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204 results for “fundamental”
Supplementary Data for Paper "The Effect of Display Pixel Density on Minimum Legible Size of Fundamental Cartographic Symbols"
<p>This is the result data of the user study described in the paper "The Effect of Display Pixel Density on Minimum Legible Size of Fundamental Cartographic Symbols".</p> <p>Author information and further metadata will be added after anonymous peer review.</p> <p>27 participants, 4 displays, 6 tasks. Note that the data contains the station ID (A-D), which maps to Displays 1-4 as described in the paper: A - D2; B - D4; C - D1; D - D3;</p> <p>File description:</p> <ul> <li>all_aggregated_users.csv: Thresholds for each task and station, for each participant. One row per participant, with fields for each station/task combination (27 rows).</li> <li>all_aggregated_thresholds.csv: Thresholds for each participant, task and station. One row per threshold value, thresholds for task #2 for participants #1-3 have been discarded, due to an error in the experiment configuration (see paper). (27 x 4 x 6 - 3 x 4 = 636 rows)</li> </ul>
New aesthetic in-house 3D-printed brackets: proof of concept and fundamental mechanical properties
<p>Dataset for all analyses</p>
Data presented in "An ultracold molecular beam for testing fundamental physics"
<p>Data presented in "An ultracold molecular beam for testing fundamental physics". The original paper can be found at https://doi.org/10.1088/2058-9565/ac107e. This is the data underlying the simulations and the experimental results.</p>
Fundamental Ethical Principles of Co-creation (Infographic)
<p>An infographic showing the fundamental ethical principles of co-creation.</p>
Fundamental Asynchronous Stimulus Timing Sound Coding Study
ClinicalTrials.gov study NCT02698787. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Ant behavioral maturation is mediated by a stochastic transition between two fundamental states
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Physiological responses of fen mosses along a nitrogen gradient point to competition restricting their fundamental niches
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Fundamental mode Rayleigh wave group velocity dispersion data
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Data for: Fundamental interaction niches: towards a functional understanding of ecological networks’ resilience
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Intensive hunting fundamentally changes human-wildlife relationships
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Data from: The fundamental role of character coding in Bayesian morphological phylogenetics
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Convergence in voice fundamental frequency in a joint speech production task - Dataset
<p>This dataset contains fundamental frequency values for 30 pairs of participants performing an alternate reading task.</p> <p>Fundamental frequency in each speaker's speech was artificially modified in real-time during the task. We provide both the untransformed and transformed fundamental frequency values.</p> <p>Full description of the experimental setup is found in < insert paper DOI here ></p> <p>The data is organised as follows:</p> <ul> <li>data for each pair is stored in a separate folder with the pair ID as the folder name</li> <li>each folder contains two repetitions of the task, as produced in a zero-phase and pi-phase condition, respectively</li> <li>file names ending with '<strong>f0</strong>' contain fundamental frequency data, sampled every 10 ms</li> <li>file names ending with '<strong>turns</strong>' contain time onsets of speaking turns</li> </ul> <p>The format of '<strong>f0</strong>' files is as follows:</p> <ul> <li>'<strong>t</strong>': time in seconds</li> <li>'<strong>ch</strong>': channel of the recording, indicating the participant (<em>A</em> or <em>B</em>)</li> <li>'<strong>f0_unstransf</strong>': fundamental frequency values as produced by the participant (untransformed) in Hertz</li> <li>'<strong>f0_transf</strong>': fundamental frequency values as heard by the other participant (transformed) in Hertz</li> </ul> <p>The format of '<strong>turns</strong>' files is as follows:</p> <ul> <li>'<strong>ch</strong>': channel of the recording, indicating the participant (<em>A</em> or <em>B</em>), or both participants at once (<em>joint</em>)</li> <li>'<strong>turn</strong>': index of the reading turn</li> <li>'<strong>type</strong>': turn type. Either <em>speech</em> or <em>silence</em> for each participant, or <em>turn</em> for the joint description.</li> <li>'<strong>t</strong>': turn onset in seconds</li> </ul> <p> </p>
SmartCom XML Scheme Definition (XSD) for a Machine-readable Communication of Fundamental Constants in Metrology
<p>The XSD defines a data structure for an unambiguous, easy-to-use, safe and uniform exchange of fundamental physical constants and mathematical constants in a machine-readable data format. The data elements for constants are defined in the Digital System of Units (D-SI) metadata model from the EMPIR project 17IND02 SmartCom. The development of the data structure was based on an own analysis of minimum requirement for transfering the data of fundamental physical constants "as are" provided by CODATA into a machine-readable form. Furthermore, the considerations for the transformation comprise traceability to the original CODATA values.</p> <p>The availability of machine-readable data of fundamental physical constants that can easy be accessed by software and that are traceable to a international accepted definition is essential for various areas of metrology where the International System of Units (SI) is of key-importance.</p>
A Replication Dataset for Fundamental Frequency Estimation
<p><em>Part of the dissertation <a href="http://localhost:8000/index.html">Pitch of Voiced Speech in the Short-Time Fourier Transform: Algorithms, Ground Truths, and Evaluation Methods</a>.<br> © 2020, Bastian Bechtold. All rights reserved.</em></p> <p> </p> <p>Estimating the fundamental frequency of speech remains an active area of research, with varied applications in speech recognition, speaker identification, and speech compression. A vast number of algorithms for estimatimating this quantity have been proposed over the years, and a number of speech and noise corpora have been developed for evaluating their performance. The present dataset contains estimated fundamental frequency tracks of 25 algorithms, six speech corpora, two noise corpora, at nine signal-to-noise ratios between -20 and 20 dB SNR, as well as an additional evaluation of synthetic harmonic tone complexes in white noise.</p> <p>The dataset also contains pre-calculated performance measures both novel and traditional, in reference to each speech corpus’ ground truth, the algorithms’ own clean-speech estimate, and our own consensus truth. It can thus serve as the basis for a comparison study, or to replicate existing studies from a larger dataset, or as a reference for developing new fundamental frequency estimation algorithms. All source code and data is available to download, and entirely reproducible, albeit requiring about one year of processor-time.</p> <p>Included Code and Data</p> <ul> <li><code>ground truth data.zip</code> is a <a href="https://jbof.readthedocs.io/en/latest/">JBOF</a> dataset of fundamental frequency estimates and ground truths of all speech files in the following corpora: <ul> <li><a href="http://www.festvox.org/cmu_arctic/">CMU-ARCTIC</a> (<em>consensus truth</em>) [1]</li> <li><a href="http://www.cstr.ed.ac.uk/research/projects/fda/">FDA</a> (<em>corpus truth</em> and <em>consensus truth</em>) [2]</li> <li><a href="https://lost-contact.mit.edu/afs/nada.kth.se/dept/tmh/corpora/KeelePitchDB/">KEELE</a> (<em>corpus truth</em> and <em>consensus truth</em>) [3]</li> <li><a href="http://www.cstr.ed.ac.uk/research/projects/artic/mocha.html">MOCHA-TIMIT</a> (<em>consensus truth</em>) [4]</li> <li><a href="https://www.spsc.tugraz.at/databases-and-tools/ptdb-tug-pitch-tracking-database-from-graz-university-of-technology.html">PTDB-TUG</a> (<em>corpus truth</em> and <em>consensus truth</em>) [5]</li> <li><a href="https://catalog.ldc.upenn.edu/LDC93S1">TIMIT</a> (<em>consensus truth</em>) [6]</li> </ul> </li> <li><code>noisy speech data.zip</code> is a <a href="https://jbof.readthedocs.io/en/latest/">JBOF</a> datasets of fundamental frequency estimates of speech files mixed with noise from the following corpora: <ul> <li><a href="http://www.speech.cs.cmu.edu/comp.speech/Section1/Data/noisex.html">NOISEX</a> [7]</li> <li><a href="https://research.qut.edu.au/saivt/databases/qut-noise-databases-and-protocols/">QUT-NOISE</a> [8]</li> </ul> </li> <li><code>synthetic speech data.zip</code> is a <a href="https://jbof.readthedocs.io/en/latest/">JBOF</a> dataset of fundamental frequency estimates of synthetic harmonic tone complexes in white noise.</li> <li><code>noisy_speech.pkl</code> and <code>synthetic_speech.pkl</code> are pickled <a href="https://pandas.pydata.org/">Pandas</a> dataframes of performance metrics derived from the above data for the following list of fundamental frequency estimation algorithms: <ul> <li>AUTOC [9]</li> <li>AMDF [10]</li> <li><a href="http://www2.ece.rochester.edu/projects/wcng/code.html">BANA</a> [11]</li> <li>CEP [12]</li> <li><a href="https://github.com/marl/crepe">CREPE</a> [13]</li> <li><a href="http://www.kki.yamanashi.ac.jp/~mmorise/world/english/">DIO</a> [14]</li> <li><a href="http://web.cse.ohio-state.edu/pnl/software.html">DNN</a> [15]</li> <li><a href="https://github.com/LvHang/pitch">KALDI</a> [16]</li> <li>MAPS</li> <li><a href="http://www.seas.ucla.edu/spapl/shareware.html">MBSC</a> [17]</li> <li><a href="https://github.com/jkjaer/fastF0Nls">NLS</a> [18]</li> <li><a href="http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html">PEFAC</a> [19]</li> <li><a href="https://github.com/praat/praat">PRAAT</a> [20]</li> <li><a href="http://www.speech.kth.se/wavesurfer/links.html">RAPT</a> [21]</li> <li><a href="http://labrosa.ee.columbia.edu/projects/SAcC/">SACC</a> [22]</li> <li><a href="http://www.seas.ucla.edu/spapl/weichu/safe/">SAFE</a> [23]</li> <li><a href="https://mathworks.com/matlabcentral/fileexchange/1230">SHR</a> [24]</li> <li>SIFT [25]</li> <li><a href="https://github.com/covarep/covarep">SRH</a> [26]</li> <li><a href="https://github.com/HidekiKawahara/legacy_straight">STRAIGHT</a> [27]</li> <li><a href="http://www.cise.ufl.edu/~acamacho/english/curriculum.html">SWIPE</a> [28]</li> <li><a href="http://www.ws.binghamton.edu/zahorian/yaapt.htm">YAAPT</a> [29]</li> <li><a href="http://audition.ens.fr/adc/">YIN</a> [30]</li> </ul> </li> <li><code>noisy speech evaluation.py</code> and <code>synthetic speech evaluation.py</code> are Python programs to calculate the above <a href="https://pandas.pydata.org/">Pandas</a> dataframes from the above <a href="https://jbof.readthedocs.io/en/latest/">JBOF</a> datasets. They calculate the following performance measures: <ul> <li>Gross Pitch Error (GPE), the percentage of pitches where the estimated pitch deviates from the true pitch by more than 20%.</li> <li>Fine Pitch Error (FPE), the mean error of grossly correct estimates.</li> <li>High/Low Octave Pitch Error (OPE), the percentage pitches that are GPEs and happens to be at an integer multiple of the true pitch.</li> <li>Gross Remaining Error (GRE), the percentage of pitches that are GPEs but not OPEs.</li> <li>Fine Remaining Bias (FRB), the median error of GREs.</li> <li>True Positive Rate (TPR), the percentage of true positive voicing estimates.</li> <li>False Positive Rate (FPR), the percentage of false positive voicing estimates.</li> <li>False Negative Rate (FNR), the percentage of false negative voicing estimates.</li> <li>F₁, the harmonic mean of precision and recall of the voicing decision.</li> </ul> </li> <li><code>Pipfile</code> is a pipenv-compatible pipfile for installing all prerequisites necessary for running the above Python programs.</li> </ul> <p>The Python programs take about an hour to compute on a fast 2019 computer, and require at least 32 Gb of memory.</p> <p>References:</p> <ol> <li>John Kominek and Alan W Black. CMU ARCTIC database for speech synthesis, 2003.</li> <li>Paul C Bagshaw, Steven Hiller, and Mervyn A Jack. Enhanced Pitch Tracking and the Processing of F0 Contours for Computer Aided Intonation Teaching. In EUROSPEECH, 1993.</li> <li>F Plante, Georg F Meyer, and William A Ainsworth. A Pitch Extraction Reference Database. In Fourth European Conference on Speech Communication and Technology, pages 837–840, Madrid, Spain, 1995.</li> <li>Alan Wrench. MOCHA MultiCHannel Articulatory database: English, November 1999.</li> <li>Gregor Pirker, Michael Wohlmayr, Stefan Petrik, and Franz Pernkopf. A Pitch Tracking Corpus with Evaluation on Multipitch Tracking Scenario. page 4, 2011.</li> <li>John S. Garofolo, Lori F. Lamel, William M. Fisher, Jonathan G. Fiscus, David S. Pallett, Nancy L. Dahlgren, and Victor Zue. TIMIT Acoustic-Phonetic Continuous Speech Corpus, 1993.</li> <li>Andrew Varga and Herman J.M. Steeneken. Assessment for automatic speech recognition: II. NOISEX-92: A database and an experiment to study the effect of additive noise on speech recog- nition systems. Speech Communication, 12(3):247–251, July 1993.</li> <li>David B. Dean, Sridha Sridharan, Robert J. Vogt, and Michael W. Mason. The QUT-NOISE-TIMIT corpus for the evaluation of voice activity detection algorithms. Proceedings of Interspeech 2010, 2010.</li> <li>Man Mohan Sondhi. New methods of pitch extraction. Audio and Electroacoustics, IEEE Transactions on, 16(2):262—266, 1968.</li> <li>Myron J. Ross, Harry L. Shaffer, Asaf Cohen, Richard Freudberg, and Harold J. Manley. Average magnitude difference function pitch extractor. Acoustics, Speech and Signal Processing, IEEE Transactions on, 22(5):353—362, 1974.</li> <li>Na Yang, He Ba, Weiyang Cai, Ilker Demirkol, and Wendi Heinzelman. BaNa: A Noise Resilient Fundamental Frequency Detection Algorithm for Speech and Music. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(12):1833–1848, December 2014.</li> <li>Michael Noll. Cepstrum Pitch Determination. The Journal of the Acoustical Society of America, 41(2):293–309, 1967.</li> <li>Jong Wook Kim, Justin Salamon, Peter Li, and Juan Pablo Bello. CREPE: A Convolutional Representation for Pitch Estimation. arXiv:1802.06182 [cs, eess, stat], February 2018. arXiv: 1802.06182.</li> <li>Masanori Morise, Fumiya Yokomori, and Kenji Ozawa. WORLD: A Vocoder-Based High-Quality Speech Synthesis System for Real-Time Applications. IEICE Transactions on Information and Systems, E99.D(7):1877–1884, 2016.</li> <li>Kun Han and DeLiang Wang. Neural Network Based Pitch Tracking in Very Noisy Speech. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(12):2158–2168, Decem- ber 2014.</li> <li>Pegah Ghahremani, Bagher BabaAli, Daniel Povey, Korbinian Riedhammer, Jan Trmal, and Sanjeev Khudanpur. A pitch extraction algorithm tuned for automatic speech recognition. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, pages 2494–2498. IEEE, 2014.</li> <li>Lee Ngee Tan and Abeer Alwan. Multi-band summary correlogram-based pitch detection for noisy speech. Speech Communication, 55(7-8):841–856, September 2013.</li> <li>Jesper Kjær Nielsen, Tobias Lindstrøm Jensen, Jesper Rindom Jensen, Mads Græsbøll Christensen, and Søren Holdt Jensen. Fast fundamental frequency estimation: Making a statistically efficient estimator computationally efficient. Signal Processing, 135:188–197, June 2017.</li> <li>Sira Gonzalez and Mike Brookes. PEFAC - A Pitch Estimation Algorithm Robust to High Levels of Noise. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(2):518—530, February 2014.</li> <li>Paul Boersma. Accurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound. In Proceedings of the institute of phonetic sciences, volume 17, page 97—110. Amsterdam, 1993.</li> <li>David Talkin. A robust algorithm for pitch tracking (RAPT). Speech coding and synthesis, 495:518, 1995.</li> <li>Byung Suk Lee and Daniel PW Ellis. Noise robust pitch tracking by subband autocorrelation classification. In Interspeech, pages 707–710, 2012.</li> <li>Wei Chu and Abeer Alwan. SAFE: a statistical algorithm for F0 estimation for both clean and noisy speech. In INTERSPEECH, pages 2590–2593, 2010.</li> <li>Xuejing Sun. Pitch determination and voice quality analysis using subharmonic-to-harmonic ratio. In Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference on, volume 1, page I—333. IEEE, 2002.</li> <li>Markel. The SIFT algorithm for fundamental frequency estimation. IEEE Transactions on Audio and Electroacoustics, 20(5):367—377, December 1972.</li> <li>Thomas Drugman and Abeer Alwan. Joint Robust Voicing Detection and Pitch Estimation Based on Residual Harmonics. In Interspeech, page 1973—1976, 2011.</li> <li>Hideki Kawahara, Masanori Morise, Toru Takahashi, Ryuichi Nisimura, Toshio Irino, and Hideki Banno. TANDEM-STRAIGHT: A temporally stable power spectral representation for periodic signals and applications to interference-free spectrum, F0, and aperiodicity estimation. In Acous- tics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on, pages 3933–3936. IEEE, 2008.</li> <li>Arturo Camacho. SWIPE: A sawtooth waveform inspired pitch estimator for speech and music. PhD thesis, University of Florida, 2007.</li> <li>Kavita Kasi and Stephen A. Zahorian. Yet Another Algorithm for Pitch Tracking. In IEEE International Conference on Acoustics Speech and Signal Processing, pages I–361–I–364, Orlando, FL, USA, May 2002. IEEE.</li> <li>Alain de Cheveigné and Hideki Kawahara. YIN, a fundamental frequency estimator for speech and music. The Journal of the Acoustical Society of America, 111(4):1917, 2002.</li> </ol>
Data from: Experimental evidence for fundamental, and not realised, niche partitioning in a plant-herbivore community interaction network
Patterns of niche partitioning can result from local ecological interactions (e.g. interspecific competition) occurring within a contemporary time frame (realised niche partitioning). Alternatively they may represent the end-product of historical processes acting over long time frames (fundamental niche partitioning). Niche partitioning is often detected by analysing patterns of resource use within communities, but experiments are rarely conducted to test whether patterns of non-overlapping resource use reflect realised or fundamental niche partitioning. We studied a community of restio leafhoppers from the genus Cephalelus, and their host plants, the Restionaceae (restios). We used network and experimental approaches to determine whether network modularity (a measure of niche partitioning within local communities) reflects fundamental or realised niche partitioning. Using a weighted modularity index for two party networks (e.g. insect - plant) we determined whether the network of this community is modular (i.e. consists of groups of species interacting strongly, with weak interactions between groups). We also aimed to identify specific Cephalelus - restio modules (groups). Using knowledge of module membership to design experiments, we tested whether Cephalelus species from two different modules, C. uncinatus and C. pickeri, prefer and perform better on restios from their own modules versus restios from other modules. These experiments were performed under controlled conditions, eliminating the influences of competition and predation on host choices. The Cephalelus – restio community was modular, implying niche partitioning. Cephalelus also preferred and performed better on restios from their own modules in the absence of local contemporary factors. Most niche partitioning in the investigated Cephalelus community, is not caused by local interactions, and thus host use patterns represent fundamental niches. Our findings highlight the importance of understanding local community structure in the light of processes extrinsic to the local community context.
Data from: Revisiting niche fundamentals with Tukey depth
The first attempts to describe species ecological niches were simple geometric procedures that depict the niche boundaries directly from environmental data. The convex hull was one of such procedures, popular for its simplicity, clear ecological rational and precise definition of the niche. However, it lacked the ability to differentiate areas of the niche with different probabilities of occurrence according to environmental suitability. We incorporate the Tukey depth, a mathematical tool to measure the centrality of a point within a cloud of points on a multidimensional space, in the convex hull approach to (i) propose a new procedure (CH‐Tukey) to estimate species' environmental suitability, and (ii) estimate niche overlap coherently. In addition to a clear ecological rational and simplicity the CH‐Tukey procedure has a number of attractive features: use of presence‐only data; independence from background data; invariance to scale; robustness to outliers; and the decomposition of the niche into a finite number of iso‐suitability levels, permitting the computation of consistent overlap indices. We illustrate the use of CH‐Tukey, using occurrence data of the main Quercus species and subspecies from Western Mediterranean Europe, comparing its outputs with BIOCLIM and MaxEnt. Results showed distinct niche geometries among the different approaches. BIOCLIM produced rectilinear niches reflecting the assumption that ecological variables are independent in their action on the species. CH‐Tukey, relaxing this assumption, adjusts niche outer boundary and the inner suitability levels to the known occurrences. MaxEnt produced unbounded niche geometries, showing abrupt shifts in the species response to the environmental variables. The niche predictions obtained with geometric approaches, BIOCLIM and CH‐Tukey, are simpler but better aligned with Hutchinson's niche concept than those obtained with MaxEnt, this latter showing ecologically implausible relationships with the environmental variables. CH‐Tukey and the related overlap measures provide an adequate tool to explore niche properties and species–environment relationships.
Data from: Fundamental and realized feeding niche breadths of sexual and asexual stick insects
The factors contributing to the maintenance of sex over asexuality in natural populations remain unclear. Ecological divergences between sexual and asexual lineages could help to maintain reproductive polymorphisms, at least transiently, but the consequences of asexuality for the evolution of ecological niches are unknown. Here, we investigated how niche breadths change in transitions from sexual reproduction to asexuality. We used host plant ranges as a proxy to compare the realized feeding niche breadths of five independently derived asexual Timema stick insects' species and their sexual relatives at both the species and population levels. Asexual species had a systematically narrower realized niche than sexual species, though this pattern was not apparent at the population level. To investigate how the narrower realized niches of asexual species arise, we performed feeding experiments to estimate fundamental niche breadths but found no systematic differences between reproductive modes. The narrow realized niches found in asexual species are therefore likely a consequence of biotic interactions such as predation or competition, that constrain realized niche size in asexuals more strongly than in sexuals.
Performance of Fe-N/C oxygen reduction electrocatalysts towards NO2-, NO, and NH2OH electroreduction – from fundamental insights into the active centre to a new method for environmental nitrite destruction
<p>This Excel data file contains the data used to produce the figures in the paper:</p> <p>Malko, D., Kucernak, A and Lopes, T, " Performance of Fe-N/C oxygen reduction electrocatalysts towards NO2-, NO, and NH2OH electroreduction – from fundamental insights into the active centre to a new method for environmental nitrite destruction"</p> <p>Journal of the American Chemical Society, 2016, DOI:10.1021/jacs.6b09622</p> <p>Please cite the above reference if you wish to use this data</p>
FIGURE 5 in The fundamental karyotype and plastid DNA of Alstroemeria piperata (Liliales, Alstroemeriaceae), a species endemic to the Valparaíso Region, Chile
FIGURE 5. Phylogenetic relationships from analyses of three plastid markers (petA-psbJ, trnL-rpl32 and intron rpl16) of Alstroemeria piperata and other Chilean alstroemerias (Baeza et al. 2022). A. Network inferred with TCS. B. Maximum likelihood tree inferred from DNA sequences and indels. The branch lengths in B were modified for aesthetic reasons.
Harmonic scale factors of fundamental transitions for dispersion-corrected quantum chemical methods
<p>These two datasets contain quantum-chemical calculations, experimental data, and scripts for obtaining scale factors for harmonic vibrational frequencies for fundamental transitions. </p> <p>The first dataset (<span><a href="../api/records/11174948/draft/files/scaling_quantum_chemistry.tar.xz/content" target="_blank" rel="noopener noreferrer">scaling_quantum_chemistry.tar.xz</a></span>) contains quantum-chemical calculations for the training dataset of various molecules and three exemplary molecules: pyrene, protonated LeuEnk, and doubly protonated gramicidin S. The second dataset (<span><a href="../api/records/11174948/draft/files/scaling_fitting.zip/content" target="_blank" rel="noopener noreferrer">scaling_fitting.zip</a></span>) contains the data and scripts for fitting the scale factors. The training set comprises 441 molecules of various sizes (from diatomics to C60). </p>
ScienceDex guides
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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.