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829 results for “evolvability”

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zenodo52/100

Data archive for "Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network"

This datasets supports the paper "Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network" submitted to IEEE Transactions in Geoscience and Remote Sensing. A preprint of the paper can be found here: <a href="https://arxiv.org/abs/2005.10374">https://arxiv.org/abs/2005.10374</a>. The code that uses these data is available at <a href="https://github.com/jleinonen/downscaling-rnn-gan">https://github.com/jleinonen/downscaling-rnn-gan</a>. The file "goes-samples-2019-128x128.nc" contains the training dataset called "GOES-COT" in the paper, consisting of cloud optical depth measurements from the GOES-16 satellite. The files "gen_weights*.nc" contain the generator weights saved at different time steps during training for the two different datasets described in the paper.

opencc-by-4.0May 2020View details →

zenodo48/100

Lehigh University Benchmark (LUBM): Evolving Graph Simulation

The Lehigh University Benchmark (LUBM) generates benchmark datasets containing people working at universities [1]. We use the Data Generator v1.7 to generate 10 versions of a graph containing 100 universities [2]. Thus, all versions are of similar size, but we emulate modifications by generating different vertex identifiers, i.e., each version is considered a timestamped graph. Each graph contains about 2.1 M vertices and 13 M edges. Over all versions, the mean degree is 6.7 (+- 0.1), the mean in-degree is 6.8 (+- 0.1), and the mean out-degree is 5.1 (+- 0.1). 1. <a href="https://dblp.uni-trier.de/pid/80/5390.html">Yuanbo Guo</a>, <a href="https://dblp.uni-trier.de/pid/48/6834.html">Zhengxiang Pan</a>, <a href="https://dblp.uni-trier.de/pid/94/1154.html">Jeff Heflin</a>: LUBM: A benchmark for OWL knowledge base systems. <a href="https://dblp.uni-trier.de/db/journals/ws/ws3.html#GuoPH05">J. Web Semant. 3(2-3)</a>: 158-182 (2005) 2. <a href="https://dblp.uni-trier.de/pid/222/6353.html">Till Blume</a>, <a href="https://dblp.uni-trier.de/pid/r/DavidRicherby.html">David Richerby</a>, <a href="https://dblp.uni-trier.de/pid/06/2380.html">Ansgar Scherp</a>: Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs. <a href="https://dblp.uni-trier.de/db/conf/cikm/cikm2020.html#BlumeRS20">CIKM 2020</a>: 75-84

opencc-by-4.0Oct 2020View details →

zenodo48/100

Berlin Sparql Benchmark (BSBM): Evolving Graph Simulation

The Berlin SPARQL Benchmark (BSBM) is a suite of benchmarks built around an e-commerce use case [1]. We generated 21 versions of the dataset with different scale factors. The first dataset, with a scale factor of 100, contains about 7,000 vertices and 75,000 edges. We generated versions with scale factors between 2,000 and 40,000 in steps of 2,000. The largest dataset contains about 1.3 M vertices and 13 M edges. For our experiments in [2], we first use the different versions ordered from smallest to largest (version 0 to 20) to simulate a growing graph database. Subsequently, we reverse the order to emulate a shrinking graph database. Over all versions, the mean degree is 8.1 (+- 0.5), the mean in-degree is 4.6 (+- 0.3), and the mean out-degree is 9.8 (+- 0.2). 1. <a href="https://dblp.uni-trier.de/pid/b/ChristianBizer.html">Christian Bizer</a>, <a href="https://dblp.uni-trier.de/pid/47/7466.html">Andreas Schultz</a>: The Berlin SPARQL Benchmark. <a href="https://dblp.uni-trier.de/db/journals/ijswis/ijswis5.html#BizerS09">Int. J. Semantic Web Inf. Syst. 5(2)</a>: 1-24 (2009) 2. <a href="https://dblp.uni-trier.de/pid/222/6353.html">Till Blume</a>, <a href="https://dblp.uni-trier.de/pid/r/DavidRicherby.html">David Richerby</a>, <a href="https://dblp.uni-trier.de/pid/06/2380.html">Ansgar Scherp</a>: Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs. <a href="https://dblp.uni-trier.de/db/conf/cikm/cikm2020.html#BlumeRS20">CIKM 2020</a>: 75-84

opencc-by-4.0Oct 2020View details →

zenodo48/100

Reconfiguration Time Measurements for Evolving Hardware

The dataset contains the raw measurements of the time necessary to reconfigure Lattice iCE40 FPGAs. The main goal is to see the effect of two approaches of implementing the reconfiguration process in software for Evolvable Hardware. One approach is to use existing open-source software externally, the other is to integrate the functionality in the software for Evolvable Hardware. The conditions for the reconfiguration measurements were altered in four dimensions: <ol> <li>The FPGA board <ul> <li>iCEstick <ul> <li>Lattice iCE40 HX1K FPGA</li> <li>Sample bitstream created by <a href="https://github.com/evolvablehardware/BitstreamEvolution">Bitstream Evolution Software</a></li> <li>No direct reconfiguration possible</li> </ul> </li> <li>iCE40 breakout board (ICE40HX8K-B-EVN) <ul> <li>Lattice iCE40 HX8K FPGA</li> <li>Sample bitstream created by CoBEA</li> </ul> </li> </ul> </li> <li>Write Target <ul> <li>Flash <ul> <li>Bitstream first written to an external EEPROM, then pulled by the FPGA from the EEPROM</li> </ul> </li> <li>Direct <ul> <li>Bitstream written directly to the FPGA</li> </ul> </li> </ul> </li> <li>Reconfiguration approach <ul> <li>Icestorm <ul> <li>Use external tools from Project Icestorm</li> </ul> </li> <li>CoBEA <ul> <li>Use integrated function of CoBEA reconfiguration module</li> </ul> </li> </ul> </li> <li>Bitstream size <ul> <li>Full <ul> <li>No size reduction</li> </ul> </li> <li>Compact <ul> <li>Size reduced as much as allowed by write approach <ul> <li>Icestorm: skip BRAM</li> <li>CoBEA: skip BRAM, skip comment, full compaction (level 4)</li> </ul> </li> </ul> </li> </ul> </li> </ol>   This upload contains three groups of files: <ul> <li>2 bitstreams <ul> <li>One for each used FPGA board</li> </ul> </li> <li>12 measurements <ul> <li>Comma-separated values</li> <li>File name contains the contiditions of the measurement</li> <li>Only 12 out of 16 possible combinations of conditions as "iCEstick" cannot be combined with "Direct"</li> </ul> </li> <li>1 aggregated statistic <ul> <li>Comma-separated values</li> <li>Minimum, mean, maximum, mean-minimum, maximum-mean for every measurement</li> </ul> </li> </ul>

opencc-by-4.0Apr 2022View details →

zenodo48/100

Supplementary data for- Heat-evolved microalgae (Symbiodiniaceae) are stable symbionts and influence thermal tolerance of the sea anemone Exaiptasia diaphana

Raw data and R codes for - Heat-evolved microalgae (Symbiodiniaceae) are stable symbionts and influence thermal tolerance of the sea anemone Exaiptasia diaphana. DOI: 10.1111/1462-2920.70011

opencc-by-4.0Apr 2025View details →

zenodo48/100

HI line observations of 290 evolved stars made with the Nancay Radio Telescope - I. Data: online Tables

--- Table B.1: Clear NRT HI detections - basic data  Description of the columns: (1)  Name: common catalogue name of the target.    An ^n after a name indicates that it is clearly not an AGB star,    a ^d that we consider its classification as an AGB to be dubious,     and a ^* indicates that notes on the object can be found in Appendix A; (2,3) RA,DEC: literature right ascension and declination of the target from Gaia EDR 3,    for epoch J2000.0; (4) Type: target type.    Primarily the variability type as listed in Version 5.1 of the General Catalogue of Variable Stars,    GCVS (A description of GCVS types is given in https://cdsarc.u-strasbg.fr/ftp/cats/B/gcvs/vartype.txt),   but if an object is not included in the GCVS, other identifiers are listed in brackets:    HPM = high proper motion star, (OH/IR) = OH/IR maser,  LPVc = long-period variable candidate,    PN = planetary nebula, pPN = proto-planetary nebula, and post-AGB star; (5,6) Spec & ref: spectral type of the star, followed by its literature reference,    as retrieved from the SIMBAD database. If none was listed there, the reference is noted as 'SIMBAD'; (7,8) Teff & ref: effective temperature of the star, in K, followed by its literature reference;  (9) d: distance of the target, based on its parallax (mainly from the Gaia EDR3), in pc.   If no Gaia parallax was available a reference to the distance we adopted is given in Appendix A   (for RAFGL 3099, mu Cep, and V Peg); (10) Vlit: published radial velocity of the target in the LSR reference frame, in km/s; (11) Vexp: literature expansion velocity measured from CO or OH 1612 MHz line observations, in km/s.    If a pair of values was published for a two-velocity component CO line fit, the largest value is listed here; (12) ref: literature references to the published Vlit and Vexp values;  (13) line: spectral line on which the published radial velocity measurement (Vlit) was based; (14,15) Mdot & ref: literature mass loss rates, in solar masses per year,    followed by its literature reference. Notes to Table B.1: References: see Table B.1 in the Astronomy & Astrophysics paper. --- Table B.2: Clear NRT HI detections - HI data  Description of the columns: (1) Name: common catalogue name of the target.    A ^T after a name indicates that HI line parameters are based on a  'total' spectrum, whereas    a ^P indicates that a 'peak' spectrum was used.    An ^n indicates that it is clearly not an AGB star,    a ^d that we consider its classification as an AGB to be dubious,     and a ^* indicates that notes on the object can be found in Appendix A; (2) VHI: our central radial velocity in the LSR reference frame of the Gaussian fitted    to the HI profile, in km/s. (3) FWHM: our full width half maximum of the Gaussian fitted to the HI line profile, in km/s; (4) Speak: our peak flux density of the HI line profile, in Jy; (5) diam: our estimated angular size of the HI CSE in the east-west direction, in arcmin; (6) FHI: our integrated line flux of the HI profile, in Jy km/s; (7) MHI: our total HI mass, in Msun; (8) HI ref: references to previously published HI studies,   see Table B.2 in the Astronomy & Astrophysics paper. --- Table B.3: Possible NRT HI detections - basic data Description of the columns: (1)  Name: common catalogue name of the target.    An ^n after a name indicates that it is clearly not an AGB star,    a ^d that we consider its classification as an AGB to be dubious,     and a ^* indicates that notes on the object can be found in Appendix A; (2,3) RA,DEC: literature right ascension and declination of the target from Gaia EDR 3,    for epoch J2000.0; (4) Type: target type.    Primarily the variability type as listed in Version 5.1 of the General Catalogue of Variable Stars,    GCVS (A description of GCVS types is given in https://cdsarc.u-strasbg.fr/ftp/cats/B/gcvs/vartype.txt),   but if an object is not included in the GCVS, other identifiers are listed in brackets:    HPM = high proper motion star, (OH/IR) = OH/IR maser,  LPVc = long-period variable candidate,    PN = planetary nebula, pPN = proto-planetary nebula, and post-AGB star; (5,6) Spec & ref: spectral type of the star, followed by its literature reference,    as retrieved from the SIMBAD database. If none was listed there, the reference is noted as 'SIMBAD'; (7,8) Teff & ref: effective temperature of the star, in K, followed by its literature reference;  (9) d: distance of the target, based on its parallax (mainly from the Gaia ED33), in pc.   If no Gaia parallax was available a reference to the distance we adopted is given in Appendix A  (for RAFGL 3099, mu Cep, and V Peg); (10) Vlit: published radial velocity of the target in the LSR reference frame, in km/s; (11) Vexp: literature expansion velocity measured from CO or OH 1612 MHz line observations, in km/s.    If a pair of values was published for a two-velocity component CO line fit, the largest value is listed here; (12) Mdot : literature mass loss rates, in solar masses per year,  (13) line: spectral line on which the published radial velocity measurement (Vlit) was based; (14) ref: literature references to the published Vlit, Vexp and Mdot values;  Notes to Table B.3: References: see Table B.1 in the Astronomy & Astrophysics paper. --- Table B.4: Possible NRT HI detections - HI data Description of the columns: See the description of the columns of Table B.2. --- Online only Table 5: Upper limits to NRT HI lines  Description of the columns: (1)  Name: common catalogue name of the target.    An ^n after a name indicates that it is clearly not an AGB star,    a ^d that we consider its classification as an AGB to be dubious,     and a ^* indicates that notes on the object can be found in Appendix A; (2,3) RA,DEC: literature right ascension and declination of the target from Gaia EDR 3,    for epoch J2000.0; (4) Type: target type.    Primarily the variability type as listed in Version 5.1 of the General Catalogue of Variable Stars,    GCVS (A description of GCVS types is given in https://cdsarc.u-strasbg.fr/ftp/cats/B/gcvs/vartype.txt),   but if an object is not included in the GCVS, other identifiers are listed in brackets:    HPM = high proper motion star, (OH/IR) = OH/IR maser,  LPVc = long-period variable candidate,    PN = planetary nebula, pPN = proto-planetary nebula, and post-AGB star; (5,6) spec & ref: spectral type of the star, followed by its literature reference,    as retrieved from the SIMBAD database. If none was listed there, the reference is noted as 'SIMBAD'; (7,8) Teff & ref: effective temperature of the star, in K, followed by its literature reference;  (9) d: distance of the target, based on its parallax (mainly from the Gaia EDR3, in pc.   If no Gaia parallax was available a reference to the distance we adopted is given in Appendix A  (for RAFGL 3099, mu Cep, and V Peg); (10) Vlit: published radial velocity of the target in the LSR reference frame, in km/s; (11) Vexp: literature expansion velocity measured from CO or OH 1612 MHz line observations, in km/s.    If a pair of values was published for a two-velocity component CO line fit, the largest value is listed here; (12) Mdot : literature mass loss rates, in solar masses per year,  (13) ref: literature references for Vlit, Vexp and Mdot, as applicable; (14) line: spectral line on which the published radial velocity measurement (Vlit) was based; (15) Speak: peak flux density of our HI line profile, in Jy; (16) notes: 'old data' indicates objects observed only in 1992/1993, before the  renovation of the NRT;    'blue/red side' indicates that either the blue or red side of the HI profile could be used to measure    an upper limit to the line flux; (17) HI ref: references to previously published HI studies; Notes to online only Table 5:  HI references: see Table B.2 in the Astronomy & Astrophysics paper. Other references: see Table B.1 in the Astronomy & Astrophysics paper. --- Online only Table 6: Confused NRT HI spectra Description of the columns: (1) Name: common catalogue name of the target.  (2,3) RA, DEC: literature right ascension and declination of the target from Gaia EDR3,    for epoch J2000.0;  (4) type: target type. Primarily the variability type as listed in Version 5.1 of the    General Catalogue of Variable Stars, GCVS; but if an object is not included in the GCVS,    other identifiers are listed in brackets:  HPM = high proper motion star, (OH/IR) = OH/IR maser,    LPVc = long-period variable candidate, PN = planetary nebula, pPN = proto-planetary nebula,    and post-AGB star;  (5,6) spec & ref spectral type of the star, followed by its literature reference,   as retrieved from the SIMBAD database. If none was listed there, the reference is noted as 'SIMBAD';  (7,8) Vlit & ref: published radial velocity of the target in the LSR reference frame, in km/s;  (9) line: spectral line on which the published radial velocity measurement (Vlit) was based;  (10) notes: 'old data' denotes objects observed only in 1992/1993, before the renovation of the NRT  (see Section 3), for which no observations in digital form are available;  (11) HI ref: references to previously published HI studies. Notes to online only Table 6: HI references: see Table B.2 in the Astronomy & Astrophysics paper. Other references: see Table B.1 in the Astronomy & Astrophysics paper.

opencc-by-4.0Nov 2024View details →

zenodo44/100

A global dataset of SST anomaly evolving processes retrieved from remote sensing products (GDSSTAEP V1.0)

The GDSSTAEP includes three datasets and two relationship files with a time range from January 1982 to December 2009. Three datasets formatted in SHP are a dataset of process object-oriented SSTA, named DSPOSSTA, storing SSTA process objects, a dataset of sequence object-oriented SSTA, named DSSOSSTA, storing SSTA sequence objects, and a dataset of variation object-oriented SSTA, named DSVOSSTA, storing SSTA variation objects, respectively. And two relationship files formatted in CSV store the evolving behaviors among sequence objects of SSTA and variation objects of SSTA, respectively.

opencc-by-4.0Sep 2020View details →

zenodo44/100

Data release for "OrchID: a Generalized Framework for Taxonomic Classification of Images Using Evolved Artificial Neural Networks"

Abstract Taxonomic expertise for the identification of species is rare and costly. On-going advances in computer vision and machine learning have led to the development of numerous semi- and fully automated species identification systems. However, these systems are rarely agnostic to specific morphology, rarely can perform taxonomic “approximation” (by which we mean partial identification at least to higher taxonomic level if not to species), and frequently rely on costly scientific imaging technologies. We present a generic, hierarchical identification system for automated taxonomic approximation of organisms from images. We assessed the effectiveness of this system using photographs of slipper orchids (Cypripedioideae), for which we implemented image pre-processing, segmentation, and colour and shape feature extraction algorithms to obtain digital phenotypes for 116 species. The identification system trained on these digital phenotypes uses a nested hierarchy of artificial neural networks for pattern recognition and automated classification that mirrors the Linnean taxonomy, such that user-submitted photos can be assigned a genus, section, and species classification by traversing this hierarchy. Performance of the identification system varied depending on photo quality, number of species included for training, and desired taxonomic level for identification. High quality photos were scarce for some taxa and were under-represented in the training set, resulting in imbalanced network training. The image features used for training were sufficient to reliably identify photos to the correct genus but less so to the correct section and species. The outcomes of this project include a library of feature extraction algorithms called ImgPheno, a collection of scripts for neural network training called NBClassify, a library for evolutionary optimization of artificial neural network construction called AI::FANN::Evolving and a planned web application called OrchID for identification of user-submitted images. All project outcomes are open source and freely available. About this release This release corresponds belongs with our response to the reviewers of PLoS One. At this stage of the review cycle the manuscript is assessed as 'minor revision'. Consequently, we don't anticipate making more releases until publication.

opencc-zeroOct 2015View details →

zenodo44/100

Phlorest phylogeny derived from Dunn et al. 2011 'Evolved structure of language shows lineage-specific trends in word-order universals'

Cite the source of the dataset as: <blockquote> Dunn M, Greenhill SJ, Levinson SC & Gray RD. 2011. Evolved structure of language shows lineage-specific trends in word-order universals. Nature, 473(7345), 79-82. </blockquote>

opencc-by-4.0Aug 2023View details →

zenodo44/100

Robust genetic codes enhance protein evolvability

The repository contains all data for our manuscript on protein evolvability under rewired genetic codes: https://www.biorxiv.org/content/10.1101/2023.06.20.545706v1 The corresponding code is available on GitHub: https://github.com/parizkh/rewired_codes_landscapes

opencc-by-4.0Feb 2024View details →

zenodo44/100

Original single session datasets from "Slowly evolving dopaminergic activity modulates the moment-to-moment probability of reward-related self-timed movements."

This archive contains the original single-session recording datasets associated with the paper "Slowly evolving dopaminergic activity modulates the moment-to-moment probability of reward-related self-timed movements" by Allison E Hamilos, Giulia Spedicato, Ye Hong, Fangmiao Sun, Yulong Li, and John A Assad (https://doi.org/10.1101/2020.05.13.094904). Files can be loaded and collated with code from our GitHub repository to reproduce all analyses (https://www.github.com/harvardschoolofmouse).

opencc-by-4.0May 2020View details →

zenodo44/100

Supporting Data for Figures in "Evolving Interior Mixing Regimes in a Tidal River Plume"

Supporting data for figures in "Evolving Interior Mixing Regimes in a Tidal River Plume" by Preston S. Spicer, Kimberly D. Huguenard, Kelly L. Cole, Daniel G. MacDonald, and Michael M. Whitney. The scientific journal article is published in Geophysical Research Letters (2022). The article elucidates the evolution of stratified shear mixing in a tidal river plume using observational data taken over an ebb pulse throughout the interior of the Merrimack River plume. The file GRL_figs.m is a MATLAB file which produces Figures 1 thru 4 in the article taking the accompanying .mat and .txt files as input. Variables names and units correspond to graphed data of each figure in the journal article.

opencc-by-4.0May 2022View details →

zenodo44/100

Tone Discriminator Evolved on iCE40 FPGA

Data created by an experiment that evolved a tone discriminator on an iCE40 FPGA. The experiment was originally conducted by Adrian Thompson on an Xilinx XC6200 FPGA in 1997. This is the reproduction on a modern FPGA.   The tone discriminator is a circuit on the FPGA that creates a 3.3 V output signal if presented with a 10 kHz square wave input and a 0 V output signal for a 1 kHz input signal. The circuit was evolved with a Genetic Algorithm and evaluated in three ways: <ol> <li>Clamping: Iterative process to evaluate which cells in the circuit contribute dynamically to the output. A random cell is chosen a and its output set to a random constant value. Afterwards the fitness of the circuit is measured. If it decreases by less than 1 %, the cell is kept clamped, else reset to its original state.</li> <li>Temperature dependence: The FPGA with the circuit was cooled or heated to different temperatures and presented with different input frequencies. The output was averaged over 5 s.</li> <li>Location dependence: The circuit was moved to a different location o the FPGA. The Genetic Algorithm was then continued for additional 200 generations.</li> </ol>   This upload contains four groups of files: <ol> <li>experiment.h5 <ul> <li>All measurements and chromosomes from the original run of the Genetic Algorithm</li> </ul> </li> <li>clamping.h5 <ul> <li>All measurements of the clamping process</li> </ul> </li> <li>temperature-XX.h5 <ul> <li>All measurements for a different temperature</li> <li>XX is the temperature in degree Celsius</li> </ul> </li> <li>new_location-X.h5 <ul> <li>All measurements and chromosomes for the continued Genetic Algorithm at a new location on the FPGA</li> <li>X is the running number for hundred generations in the file, e.g. 2 contains generations 101 to 200</li> </ul> </li> </ol>     Errata: <ul> <li>The timestamps for the temperature measurements are missing in all files but experiment.h5.</li> </ul>

opencc-by-4.0May 2022View details →

zenodo44/100

Data files for: Gigantic jet discharges evolve stepwise through the middle atmosphere

Original video files and some other data belonging to the article "Gigantic jet discharges evolve stepwise through the middle atmosphere" published in Nature Communications on September 25th, 2019 (https://doi.org/10.1038/s41467-019-12261-y) The high-speed video .cine files can be read by (free) CineViewer and PCC software of Vision Research Inc. which can convert to avi files. For any questions, contact the first author.

opencc-by-4.0Sep 2019View details →

zenodo44/100

Macroevolutionary foundations of a recently-evolved innate immune defense

There are two datasets along with accompanying statistical code used to generate the results of our article, the first is a database of published articles that from the basis of the literature review and then the other file contains data from an experimental study of phylogenetic conservation of peritoneal fibrosis in 17 species of ray-finned fish. Detailed description of the methodology can be found in the article (https://doi.org/10.1101/2020.07.08.191601).

opencc-by-4.0Jul 2021View details →

zenodo44/100

Supplementary material for "Surface frustration re-patterning underlies the structural landscape and evolvability of fungal orphan candidate effectors"

Tables Table S1. List of fungal genomes analyzed in this work, associated references and properties. Table S2. List of all secreted proteins less than 300 amino-acids from the 20 fungal genomes. The table includes Signalp4.0 output, mature sequence, Espritz % disorder, pfam domains, AlphaFold top prediction pLDDT and the associated pdb file in Dataset S1. Table S3. Top Hits to pdb database for all OCE structures. 'network_node_name' corresponds to the portein identifier in the OCE structure similarity network provided in Dataset S3. 'Hidef_raw_community' corresponds to groups of structural OCE analogs identified by HiDEF community detection performed on the network provided in Dataset S3. Table S4. Table S4. List of the 62 major OCE folds with associated statistics. Columns I to AB provide the number of occurrences per species. Note that the actual number of members per species might be underestimated due to the stringent pipeline used for OCE identification (excluding proteins larger than 300 amino acids or containing PFAMs for instance).   Table S5. Relative surface exposure, conformational flexibility and conservation data mapped on residues of members of the Alt-A1 and BoNT families. RMSD, root mean square deviation for all aligned atoms; Conservation, percentage conservation in multiple structure alignment. Table S6. Assignment of NCBI accessions to MMseqs clusters and assignment of MMseqs clusters to HMM matching-based super-clusters. Table S7. Co-mutation occurrences and associated p-values in two OCE clades from the Alt-A1 and KP6 families. Table S8. Amino acid properties inferred from mutation scans and frustration analyses in Alt-A1 cluster yellow1 and KP6 cluster 43. 'Number of aa variants' corresponds to the number of different amino acids found at each position (deletion counts as 1). 'Alanine scan ∆Z' and 'Deletion scan ∆Z' correspond to the difference between Z-score for the native protein agains itself and Z-score for the native protein against mutant at each position (either Alanine replacement or 5-aa deletion). 'Destabilization factor' is the average of column E and F. 'Stabilization factor' corresponds to the difference between expected structural variation due to destabilization factor and the observed structural variation in multiple mutants. 'netEffect' is difference between column G and H. 'Max co-mutation %' is the highest frequency of co-mutation observed with other residues in natural variants, with 'Min co-mutation p-value (Bonferroni corrected)' the associated p-value.Table S9.  Sequence and delta Z of natural variants and mutants from AA1_cl25 Table S9. List of natural variants and in silico mutants from the Alt-A1 cluster 25 analyzed in this work, including protein sequence and structure comparison scores (comparison with the reconstructed clade ancestor n0). Table S10. List of natural variants and in silico mutants from the KP6 cluster 43 analyzed in this work, including protein sequence and structure comparison scores (comparison with the reconstructed clade ancestor n0). Table S11. Summary statistics for the phylogenetic trees of 15 OCE clades analyzed for structure and frustration evolution. Table S12. Mapping of structural and frustration data onto phylogenetic trees for 15 OCE clades. The corresponding trees and protein structures are provided in Dataset S7. Datasets Dataset S1. AlphaFold rank1 models for 3 927 OCEs (.pdb format). Dataset S2. Pairwise structure comparison for 3 911 OCE. DALI matrix output containing pairwise Z-scores. Dataset S3. Network file including 2 561 OCEs with 3 or more vertices of Z-score weight 5.2 or more, in .sif and .xgmml formats. Dataset S4. Videos illustrating the mapping of relative surface exposure and structural variability in Alt-A1 and BoNT groups, amino-acids conservation, co-selected mutation patches and residue net stabilization effects on Alt-A1 clade 25 ancestor and KP6 cluster 43 ancestor. Color scales are as in Figure 2 and 3 respectively (.mp4 format). Dataset S5. Phylogenetic trees (.nwk), ancestral (.fasta) and modern variant (.faa) sequences, and AlphaFold best protein models (.pdb) for members of KP6 cluster 43 and Alt-A1 cluster 25. The archive includes 140 Alt-A1 protein structure and 128 KP6 protein structures. Dataset S6. Best predicted structures for 917 natural variants and mutants of AA1_cl25 and 801 natural variants and mutants of KP6_cl43 (.pdb format). Dataset S7. Phylogenetic trees (.nwk) and AlphaFold best protein models (.pdb) for 15 OCE clades. The file includes 2 598 protein structures distributed from clades AA1_s (139), AA1_t (135), AA1_y1 (140), AA1_y2 (90), AA1_y3 (128), BoNT_s (291), CIP_s (167), CIP_t (231), crystallin (233), GNK2 (189), KP6_cl3 (203), KP6_cl26 (111), KP6_cl43 (123), KP6_cl96 (231), KP6_cl242 (187). Text and Figures Text S1. Contains supplementary methods, results and figures S1 to S13.

opencc-by-4.0Jan 2023View details →

zenodo44/100

Appendices of the work "On the perceived relevance of critical internal quality attributes when evolving software features"

Several refactorings performed while evolving software features aim to improve internal quality attributes like cohesion and complexity. Studies show that non-assisted refactorings might worsen, not improve, internal attributes. Current knowledge is scarce on how developers perceive the relevance of critical internal attributes while evolving features. Internal attributes are critical if their measurement assumes anomalous values. This qualitative study investigates the developer's perception on the relevance of critical internal attributes when evolving features. We target six class-level critical attributes: low cohesion, high complexity, high coupling, large hierarchy depth, large hierarchy breadth, and large size. We performed two industry case studies based on online focus group sessions. Developers discussed how much (and why) critical attributes are relevant for adding or enhancing features. We assessed the relevance of critical attributes individually and relatively, reasons behind the relevance of each critical attribute, and interrelations of critical attributes. Low cohesion and high complexity were perceived as very relevant because they often make evolving features hard while tracking failures and adding features. The other critical attributes were perceived as less relevant when reusing code or adopting design patterns. An example of perceived interrelation is high complexity leading to high coupling.

opencc-by-4.0Jan 2023View details →

zenodo44/100

Appendices of the work "On the perceived relevance of critical internal quality attributes when evolving software features"

opencc-by-4.0Jan 2023View details →

zenodo44/100

Venkataraman et al. Two novel, tightly linked, and rapidly evolving genes underlie Aedes aegypti mosquito reproductive resilience during drought

VERSION 1: These supplementary files accompany the manuscript by Venkataraman et al. entitled "Rapidly evolving genes underlie Aedes aegypti mosquito reproductive resilience during drought." This includes all raw data in the paper, supplementary data, and instructions for the blood puck feeder. VERSION 2: Supplemental Data Files 16-20 were added on 12/19/2022 to accompany a revision of the original bioRxiv pre-print after peer-review at eLife. VERSION 3: New versions of all files were added on 3/21/2023 to accompany the version of record published in eLife: Krithika Venkataraman , Nadav Shai, Priyanka Lakhiani, Sarah Zylka, Jieqing Zhao, Margaret Herre, Joshua Zeng, Lauren A Neal, Henrik Molina, Li Zhao, Leslie B Vosshall. Two novel, tightly linked, and rapidly evolving genes underlie Aedes aegypti mosquito reproductive resilience during drought. Elife. 2023 Feb 6;12:e80489. PMID: 36744865 DOI: 10.7554/eLife.80489

opencc-by-4.0Mar 2023View details →

zenodo40/100

Reproduction package for the paper "Correlating spectral and timing properties in the evolving jet of themicro blazar MAXI J1836-194"

Basic reproduction package for the paper in the title; it contains all the necessary information and scripts required to replicate the results and plots, minus the proprietary code used (which can be found on github on request).

opencc-by-4.0Dec 2020View details →

ScienceDex guides

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record