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.
1,025
datasets available to search
ShareScore release 0.9.0
Dataset results
1,025 results for “Encoding”
Figure 10 Optimal matrices for functions f1 (a), f2 (b), and f3 (c)-SELECTING OPERATIONS FOR ASSEMBLER ENCODING
<p>In the experiments, the following sets of operations were tested (a description of all<br> the operations specified below is presented in Appendix 1):<br> • Set 1 (all four-parameter operations used during the research reported in [15,17]):<br> CHG, CHGC0, CHGC1, CHGC2, CHGC3, CHGC4, CHGR0, CHGR1,<br> CHGR2, CHGR3, CHGR4, CHGM0, CHGM1, CHGM2, JMP;<br> • Set 2 (the most effective four-parameter operations used in the previous research):<br> CHGC0, CHGC3, CHGR0, CHGR3, CHGM0, CHGM2, JMP;<br> • Set 3: simpler variants of operations included in Set 1, the simpler operations, unlike<br> their more complex counterparts, always changed either the whole column or the<br> whole row or the whole matrix, operations from this set had maximally three<br> parameters;<br> • Set 4 (the three-parameter operations and jumps): CHG_VALUE, CHG_MEMORY,<br> JMP.</p>
Figure 9. Encoding CHG_MEMORY-Selecting Operations for Assembler Encoding
<p>The three-parameter operations are represented in a somewhat different way. Their<br> encoded form resembles classifiers from Learning Classifier Systems [1,3,6]. Similarity<br> between classifiers and the three-parameter operations results from the use of the so-called<br> don’t care symbol “#” in both cases. Each encoded operation consists of four blocks of genes.<br> The first single-bit block determines one of two possible variants of the operation (see<br> Appendix 1). The second and third block indicate location of changes performed by the<br> operation (don’t care symbol is used for this purpose). The last block specifies the value of an<br> integer parameter of the operation. The example use of don’t care symbol to locate changes in<br> NDM is illustrated in Fig 9.</p>
Figure 8. AEP encoding scheme-Selecting Operations for Assembler Encoding
<p>Each population delegates exactly one representative to each AEP.<br> In the beginning, AEPs have only one operation and a sequence of data. Both the operation<br> and data come from two different populations. Further populations including operations are<br> successively added if AEPs cannot accomplish progress in performance over the assumed<br> number of co-evolutionary cycles (we use term “co-evolutionary cycle” to differ it from the<br> evolutionary generation that takes place inside a single population with operations and data).</p>
Evolutionary Information Encoded in pLMs
<p>This dataset was created to test the effect of combining evolutionary information with protein language model embeddings by evaluating the effect on secondary structure prediction. Our method for predicting secondary structure used <em>PDB</em> (Berman et al., 2000) structures as ground truths. Sequences were cross-checked with <em>PDBredo DB</em> (Joosten et al., 2014) and <em>CATH</em> (Sillitoe et al., 2021). This resulted in 296,596 protein chain sequences from 117,623 different proteins. HSSP-values (HVAL) (<span>Rost, 1999; Sander & Schneider, 1991</span>) were computed for all protein chain pairs, and the sequences split into training test and validation set as follows:</p> <p><strong>TEST100:</strong> 100 randomly selected sequences meeting the following criteria: </p> <ol> <li>Deposited after April 2018 to allow a fair comparison to other recent methods</li> <li>Resolution: ≤2Å</li> <li>Any sequence pair (a,b) with a,b ∈ TEST100 must have an HVAL≤0</li> </ol> <p><strong>VAL100:</strong> 100 additional randomly selected sequences constrained to:</p> <ol> <li>Deposited before April 2018</li> <li>Resolution: ≤2Å</li> <li>Any sequence pair (a,b) with either a ∈ TEST100 or a ∈ VAL100 and b ∈ VAL100 had a maximal HVAL≤0</li> </ol> <p><strong>TRAIN6727:</strong> we used the remaining sequences for training if and only if the following criteria were fulfilled:</p> <ol> <li>Deposition before April 2018</li> <li>CATH annotations on the topology level (T) had to be different from any contained in TEST100 or VAL100</li> <li>HVAL≤0 for any pair (a,b) with a ∈ TEST100 or a ∈ VAL100 and b ∈ TRAIN6727</li> <li>PIDE≤70 for any pair (a,b) with a,b ∈ TRAIN6727, if a≠b</li> </ol> <p>This yielded 6,727 protein chains for training.</p> <p> </p> <p>This resource provided sequences, secondary structure annotations in 3-states, annotation of disordered regions, MSAs generated by <em>MMseqs2</em> (<span>Steinegger & Söding, 2017)</span>, PSSMs generated by MMseqs2 and meta files containing possible alternative PDB sequence IDs and CATH annotations.</p> <p>The original 8 DSSP (<span>Kabsch & Sander, 1983)</span> classes for secondary structure annotations were reduced to 3 following this protocol:</p> <ul> <li>DSSP-H, DSSP-G, and DSSP-I to helix (H)</li> <li>DSSP-E and DSSP-B to strand (E)</li> <li>all remaining classes to other (-)</li> </ul> <p>Disorder annotations were used to mask out residues in our evaluation that could not be resolved experimentally. All unresolved (disordered) residues are marked with X, while a dash (-) indicates a resolved position.</p> <p>Multiple Sequence alignments are provided in Stockholm format and the PSSMs are generated based on the provided MSAs. PSSMs were enumerated during creation. The mapping between the original PDB identifiers and the enumerated PSSMs is provided in the xxx.lookup files. </p>
TEI-XML Encoding of Paris, Bibliothèque nationale de France, fonds français, 24432
<p><strong>Encoding Principles</strong></p> <p>Material divisions within the codex have been marked, with <span><gb></span>, <span><pb></span>, <span><cb></span>, and <span><lb></span> tags indicating the beginning of a new quire, folio, column, and line respectively, while <span><handShift></span> is used to mark the beginning of a section of work by a particular scribe. Individual texts, and their <em>incipit</em> and <em>explicit</em> markings have been separated through use of the <span><div></span> tag, with sections of prose marked using <span><p></span>, and sections of poetry marked using <span><lg></span>, with individual lines of verse tagged using <span><l></span>. When a text is dialogic in form, this is marked using <span><spGrp></span>, with contributions by individual speakers marked using <span><sp></span>. Direct speech within non-dialogic text has been marked with <span><q></span> where such details are pertinent to our analysis.</p> <p>Labels, marginal notes, catchwords, highlighted sections, moments of damage, gaps in text, and blank spaces are marked using <span><label></span>, <span><note></span>, <span><fw></span>, <span><hi></span>, <span><damage></span>, <span><gap></span>, and <span><space></span> respectively, while the table of contents is encoded as a table using the <span><table></span>, <span><cell></span>, and <span><row></span> tags.</p> <p>Within the text of the encoding, expansions of scribal abbreviation, additions and deletions by scribal or later hands, as well as editorial regularisations, corrections, suppressions and insertions have been encoded using the <span><expan></span>, <span><add></span>,<span><del></span>, <span><reg></span>, <span><corr></span>, <span><surplus></span>, and <span><supplied></span> elements respectively, alongside the unexpanded, unregularised and uncorrected version of the text, through the <span><abbr></span>, <span><orig></span>, and <span><sic></span> elements respectively. Where more than one variant of the same segment of text has been encoded, for example in cases of expanded abbreviation and regularisation, the variants have been contained within a <span><choice></span> tag. Again, where such details have been pertinent to our analysis, personal names, geographic names, dates, and numbers have been encoded within <span><persName></span>, <span><placeName></span>, <span><date></span> and <span><num></span> elements.</p>
Fig. 4. Maximum Likelihood phylogenetic tree generated using N in The African buffalo parasite Theileria. sp. (buffalo) can infect and immortalize cattle leukocytes and encodes divergent orthologues of Theileria parva antigen genes
Fig. 4. Maximum Likelihood phylogenetic tree generated using N-terminal sequences of T. sp. (buffalo) and T. parva PIM antigen genes. Maximum composite likelihood trees were constructed using 1000 bootstrap replicates as implemented in MEGA5; the optimal nucleotide substitution model was identified using data monkey. The tree constructed with RAxML (Stamatakis et al., 2014) using a GTR/G/I model with 100 bootstrap iterations.
Fig. 5 in The African buffalo parasite Theileria. sp. (buffalo) can infect and immortalize cattle leukocytes and encodes divergent orthologues of Theileria parva antigen genes
Fig. 5. Maximum Likelihood Phylogenetic trees illustrating the genetic relationships of T. parva CD8 T target antigen gene orthologues from T. sp. (buffalo). Panel (A) Tp6; Panel B Tp7: Panel C Tp8. Sequences were aligned and used to construct a maximum likelihood tree, at which the nodes were confirmed using 1000 bootstrap replications. The bootstrap values indicating the degree of support for each node are shown and also the GenBank accession numbers of the sequences. For Tp6, the tree was rooted using the prohibitin gene sequences present in Babesia bovis (XM001609045) and Theileria orientalis (AB161472). For Tp7, the tree was rooted using the putative Heat shock protein 90 gene sequences from Toxoplasma gondii (AY344115), Babesia bovis (AK442026) and Theileria annulata (XM_947380). For Tp8, the tree was rooted using an orthologue of Tp8 found in Theileria equi (CP001669).
Fig. 3 in The African buffalo parasite Theileria. sp. (buffalo) can infect and immortalize cattle leukocytes and encodes divergent orthologues of Theileria parva antigen genes
Fig. 3. PCR amplification of genes encoding Theileria parva antigens from Marula schizont-infected leukocyte cultures. Panel A, p104 primers; Panel B PIM, primers; Panel C p67 primers. The order of the schizont-infected lymphocyte samples is (1) N6; (2). N13; (3). N18; (4). N20; (5). N33; (6). N36; (7). N38; (8). N43; (9). N50; (10). N55; (11). N69; (12). N76; (13). N77, (14). N79; (15). N86, (16). N88; (17). N99; (18). N100; (19). N102; (20). N103; (21). N106; (22). N107.
Fig. 2 in The African buffalo parasite Theileria. sp. (buffalo) can infect and immortalize cattle leukocytes and encodes divergent orthologues of Theileria parva antigen genes
Fig. 2. Results of a semi-nested PCR assay used to amplify 18S ribosomal subunit DNA using primers specific for T. parva and T. sp. (buffalo). Samples are as follows: 1)N13 2)N18 3) N20 4)N33 5)N36 6) N43 7)N50 8)N55 9) N69 10)N76 11) N79 12) N86 13) N88 14) N99 15)N100 16) N102 17) N103 18)N107 19—21) T. parva clones 22—24) T. sp. (buffalo) clones (documented in Table 2).
Fig. 1 in The African buffalo parasite Theileria. sp. (buffalo) can infect and immortalize cattle leukocytes and encodes divergent orthologues of Theileria parva antigen genes
Fig. 1. Reverse line blot analysis of schizont cultures containing parasites isolated from Marula farm. The following species-specific oligonucleotide probes were used (a) T. annulata, (b) T. parva, (c) T. mutans, (d) T. velifera, (e) T. taurotragi, (f) T. buffeli, (g) T. sp. (buffalo). (h) B. bigemina, (i) B. bovis. The order of the experimental samples hybridized is DNA from cell culture isolates in lanes 1—22 was lane 1; (1) N6, (2) N13, (3) N18, (4) N20, (5) N33, (6) N36, (7) N38 (8) N43, (9) N50 (10) N55, (11) N69, (12) N76, (13) N77, (14) N79, (15) N88, (16) N99, (17) N100 (18) N103, (19) N106, (20) N107, (21) N86, (22) N102 and DNA extracted from whole cattle blood (23) N106 (24) N69 (25) N86.
Figure 2. PAM250 matrix for the encoded sequence-Secondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm
<p>The PAM matrix (Dayhoff et al., 1978) describes the probability that original amino acid<br> will be replaced by another amino acid over a defined evolutionary interval. The unit of<br> evolutionary divergence is defined as the interval in which 1% of the amino acids have been<br> changed between two sequences. The work uses PAM250, which assumes the occurrence of 250-<br> point mutations per 100 amino acids.<br> So, for the given the protein sequence GIVEQCCASVCSLYQLENYCN, A will be replaced<br> by 1 -3 0 1 -3 -1 0 5 -2 -3 -4 -2 -3 -5 0 1 0 -7 -5 -1 as shown in Figure 2.</p>
Virtual ChIP-seq predictions for TF binding in Cistrome and ENCODE-DREAM datasets
<p>Each gzipped tarball contains BED files of Virtual ChIP-seq posterior probabilities.</p> <p>Each BED file corresponds to binding of a TF in one chromosome (one of chr5, chr10, chr15, and chr20) in a validation cell type of Cistrome (virchipCistromePredictions_V2.0.0.tar.gz) or ENCODE-DREAM Challenge datasets (virchipDreamPredictions_V1.0.0.tar.gz).</p> <p> </p> <p>V2.0.0 update:</p> <ul> <li>We provided our predictions on ENCODE-DREAM Challenge validation chromosomes (chr1, chr8, and chr21) in virchipDreamPredictions_V2.0.0.tar.gz</li> <li>FOXA1 predictions for binding in ch5 and chr10 of liver, and CTCF predictions for binding in chr10 of MCF-7 were missing from virchipCistromePredictions_V1.0.0.tar.gz</li> </ul> <p> </p>
Data supporting publication: Continuous spectral and coupling-strength encoding with dual-gradient metasurfaces
<p>Dataset for "Continuous spectral and coupling-strength encoding with dual-gradient metasurfaces" Figure 1 to Figure 5.</p>
Biomechanical filtering supports efficient tactile encoding in the human hand
<p>This repository contains the data and code used to produce the results in the publication "Biomechanical filtering supports efficient tactile encoding in the human hand." If you use these data or code, please cite our publication.</p> <p>Full citation: N. Tummala, G. Reardon, B. Dandu, Y. Shao, H. P. Saal, and Y. Visell, "Biomechanical filtering supports efficient tactile encoding in the human hand". bioRxiv, 2024. doi: 10.1101/2023.11.10.565040</p> <p> </p> <p><strong>Abstract From Manuscript</strong></p> <p>Touching an object elicits skin oscillations that are biomechanically transmitted throughout the hand, driving responses in thousands of tactile receptors, including numerous exquisitely sensitive Pacinian corpuscles (PCs). Accepted descriptions of PC functionality characterize their response properties as highly stereotyped, based on experimental data gathered when stimuli are applied near the receptor. However, during natural touch, spiking activity in the majority of PCs is evoked by transmitted skin oscillations that are modified by biomechanical filtering. This filtering mechanism, stemming from dispersive wave dynamics in the skin, bears some similarity to the pre-neuronal filtering of auditory signals by the basilar membrane, a mechanical process that is instrumental to perception. Thus, we sought to clarify how skin biomechanics might influence tactile information encoding in the periphery. We used vibrometry imaging and computational neural experiments to examine the influence of biomechanical filtering on neural activity in whole-hand PC populations. We observed complex, location- and frequency-dependent patterns of filtering that were shaped by tissue mechanics and hand morphology. This source of biomechanical modulation diversified PC population spiking activity and enhanced tactile information encoding efficiency. These findings indicate that biomechanics furnishes a pre-neuronal mechanism that facilitates efficient tactile encoding and processing.</p> <p> </p>
TEICatalogs: Corpus of encoded 19th and 20th catalogs
<p>This release contains six XML-TEI exhibition catalogs encoded using the <a href="https://zenodo.org/record/5464146#.YTXXrI5KjIU">Open Source Pipeline</a> created for <a href="https://artlas.huma-num.fr/fr/">Artl@s.</a></p>
Populations of local direction-selective cells encode global motion patterns generated by self-motion. Data, Code and Model.
<p>Directional tuning of the population of local motion detectors T4/T5 in the visual system of the fruit fly <em>Drosophila melanogaster</em>. Direction tuning and receptive field location was measured by recording responses to visual stimuli containing dark or bright edges/stripes moving into 8 directions. All provided MATLAB scripts were used to analyze and illustrate data show in the manuscript 'Populations of local direction-selective cells encode global motion patterns generated by self-motion.'</p> <p>All data were obtained using <em>in vivo </em>two photon microscopy. Image time series were preprocessed using SIMA python software for motion alignment and further processed using custom written matlab or python code.</p> <p>Please find all relevant information to use the code in the README file.</p>
Sound improves neuronal encoding of visual stimuli in mouse primary visual cortex
<p class="MsoNormal"><span>In everyday life, we integrate visual and auditory information in routine tasks such as navigation and communication. While concurrent sound can improve visual perception, the neuronal <span>correlates of audiovisual integration are not fully understood. Specifically, it remains unclear whether neuronal firing patters in the primary visual cortex (V1) of awake animals demonstrate similar sound-induced improvement in visual discriminability. Furthermore, presentation of sound is associated with movement in the subjects, but little is understood about whether and how sound-associated movement affects audiovisual integration in V1. Here, we investigated how sound and movement interact </span>to modulate V1 visual responses in awake, head-fixed mice and whether this interaction improves neuronal encoding of the visual stimulus. We presented visual drifting gratings with and without simultaneous auditory white noise to awake mice while recording mouse movement and V1 neuronal activity. Sound modulated activity of 80% of light-responsive neurons, with 95% of neurons increasing activity when the auditory stimulus was present. A generalized linear model revealed that sound and movement had distinct and complementary effects of the neuronal visual responses. Furthermore, decoding of the visual stimulus from the neuronal activity was improved with sound, an effect that persisted even when controlling for movement. These results demonstrate that sound and movement modulate visual responses in complementary ways, improving neuronal representation of the visual stimulus. This study clarifies the role of movement as a potential confound in neuronal audiovisual responses and expands our knowledge of how multimodal processing is mediated at a neuronal level in the awake brain.</span></p> <p></p>
CNN weight data for "Model identification of neural encoding (MINE)" publication - Set 2
<p>This dataset contains the weights of fit CNN models generated during the analysis of the zebrafish thermoregulation dataset and the Musall et al. mouse dataset processed by MINE. This set contains the last fish and the mouse MINE model weights. The other 24 fish are contained in Set 1.</p>
CNN weight data for "Model identification of neural encoding (MINE)" publication - Set 1
<p>This dataset contains the weights of fit CNN models generated during the analysis of the zebrafish thermoregulation dataset processed by MINE. This set contains 24/25 fish. The last fish and mouse MINE model weights are contained in Set 2.</p>
EEG Dataset for 'Decoding of selective attention to continuous speech from the human auditory brainstem response' and 'Neural Speech Tracking in the Theta and in the Delta Frequency Band Differentially Encode Clarity and Comprehension of Speech in Noise'.
<p>The repository contains the unprocessed EEG data recorded for the publications [1, 2]. For convenience, the onsets of the EEG data provided here are time-aligned with the onsets of the audio books in the 'audiobooks' folder, and the EEG data are provided in HDF5 format. Please refer to the original version of this dataset for more details.</p> <p>More details, as well as the original data files, are available at the original repository <a href="https://doi.org/10.5281/zenodo.7086209">here</a>.</p> <p>Examples of using these data (preprocessing, fitting linear models) can be found <a href="https://github.com/Mike-boop/trf-examples">here</a>.</p> <p>The English conditions (clean, lb, mb, hb, fM, fW) comprised a single recording session. The Dutch conditions (cleanDutch, lbDutch, mbDutch, hbDutch) comprised a separate recording session. You see which participants took part in each session in session_info.json.</p> <p>Please note some details about the stimulus presentation for the various listening conditions:</p> <ul> <li>English speech-in-babble-noise (lb, mb, hb): babble noise was played by itself for one second before the audiobook track began. The babble noise was also played for one second after the audiobook track ended. Therefore, you should discard the first second and the last second from these trial during your analysis.</li> <li>Dutch speech-in-babble-noise (lbDutch, mbDutch, hbDutch): the story (narrated in Dutch) was played by itself for one second before the babble noise track began. Then, the babble noise was increased linearly in amplitude for one second. Therefore, you should discard the first two seconds from these trials during your analysis.</li> <li>Dutch in quiet, and Dutch-in-babble-noise (cleanDutch, lbDutch, mbDutch, hbDutch): some English sentences were embedded in the Dutch narratives in order to encourage attention. You should crop these from your analysis. The onsets and offsets of the English sentences (in samples, at 44100Hz) are provided in the audiobooks/*Dutch/english_onsets_info.json files.</li> <li>Competing-speakers conditions (fM, fW): sometimes the attended track is longer than the unattended track, or vice-versa. The onsets of both tracks are aligned. You should crop the trial to the length of the shortest track for your analysis.</li> </ul> <p>If you use this data, please cite the original publications, as well as this repository [1,2,3].</p> <p>[1] Etard O, Kegler M, Braiman C, Forte A E and Reichenbach T. “Decoding of selective attention to continuous speech from the human auditory brainstem response” 2019. <em>NeuroImage</em> <strong>200</strong> 1–11</p> <p>[2] Etard O and Reichenbach T. “Neural speech tracking in the theta and in the delta frequency band differentially encode clarity and comprehension of speech in noise” 2019. <em>J. Neurosci.</em> <strong>39</strong> 5750–9</p> <p>[3] Etard O and Reichenbach T. "EEG Dataset for 'Decoding of selective attention to continuous speech from the human auditory brainstem response' and 'Neural Speech Tracking in the Theta and in the Delta Frequency Band Differentially Encode Clarity and Comprehension of Speech in Noise". Doi: 10.5281/zenodo.7086208</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.