrees, using the complete three-rosette method.</li> <li>One dataset of a measurement of a ball plate with 12 non-equidistant balls for the detection of the rotary table errors in steps of 5 degrees, using the new reduced three-rosette method.</li> </ul> <p> </p> <p><strong>Acknowledgement</strong></p> <p>This dataset was obtained within the 19ENG07 Met4Wind project. This project has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme.</p> <p>Project title: Metrology for enhanced reliability and efficiency of wind energy systems<br> Funder name: European Commission<br> Funder ID: 10.13039/100014132<br> Grant number: 19ENG07 Met4Wind<br> Link to project homepage: <a href="https://www.ptb.de/empir2020/met4wind/home/">https://www.ptb.de/empir2020/met4wind/home/</a></p>
SCA-2023: A two-part dataset for benchmarking the methods of image precompensation for users with refractive errors
<p>The recent practices of demonstrating various static and video images to users by means of digital, processor-controlled, often self-luminous devices (computer monitors, smartphone and tablet screens, etc.) have spurred the development of various methods for improving the perception of such images through their computer processing. In particular, this applies to the task of precompensating images shown to users with various anomalies of refraction of the eyes (e.g. myopia or astigmatism) in situations where they are not equipped with glasses or other corrective devices. Researchers have proposed a considerable number of such precompensation methods, but to this day there has been no way to accurately compare their quality. We propose an original dataset, which we called “SCA-2023”, of images specially designed for this purpose. Its most important feature is the fact that it includes not only a set of ground-truth images for implementing the precompensation transform, but also a separate set of images characterizing specific types and degrees of manifestation of the refractive errors. The benchmarking procedure itself includes applying the precompensation transformation to a certain image from the first part of the dataset, computer simulation of the so-called retinal image (distribution of light on the retina of an imaginary observer) based on the selection of the “distorting eye” from the second part of the dataset, and evaluating the similarity of this image to the ground-truth image, using any of the commonly used similarity metrics for this purpose.</p>
Annotated Video Dataset of Fencing Movements and Corresponding Error Patterns
<p>Video dataset containing 1289 videos and their augmentation of four fencing movements, performed by a variety of fencers. The main actions included are counterattack, lunge, flèche and parry. For each movement samples with typical error patterns are included and annotated. Additionally labels for mulit-labelling are documented. The corresponding paper "Mastering Fencing Techniques with Machine Learning: A Video-Based Classification and Correction System" is published at the 10th IEEE Swiss Conference on Data Science (SDS 2023)</p>
Reactive Correction of Object Placement Errors for Robotic Arrangement Tasks (Video)
<p>Supplementary video for the paper "Reactive Correction of Object Placement Errors for Robotic Arrangement Tasks".</p>
Supplements for "Solvent Accessibility Promotes Rotamer Errors During Protein Modelling with Major Side-Chain Prediction Programs"
<p><strong>Supplements for "Solvent Accessibility Promotes Rotamer Errors During Protein Modelling with Major Side-Chain Prediction Programs"</strong></p> <p>This supplement includes the following files:</p> <ul> <li>Main_v02.R --- Script in R language to process files (in "PDBs.zip") and produce the dataset ("Dataset_v02.csv")</li> <li>PDBs.zip --- PDB files include filtered structures processed by three programs</li> <li>Dataset_v02.csv --- final filtered version of dataset produced in R language (by "Main_v02.R"). </li> <li>Dataset key.txt --- key to column names in Dataset_v02.csv </li> </ul> <p>The article featuring this dataset is published in:</p> <p>Journal: Journal of Chemical Information and Modeling<br>Title: "Solvent Accessibility Promotes Rotamer Errors During Protein Modelling with Major Side-Chain Prediction Programs"<br>Author(s): Hameduh, Tareq; Mokry, Michal ; Miller, Andrew ; Heger, Zbynek; Haddad, Yazan</p> <p><a href="https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fpubs.acs.org%2Fdoi%2F10.1021%2Facs.jcim.3c00134&data=05%7C01%7C%7C4876a5c33e5f4bd8daf008db7e55a28d%7C84df9e7fe9f640afb435aaaaaaaaaaaa%7C1%7C0%7C638242678496577396%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=NE%2F0dYvc4m5%2B8T0YQtqxY21yk1pA9ZpiADqzx3vEJ4Q%3D&reserved=0">https://pubs.acs.org/doi/10.1021/acs.jcim.3c00134</a></p> <p> </p>
The locus coeruleus broadcasts prediction errors across the cortex to promote sensorimotor plasticity
<p>Raw data and code to generate the figures of the publication "The locus coeruleus broadcasts prediction errors across the cortex to promote sensorimotor plasticity" <a href="https://doi.org/10.7554/eLife.85111.2">https://doi.org/10.7554/eLife.85111</a>. </p>
Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses
<p>Raw data and code to reproduce figures in the manuscript "Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses"</p> <p># README</p> <p>## Introduction</p> <p>This README provides essential information about the codebase for the manuscript titled "Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses." The code in this repository is self-contained and is expected to run smoothly given the appropriate versions of the required libraries/packages.</p> <p>## Directory structure and execution details</p> <p>### R code</p> <p>- Main Figures 2A-2D, 3A-3C, and 4A-4E, as well as supplemental figures S2A-S2H, S3A-S3E, S4L, and S5A-S5I, were generated using R. Execute the `R_figs_master.r` script located in the `r_code` directory.<br> - All figures will be saved within the `r_code/code_generated_figures` directory.<br> - Note: Exact UMAP representations might vary across different hardware and operating systems, likely due to an issue with the UWOT package ([Reference Issue](https://github.com/satijalab/seurat/issues/5514)). If figures appear outside their designated plot ranges, set "FixAxes" to 'FALSE' in the `single_cell_variables.r` script.</p> <p>### MATLAB code</p> <p>- Main figures 1B, 1D-1F, and 6A-6H, as well as supplemental figures S1A-S1J and S6A-S6I, were generated using MATLAB (version 9.11.0.1809720 (R2021b) Update 1). Execute the `get_the_figs_matlab.m` script located in the `matlab_code` directory.<br> - All figures will be saved within the `matlab_code/code_generated_figures` directory.<br> - Required: [fca_readfcs, version 2020.06.22](https://ch.mathworks.com/matlabcentral/fileexchange/9608-fca_readfcs).</p> <p>### Python code</p> <p>- Figures 5B-5F panels were generated using Python (version 3.6.8). Run the `fig_5_analysis_code.py` script located in the `python_code` directory.<br> - All figures will be saved within the `python_code/code_generated_figures` directory.<br> - The preprocessed images located in `python_code/data_repository/Adamts2_processed`, `python_code/data_repository/Agmat_processed`, and `python_code/data_repository/Baz1a_processed` were generated using the ImageJ macro `python_code/cropped_to_processed_macro.ijm` from the raw images in `python_code/data_repository/Adamts2_cropped`, `python_code/data_repository/Agmat_cropped`, and `python_code/data_repository/Baz1a_cropped`.</p> <p>## Supplementary code (for reference only as raw data is not included)</p> <p>### Mapping code and genome construction code</p> <p>- Initial processing of Single-cell RNA-sequencing was performed with Cell Ranger, coordinated by the Python script:<br> `python_code/mapping_and_genome_construction/single_cell_mapping_pipeline.py`. Some components of this script are deprecated and were primarily used to pass .fastq files to Cell Ranger and organize the outputs.<br> - A custom genome was constructed to account for the expression of CaMPARI2 in the single-cell RNA-sequencing dataset:<br> `python_code/mapping_and_genome_construction/campari2_genome_construction.py`.<br> - Processing of Bulk RNA-sequencing, either single or paired-end, was executed through Python:<br> `python_code/mapping_and_genome_construction/bulk_single_end_mapping.py` and `python_code/mapping_and_genome_construction/bulk_paired_end_mapping.py`.<br> - A custom genome was constructed to account for the expression of various artificial promoter viruses:<br> `python_code/mapping_and_genome_construction/bulk_seq_genome_construction.py`.</p>
Standard Error Estimates from ARRI ensemble GAM model outputs
<p><strong>Summary (Purpose)</strong></p> <p>Basal area per acre (BAA) standard error estimate (SEE) for all trees, pine trees, and non-pine trees across three diameter at breast height (DBH) size classes, 2- to 10-inch, 10- to 14-inch, and 14+ inch. Models were informed by relative density rasters from 2018 Light Detection and Ranging (lidar) point clouds.</p> <p><strong>Description</strong></p> <p>The GAM_SEE_rasters are modelled basal area per acre standard error estimate single band rasters. The units for the rasters’ are square foot per acre. Rasters are divided into three tree species groups: 'All' trees, 'Pine' trees (defined as trees of the genus<em> Pinus)</em>, and 'No-Pine' trees, and three size classes: LT 10 for trees with DBH between 2- and 10-inches, 10-14 for trees with DBH between 10- and 14- inch DBH, and GT 14 for trees with DBH greater than 14-inches.</p> <p>Ensemble generalized additive models (GAM) of estimated tree basal area per DBH class were created from Restore field plots and relative density canopy cover rasters, or RDCC (St. Peter, et al., 2023). This ensemble GAM model was created using the R script detailed in Hogland, 2021. The parameters used were 0.75 for the percent of data used to train the model (selected using random sampling with replacement), 50 models, and using gaussian family. The estimated BAA for each of the 50 ensemble GAM models for each cell were averaged (mean) to produce the GAM estimate, additionally the variability between these estimates was used to create the standard error estimate (SEE) for each cell. </p> <p> </p> <p>The 246 Restore field plots used to train the GAM model of basal area include measurements of all trees within four non-overlapping 9m radius circular subplots within a 36m square plot. Tree diameter at breast height (DBH), species, count, and condition measurements were recorded. Measurements were summarized to the plot and DBH (square inches) was converted to basal area per acre (square feet per acre) using the formula 0.005454 * DBH^2. Restore field plots were measured in the Spring of 2018. The RDCC metrics are 5m resolution multiband rasters produced by applying a custom r software function that uses the r software’s ‘lidR’ package to produce forest metrics summarized from Light Imaging Detection and Ranging (LiDAR) point clouds. ARSA LiDAR is a combination of three collections, Block 2 and 3 were collected in early 2018 and has a NPS of 0.7-m using a Riegl VQ-1560i lidar system. Leon county LiDAR data has a nominal pulse spacing (NPS) of 0.35-m and was acquired between February 05, 2018 and April 25, 2018 using the Leica ALS80 HP SN8137 and SN8235 lidar systems. Choctawhatchee data was acquired in early 2017, using the Riegl LMS-Q1560 lidar system and has a NPS of 0.7-m. Rasters were generated in their vendor provided spatial projection before being reprojected to UTM Zone 16.</p> <p> </p> <p>The 5m resolution RDCC bands were summarized to 40x40m to correspond with the area of our plots and used as predictor variables in the models of all trees basal area. Each 5m pixel value represents the estimated standard error of the basal area per acre estimate as if it was the center of a 40x40m (8x8 cells) plot surrounding that pixel. </p> <p> </p> <p>Reference:</p> <p>Hogland, J. (2021). Ensemble Generalized Additive Models (EGAM). Retrieved from Jupyter Notebook: <a href="https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing">https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing</a></p> <p>St. Peter, Joseph, Drake, Jason, Medley, Paul, & Ibeanusi, Victor. (2023). Relative Density Canopy Cover Outputs for Leon Lidar data in the Florida Panhandle 2018 [Data set]. In Remote Sensing (Vol. 13, Number 23, p. 4763). Zenodo. https://doi.org/10.5281/zenodo.8222114</p> <p><strong>Credits</strong></p> <p>This dataset was built by Joseph St. Peter of FAMU’s Center for Spatial Ecology and Restoration using 2018 LiDAR data funded by Leon County, Northwest Florida Water Management District, US Geological Survey and the USDA Forest Service and processed using the r package lidR. Restore plots were funded by the Gulf Coast Ecosystem Restoration Council (RESTORE Council) through an interagency agreement with the USDA Forest Service (17-IA-11083150-001) for the Apalachicola Tate’s Hell Strategy 1 project.</p> <p><strong>Use Limitations</strong></p> <p>This spatial data is based on various data collection and processing techniques as well as on modeling or interpretation. While this data uses the most current and complete information available at the time of production, spatial data and derivative products may vary in accuracy. Spatial data are often developed from sources of differing accuracy which may be accurate only at certain scales. This data has been quality checked but may contain spurious errors or be incomplete or inappropriate for certain uses. Spatial data products used for purposes other than those for which they were created, may yield inaccurate or misleading results. The USDA Forest Service, Florida A&M University, and the Center for Spatial Ecology & Restoration (CSER) reserves the right to correct, update, modify, remove or replace GIS products at any time and without notification. This data may not be distributed without written permission from the Center for Spatial Ecology & Restoration (CSER) at Florida A&M University, and/or the USDA Forest Service.</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.