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1,048 results for “interpreting”
FIG. 5. — Tomb 46 in Gaming and divination pieces, markers of ownership, or all three? Zooarchaeology and the interpretation of knuckle bones found in tombs of the Iberian necropolis of El Poblado (Coimbra del Barranco Ancho, Murcia, Spain)
FIG. 5. — Tomb 46, perforated knuckle bone. Scale bar: 1 cm. Photo credit: P. Thomas.
FIG. 2 in Gaming and divination pieces, markers of ownership, or all three? Zooarchaeology and the interpretation of knuckle bones found in tombs of the Iberian necropolis of El Poblado (Coimbra del Barranco Ancho, Murcia, Spain)
FIG. 2. — View of the Coimbra del Barranco Ancho site. Photo credit:J. M. García Cano.
FIG. 1 in Gaming and divination pieces, markers of ownership, or all three? Zooarchaeology and the interpretation of knuckle bones found in tombs of the Iberian necropolis of El Poblado (Coimbra del Barranco Ancho, Murcia, Spain)
FIG. 1. — Location of the Coimbra del Barranco Ancho site.
Results and interpretation of spectral-window selection by ModeNet
<p>This data set includes the results and interpretation of spectral-window selection by ModeNet shown in the article "ModeNet: A machine learning-based neural network for selecting normal-mode spectral windows".</p>
Smartphone Disengagement - Autoethnography fieldnotes, classification & interpretation
<p>This dataset contains transcribed fieldnotes from a “methodological assemblage” and technological prototype connecting autoethnography to the practices of self-research in personal science. As an experimental process of personal data gathering, the author used a low-tech device for the active registration of events and their perception, in a case study on disengaging from his smartphone.</p> <p>The dataset contains 267 data points (timestamp and intensity/duration of perceived events) with alternative classification and subclassification categories through thematic analysis, as well as 112 "on spot" fieldnotes and 112 related retrospective interpretation notes (with corresponding references where indicated) from a focused autoethnography intervention, tracking the authors' experience of living without constant access to a smartphone for a month (during May 2021).</p> <p>For more info on the process and the main results of this personal experiment, here's the derived publication: Senabre Hidalgo, E., & Greshake Tzovaras, B. (2023). “One button in my pocket instead of the smartphone”: A methodological assemblage connecting self-research and autoethnography in a digital disengagement study. <em>Methodological Innovations</em>. <a href="https://doi.org/10.1177/20597991231161093">https://doi.org/10.1177/20597991231161093</a></p> <p>As described in the paper, this dataset contains a classification of events based on two complementary approaches:</p> <p><strong>Initial categories (May 2021): </strong>Positive - Negative - Reflection</p> <p><strong>Retrospective categories and subcategories (November 2021):</strong></p> <ol> <li>Response to habit: <ul> <li>Basic impulse: looking for smartphone in pocket / “If I had it now…”</li> <li>Abstinence reaction: attempts to reconnect to “the cloud”</li> <li>Fear of missing out (FOMO) after long periods of time</li> <li>Disruption in basic routine: perception of altered habits</li> </ul> </li> <li>Social context: <ul> <li>Feelings of embarrassment: in relation to others due to feature phone</li> <li>Observing others: people using smartphones around me</li> <li>Meta level: how to share process afterwards (in community, academically)</li> </ul> </li> <li>User experience (UX) practicalities: <ul> <li>Moving around solo: orientation when commuting without GPS</li> <li>Parasitic use of other smartphones (in case of urgent need) </li> <li>Capturing things on the spot (pictures, information, feelings)</li> <li>Local stored culture (music and reading away from screens)</li> <li>Communication problems: dysfunctions in everyday coordination</li> <li>Reconnection need: whenever ending up using the smartphone again</li> </ul> </li> <li>Self-reinforcing / awareness: <ul> <li>Succeeding in intended detox: satisfaction for achievement </li> <li>Full attention to moment / stimulus: mindfulness and concentration</li> <li>Relevant changes in routine: positive reinforcement by new habit</li> </ul> </li> </ol>
Raw data for the article: A Cryptochrome adopts distinct moon- and sunlight states and functions as sun- versus moonlight interpreter in monthly oscillator entrainment
<p>The moon's monthly cycle synchronizes reproduction in countless marine organisms. The mass-spawning bristle worm Platynereis dumerilii uses an endogenous monthly oscillator to phase reproduction to specific days. Classical work showed that this oscillator is set by full moon. But how do organisms recognize such a specific moon phase? We uncover that the light receptor L-Cryptochrome (L-Cry) is able to discriminate between different moonlight durations, as well as between sun- and moonlight. Consistent with L-Cry's function as light valence interpreter, its genetic loss leads to a faster re-entrainment under artificially strong nocturnal light. This suggests that L-Cry blocks "wrong" light from impacting on the monthly oscillator. A biochemical characterization of purified L-Cry protein, exposed to naturalistic sun- or moonlight, reveals the formation of distinct sun- and moonlight states characterized by different photoreduction- and recovery kinetics of L-Cry's co-factor Flavin Adenine Dinucleotide. In vivo, L-Cry's sun- versus moonlight states correlate with distinct sub-cellular localizations, indicating different signalling. In contrast, r-Opsin1, the most abundant ocular opsin, is not required for monthly oscillator entrainment. Our work reveals a new concept for correct moonlight interpretation involving a "valence interpreter" that provides entraining photoreceptor(s) with light source and moon phase information. These findings advance our mechanistic understanding of a fundamental biological phenomenon: moon-controlled monthly timing. Such level of understanding is also an essential prerequisite to tackle anthropogenic threats on marine ecology.</p>
Interpretable Deep Learning for Probabilistic MJO Prediction: CNN Forecasts
<p>This repository contains data produced for the paper "Interpretable Deep Learning for Probabilistic MJO Prediction" by A. Delaunay and H. M. Christensen (2021).</p> <p>>> mu_ens_XX.pt<br> contains the mean forecasts from each ensemble member at a lead time of XX days</p> <p>>> cov_alea_XX.pt<br> contains the aleatoric predictions of each ensemble member at a lead time of XX days</p>
Results files from the 2022 SRBench Competition: Interpretable Symbolic Regression for Data Science
<p>Results files from the 2022 SRBench Competition: Interpretable Symbolic Regression for Data Science</p>
Supporting information for 'Towards better understanding of the ferrofluid impregnation process and potential artefacts – a prerequisite for reliable interpretation of magnetic pore fabrics'
<p>These tables contain data for the manuscript 'Towards better understanding of the ferrofluid impregnation process and potential artefacts – a prerequisite for reliable interpretation of magnetic pore fabrics'</p>
Actionable and Interpretable Fault Localization for Recurring Failures in Online Service Systems
<p>These are the datasets for our ESEC/FSE'22 paper "Actionable and Interpretable Fault Localization for Recurring Failures in Online Service Systems." In each dataset, <code>graph.yml </code>or <code>graphs/*.yml</code> are FDGs, <code>metrics.csv</code> is metrics, and <code>faults.csv</code> is failures (including ground truths).<code>FDG.pkl</code> is a pickle of the FDG object, which contains all the above data. Note that the pickle files are not compatible in different Python and Pandas versions. So if you cannot load the pickles, just ignore and delete them. They are only used to speed up data load.</p> <p>See more at https://github.com/NetManAIOps/DejaVu</p>
Data for NHESS manuscript by Biass et al. (2022): Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data
<p>This repository contains the data produced in the context of the following paper:</p> <blockquote> <p>Biass S, Jenkins SF, Aberhard WH, Delmelle P, Wilson T (2022): Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data, Accepted in NHESS</p> </blockquote> <p>Naming convention is: `run_date`_`landcover`_`impact_metrics`_`VI`_`anomaly`_test.pkl, where:</p> <ul> <li>Landcover is either crops, shrubs, herbaceous vegetation (grass), forests (trees) or all together</li> <li>Impact metrics is either minV (impact magnitude) or minT (impact duration)</li> <li>VI is the vegetation index (here, EVI)</li> <li>Anomaly is the impact indicator (here, cumulative difference index)</li> </ul> <p>Refer to the associated paper for more information on the methodology.</p> <p>Files are saved as .pkl and were generated by the <a href="https://explainerdashboard.readthedocs.io/en/latest/">explainerdashboard</a> library. They are the result of <a href="https://xgboost.readthedocs.io/en/stable/">XGBoost</a> runs that were optimised with <a href="https://optuna.org">Optuna</a> and analysed with the <a href="https://shap.readthedocs.io/en/latest/">SHAP</a> library. They contain:</p> <ol> <li>The explanatory variables and observed and computed target variables for all features</li> <li>The SHAP values</li> </ol> <p>To load the files, use <a href="https://explainerdashboard.readthedocs.io/en/latest/cli.html?highlight=load#explainerdashboard.explainers.BaseExplainer.from_file">this method</a>.</p> <p> </p>
Supplementary material to the article "Finding a way into an interpreter's heart: Methodological considerations on heart-rate variability building on an exploratory study"
<p>Supplementary material to the article "Finding a way into an interpreter's heart: Methodological considerations on heart-rate variability building on an exploratory study" by Nicoletta Spinolo, Christian Olalla-Soler & Ricardo Muñoz Martín. </p> <p>The ZIP file contains the four texts that were used in the study reported in the article. </p> <p>Reference:</p> <p>Spinolo, Nicoletta; Olalla-Soler, Christian, Muñoz Martín, Ricardo (202X). "Finding a way into an interpreter's heart: Methodological considerations on heart-rate variability building on an exploratory study". </p>
Datasets for The Dynamics of Megafire Smoke Plumes in Climate Models: Why a Converged Solution Matters for Physical Interpretations
<p>The data in this repository contains the information needed to reproduce the core results of the paper, "The Dynamics of Megafire Smoke Plumes in Climate Models: Why a Converged Solution Matters for Physical Interpretations" submitted to the Journal of Advances in Modeling Earth Systems (JAMES) on 10/3/2022.</p> <p> </p> <p>The "intsmoke*.txt" files are text files with the globally integrated smoke mass above 150 hPa for all simulations in the paper. A header is provided in each file.</p> <p>The "spectra*.mat" files are matlab files that contain fields used to plot the kinetic energy spectra for all simulations. The variable "kes" is the kinetic energy as a function of wavelength, "wvl" are the associated wavelengths of "kes". The variables "hcut" and "lcut" represent the indices of the data "kes" and "wvl" that are used to make the kinetic energy spectra plots in the paper.</p> <p>The "budget*.nc" files contain the budget terms for the relative, vertical vorticity evolution equation at the appropriate date/time. The fields in the netcdf file are self-describing. These budget terms are for the "optimal simulation" described in the paper.</p> <p>The "vort*.nc" file contains the vorticity fields just before the plume forcing starts based on the date/time on the file.</p>
Tell Me, What Do You See?—Interpretable Classification of Wiring Harness Branches with Deep Neural Networks- dataset
<p>Dataset associated with the paper "Tell Me, What Do You See?—Interpretable Classification of Wiring Harness Branches with Deep Neural Networks"</p>
Interpreting the mammal deposits of Cloggs Cave (SE Australia), GunaiKurnai Aboriginal Country, through community-led partnership research
<p>Palaeontological animal bone deposits are rarely investigated through research partnerships where the local First Nations communities have a defining hand in both the research questions asked and the research processes. Here we report research undertaken through such a partnership approach at the iconic archaeological site of Cloggs Cave (GunaiKurnai Country, East Gippsland), in the southern foothills of SE Australia's Great Dividing Range.</p> <p>A new excavation was combined with detailed chronometric dating, high-resolution 3D mapping, and geomorphological studies. This allowed for the interpretation of a sequence of stratigraphic layers spanning from a lowermost excavated mixed layer dated to between 25,640–48,470 cal BP, to a dense set of uppermost, ash layers dated to between 1460–3360 cal BP. This long and well-dated chronostratigraphic sequence enabled temporal trends in the abundant small mammal remains to be examined.</p> <p>The fossil assemblage consists of at least 31 taxa of mammals which change in proportions through time. Despite clear evidence that the Old Ancestors repeatedly carried vegetation into the cave to fuel cool fires (no visible vegetation grows in Cloggs Cave), we observed little to no evidence of cooking fires or calcined bone, suggesting that people had little involvement with the accumulation of the faunal remains. Small mammal bones were most likely deposited in the cave by large disc-faced owls, <em>Tyto novaehollandae</em> (Masked Owl) or <em>Tyto tenebricosa</em> (Sooty Owl).</p> <p>Despite being well-dated and largely undisturbed, the Cloggs Cave assemblage does not appear to track known Late Quaternary environmental change. Instead, the complex geomorphology of the area fostered a vegetation mosaic that supported mammals with divergent habitat preferences. The faunal deposit suggests a local ancestral landscape characterised by a resilient mosaic of habitats that persisted over thousands of years, signaling that the Old Ancestors burned landscape fires to encourage and manage patches of different vegetation types and ages within and through periods of climate change.</p>
Additional datasets and code accompanying the article "Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines"
<p> </p> <p> </p> <p>Datasets and code accompanying the manuscript titled “Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines” by Rajarshi Ghosh, Ninak Oak and Sharon E. Plon.</p>
Interpretation of H2‑TPR from Cu-CHA Using First-Principles Calculations
<p>Dataset related to the article with the same title and authors:</p> <p><a href="https://doi.org/10.1021/acs.jpcc.3c07998">Interpretation of H2‑TPR from Cu-CHA Using First-Principles Calculations</a></p> <p> </p> <p>The zip file contains selected structures and a README.txt file with additional information.</p>
Dataset from: "Uncertainty-Aware Interpretable Prognosis for Wave Energy Converters with Recurrent Expansion"
<p>This dataset comprises run-to-failure sensor data derived from a mathematical model simulating wave energy converter behavior, particularly for Oscillating Water Column Turbines (OWCTs). The dataset includes vibration, temperature, pressure, acceleration, strain, flow, torque, rotation, and remaining useful life (RUL) readings for one life cycle. Through normalization, the data is scaled uniformly for robust analysis and interpretation. Researchers can leverage this dataset to develop and validate their prognostic models for OWCTs.</p> <p>To cite this dataset, please refer to:</p> <p>Berghout Tarek and Benbouzid Mohamed. (2024). Uncertainty-Aware Interpretable Prognosis for Wave Energy Converters with Recurrent Expansion. SSRN, 1–22. <span><a href="https://dx.doi.org/10.2139/ssrn.4825408" target="_blank" rel="noopener"><span>http://dx.doi.org/10.2139/ssrn.4825408</span></a> </span></p>
RepliChrom: Interpretable machine learning predicts cancer-associated enhancer-promoter interactions using DNA replication timing
<p>This dataset accompanies the study "RepliChrom: Interpretable machine learning predicts cancer-associated enhancer-promoter interactions using DNA replication timing". The study introduces RepliChrom, a computational framework designed to predict enhancer–promoter interactions (EPIs) by leveraging multi-scale replication timing (RT) signals. This approach addresses the fundamental challenge of distinguishing gene targets regulated by distal enhancers from those activated by proximal transcriptional activity-a key problem in understanding the causal basis of complex diseases.</p> <p>Despite recent advances in high-throughput technologies such as Hi-C, ChIA-PET, and Hi-TrAC that allow genome-wide reconstruction of 3D chromatin architecture, the role of DNA replication timing in mediating these spatial interactions remains underexplored. RepliChrom fills this gap by using cell-type-specific RT profiles as predictive features for chromatin interaction inference.</p> <p>To support model development, training, and evaluation, we provide a comprehensive multi-omics dataset covering six human cell lines (K562, GM12878, HeLaS3, IMR90, NHEK, and HUVEC ), encompassing:</p> <p>Hi-C datasets: Processed chromatin interaction loops used to define positive and negative enhancer–promoter interaction pairs.</p> <p>ChIA-PET datasets: Interaction data anchored around transcription factor binding, including POLR2A and CTCF, used for model validation across different interaction types.</p> <p>Hi-TrAC datasets: Targeted chromatin accessibility-derived interaction data, offering complementary validation of the model on alternate platforms.</p> <p>Replication Timing (RT) data: Processed RT signal profiles for each cell line, used to extract multi-scale temporal features as inputs for RepliChrom.</p> <p>These datasets enable reproducibility of the model training process and serve as benchmark resources for future research into DNA replication–mediated regulation of 3D genome architecture.</p> <p><strong>Included Files</strong></p> <p>Hi-C_datasets.zip (2.23 MB): Processed Hi-C interaction pairs for six cell types.</p> <p>ChIA-PET_datasets.zip (818.18 KB): CTCF and POLR2A ChIA-PET interactions across multiple lines.</p> <p>Hi-TrAC_datasets.zip (196 bytes): Hi-TrAC-based chromatin interaction training datasets across multiple lines.</p> <p>Cellline_RT_data.zip (86.17 MB): Replication timing signal data across multiple human cell types for multi-scale replication timing feature extraction.</p> <p><strong>Usage</strong><br>All datasets are intended for academic, non-commercial use. The provided files can be directly used to reproduce the training and evaluation of RepliChrom, and may also support broader applications in enhancer–promoter modeling, replication-timing analysis, and 3D genomics studies. For detailed usage instructions and code implementation, please refer to the GitHub repository: https://github.com/DaoFuying/RepliChrom</p>
FIG. 1 in Quantifying and interpreting nestedness of bryophytes in the Zhoushan Archipelago, China
FIG. 1. — Location and map of 66 islands in the Zhoushan Archipelago (Yu et al. 2019).
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.