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7,195 results for “SET”
AG-PT-set: Acoustic Guitar Playing Technique dataset
<p>This is the Acoustic Guitar Playing Technique dataset (AG-PT-set).<br>It contains <strong>15 hours and 55 minutes</strong> of monophonic recordings of <strong>12</strong> expressive guitar playing techniques (pitched and percussive). <br>Of these, <strong>10 hours and 4 minutes</strong> of recordings encompassing 8 of the 12 techniques have been labeled, meaning that onsets were identified at the millisecond level and their timestamp was saved, alongside playing technique information.<br>As a result, <strong>32,592</strong> individual notes have been labeled.<br>Recordings were performed by <strong>6 players</strong> on <strong>7</strong> different acoustic steel-string <strong>guitars</strong>.</p> <p>The techniques are the following:</p> <ol> <li><strong>Kick technique</strong> (<em>*percussive*</em>): producing a sound that resembles a kick drum by hitting the lower right part of the top of the guitar body;</li> <li><strong>Snare-A technique </strong>(<em>*percussive*</em>): producing a sound by hitting the lower right side of the guitar body;</li> <li><strong>Tom technique</strong> (<em>*percussive*</em>): producing a sound by hitting the area of the guitar body near the top of the end of the fretboard, using the thumb;</li> <li><strong>Snare-B technique</strong> (<em>*percussive*</em>): producing a sound by hitting the muted strings over the end of the fretboard;<br> </li> <li><strong>Natural Harmonics</strong> (pitched): plucking the strings while lightly touching the string with the fretting finger (i.e., not pressing the string fully), therefore letting only some harmonic overtones ring;</li> <li><strong>Palm Mute</strong>: partially muting the strings with the palm of the picking hand, resulting in a muffled sound.</li> <li><strong>Pick Near Bridge</strong> (pitched): plucking the string near the guitar bridge, producing sounds with great high-frequency content;</li> <li><strong>Pick Over the Soundhole</strong> (pitched): plucking the string over the soundhole, producing sounds with lower treble content and greater intensity;</li> <li><strong>Bending technique</strong> (pitched): pulling the strings, raising the pitch (half-tone interval);</li> <li><strong>Hammer-on technique</strong> (pitched): sharply bringing a finger down onto the fingerboard, creating a legato sound (half-tone interval);</li> <li><strong>Staccato</strong> (pitched): playing short notes;</li> <li><strong>Vibrato</strong> (pitched): Moving the fretting finger to warp the pitch and tone of the sound.</li> </ol> <p>Techniques 1 through 8 have been meticulously labeled.</p> <p> </p> <p>This dataset is presented in detail in the following conference paper:</p> <div>D. Stefani, G. A. Giudici, and L. Turchet. 2024. <strong>On the Importance of Temporally Precise Onset Annotations for Real-Time Music Information Retrieval: Findings from the AG-PT-set Dataset</strong>. In Proceedings of the 19th International Audio Mostly Conference: Explorations in Sonic Cultures (AM '24). </div> <p><a href="https://doi.org/10.1145/3678299.3678325">https://doi.org/10.1145/3678299.3678325</a></p>
Data Sets "Living Kombucha Electronics with Proteinoids"
<p>The data presents the electrical oscillations observed in Kombucha-proteinoid solutions with compositions of 40:60% (v/v) and 25:75% (v/v). </p>
data sets supporting information about rime-splintering experiments
<p>The uploaded data are related to the publication: ' Secondary Ice Production - No Evidence of Efficient Rime-Splintering Mechanism during Dry and Wet Rimer Growth' by Seidel et al. in the journal Atmospheric Chemistry and Physics.</p> <p>Droplet size distributions can be found in DSD.csv. Information on the individual secondary ice production experiments are provided by SIP_experiments_valid.csv. The descriptive information of the parameters can be found in the respective read_me file.</p>
Supporting data sets for "Evolutionary analyses of IDRs reveal widespread signals of conservation"
<p>S1: Disorder and order regions of alignments computed from AUCPreD scores which passed the 30 residue minimum length and phylogenetic diversity filters. The "ppids" column indicates the sequences in each alignment which were not excluded due these and other filtering criteria. See the main text methods for more details.<br><br>S2: Substitution model parameters fit to meta-alignments derived from disorder and order filtered regions.<br><br>S3: Features of segments in all regions.<br><br>S4: Contrasts and root estimates of disorder scores in filtered regions computed with Felsenstein's contrasts algorithm.<br><br>S5: Contrasts and root estimates of features in filtered regions computed with Felsenstein's contrasts algorithm.<br><br>S6: Estimated BM and OU model parameters of simulated data.<br><br>S7: Estimated BM and OU model parameters of features in filtered regions computed with maximum likelihood.</p><p> </p>
Data Sets ''Kombucha--Proteinoid Biosynthetic Classifiers of Audio Signals''
<p>These voltage signals represent the electrical response generated by the Kombucha-Proteinoid biosynthetic system when exposed to various audio signals. The analysis of this voltage signal data contributes to the understanding and development of novel audio classification methods based on organic systems.</p>
Data set of dorsal halfed shield outlines of specimens of Meiura, from the publication "Morphological diversity in true and false crabs reveals a common middle ground – the megalopa phase"
<p>Data set, containing all reconstructed shield shapes, all from representatives of Meiura, included in the analysis of the manuscript "Morphological diversity in true and false crabs reveals a common middle ground – the megalopa phase". The data sheet, detailing data origin can be found in the supplementarty material of the publication.</p>
IW-NET sample data-set: River Weser IW network
<p>This data-set provides a sample of the data that was utilized for the analysis performed in the context of the IW-NET research project. The data have been collected via publicly available sources and are offered in this package as a sample. The use case the data refer to is River Weser, in northern Germany. The following table explains the contents of each of the uploaded files.</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><a href="../api/records/10391858/draft/files/RiverWeserWaterway.json/content" target="_blank" rel="noopener noreferrer">RiverWeserWaterway.json</a></td> <td>OpenStreetMap data describing the Weser region</td> </tr> <tr> <td><a href="../api/records/10391858/draft/files/RiverWeserRelationsWaysNodes.json/content" target="_blank" rel="noopener noreferrer">RiverWeserRelationsWaysNodes.json</a></td> <td>OpenStreetMap data describing the Weser region</td> </tr> <tr> <td> <div><a href="../api/records/10391858/draft/files/IWTWeather.json/content" target="_blank" rel="noopener noreferrer">IWTWeather.json</a></div> </td> <td>Weather reports from 6 stations in the Weser region</td> </tr> <tr> <td><a href="../api/records/10391858/draft/files/unCitiesDE.json/content" target="_blank" rel="noopener noreferrer">unCitiesDE.json</a></td> <td>UN/LOCODE data in json format.</td> </tr> <tr> <td> <div><a href="../api/records/10391858/draft/files/MMSI.xlsx/content" target="_blank" rel="noopener">MMSI.xlsx</a></div> </td> <td>Correspondance of MMSI codes to Vessel registration country</td> </tr> </tbody> </table> <p>These resources were combined with AIS data logs from vessels active in the area - which, for legal reasons, cannot be made publicly available to provide powerful insights into the logistics operations and their intricacies.</p>
Data set for "Drought response of the boreal forest carbon sink is driven by understory-tree composition"
<p>This data set is a compilation of 1) environmental conditions, 2) biometric- and chamber-based annual CO<sub>2</sub> fluxes, 3) vegetation phenological greenness, and 4) forest-floor environmental conditions, all measured over the Krycklan Catchment Study (KCS, <a href="https://www.slu.se/Krycklan">https://www.slu.se/Krycklan</a>), a multi-scale long-term monitored boreal catchment spanning 68 km<sup>2</sup> in northern Sweden.</p> <p>The environmental measurements cover the period 1991–2020. Specifically, meteorological conditions measured close to the central part of the KCS at the Svartberget reference climate station (64°14′N, 19°46′E, 225 m.a.s.l.) included air temperature at 1.7 m above ground (Ta, °C), global radiation at 1.7 m above ground (Rg, MJ m<sup>-2</sup>), and precipitation (P, mm). Drought conditions were characterized by the Standardized Precipitation Evapotranspiration Index (SPEI) computed at 3-month time scale. SPEI was retrieved from the 0.5° gridded dataset supplied in the Global SPEI Database (SPEIbase v2.8, <a href="https://spei.csic.es/database.html">https://spei.csic.es/database.html</a>). The data set comprises monthly values obtained during the long-term reference period 1991–2020 (LT<sub>91–20</sub>), the baseline period 2016–2017 (BL<sub>16–17</sub>), and the drought year 2018 (D<sub>18</sub>). The standardized anomaly (ɀ-score) was used to identify extreme environmental measurements during both the BL<sub>16–17 </sub>and D<sub>18</sub> periods relative to the LT<sub>91–20 </sub>period.</p> <p>Annual CO<sub>2</sub> flux estimates were collected in 50 forest stands located across the KCS during the period 2016–2018 using biometric- and chamber-based methods. However, to prevent confounding effects, one forest stand that was subjected to thinning operations in spring 2018 was excluded from the analysis. The selected forest stands encompassed different landscape attributes such as 1) soil type (i.e., sediment and till), 2) dominant tree species (i.e., pine and spruce), and 3) stand age classes (i.e., initiation, young, middle-aged, mature, and old-growth stands). The annual CO<sub>2</sub> fluxes included the net ecosystem production (NEP) and its component fluxes, i.e., net primary production (NPP), total heterotrophic respiration (RH), net primary production of trees (NPP<sub>t</sub>) and its above- and belowground components (ANPP<sub>t</sub> and BNPP<sub>t</sub>, respectively), and net primary production of understory (NPP<sub>u</sub>) and its above- and belowground components (ANPP<sub>u</sub> and BNPP<sub>u</sub>, respectively). The impact of drought on annual CO<sub>2</sub> fluxes was evaluated by calculating both the absolute and relative anomalies (∆X and δX, respectively) of D<sub>18</sub> relative to BL<sub>16–17</sub>. To identify the temporal shift of the dominant contributor to ∆NEP, a moving-window correlation was conducted between the absolute anomaly of NEP (∆NEP) and the absolute anomalies of understory and tree NPP (∆NPP<sub>u</sub> and ∆NPP<sub>t</sub>, respectively), using a 7-forest-stand window with 1-forest-stand step.</p> <p>The study assessed the phenological greenness of the understory and trees in a ⁓110 years-old mixed-species forest stand in the central part of the KCS from 2016 to 2018. The greenness index (gcc) was derived from hourly images collected through digital repeat photography at the Integrated Carbon Observation System (ICOS) Svartberget ecosystem station (SE-Svb, 64°15′N, 19°46′E, 270 m.a.s.l., <a href="https://www.icos-sweden.se/svartberget">https://www.icos-sweden.se/svartberget</a>). Web cameras were used to capture images below- and above-tree canopy to define the gcc index for understory (gcc<sub>u</sub>) and trees (gcc<sub>t</sub>), respectively. The gcc<sub>u</sub> and gcc<sub>t</sub> values were then normalized (0–1) to describe the seasonal minimum and maximum of vegetation biomass development. A locally estimated scatterplot smoothing (loess) curve fit was then used through the normalized data points to improve visualization. The impact of drought on mean estimates of gcc<sub>u</sub> and gcc<sub>t</sub> during the growing season was evaluated by calculating the absolute and relative anomalies (∆X and δX, respectively) of D<sub>18</sub> relative to BL<sub>16–17</sub>.</p> <p>Environmental conditions at the forest-floor interface were measured in each of the 50 forest stands located across the KCS during the period 2016–2018. As before, one forest stand that was subjected to thinning operations in spring 2018 was excluded from the analysis to prevent confounding effects. The measured conditions included the below-canopy air temperature (Ta<sub>bc</sub>, °C), soil temperature at 10 cm depth (Ts, °C), and soil volumetric water content at 5 cm depth (SWC, %). The data set includes mean monthly and mean May-August values estimated during the BL<sub>16–17</sub> and D<sub>18</sub> periods, for which the absolute and relative anomalies (∆X and δX, respectively) were calculated.</p> <p>This data set consists of four Microsoft Excel workbooks:</p> <p>1_dataset_environmental_conditions.xlxs</p> <p>2_dataset_biometric_&_chamber-based_CO2_fluxes.xlxs</p> <p>3_dataset_vegetation_phenological_greenness.xlxs</p> <p>4_dataset_forest-floor_environmental_conditions.xlxs</p> <p>Further details can be found in Martínez-García et al. “Drought response of the boreal forest carbon sink is driven by understory-tree composition” (Nature Geoscience, <a href="https://doi.org/10.1038/s41561-024-01374-9" target="_blank" rel="noopener noreferrer">https://doi.org/10.1038/s41561-024-01374-9</a>).</p> <p>Contact information:</p> <p>Ph.D. Eduardo Martínez García<sup>1,2</sup> (<a href="mailto:eduardo.martinez@slu.se">eduardo.martinez@slu.se</a>, <a href="eduardo.martinezgarcia@luke.fi">eduardo.martinezgarcia@luke.fi</a>, <a href="mailto:edu.martinez.garcia@gmail.com">edu.martinez.garcia@gmail.com</a>)</p> <p>Professor Matthias Peichl<sup>1</sup> (<a href="mailto:matthias.peichl@slu.se">matthias.peichl@slu.se</a>)</p> <p><sup>1</sup> Department of Forest Ecology and Management, Swedish University of Agricultural Sciences (SLU), Skogsmarksgränd 17, SE-901 83, Umeå, Sweden</p> <p><sup>2</sup> Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790, Helsinki, Finland</p>
Data set for "Distributed and specific encoding of sensory, motor and decision information in the mouse neocortex during goal-directed behavior"
<p>Data set for: Oryshchuk A, Sourmpis C, Weverbergh J, Asri R, Esmaeili V, Modirshanechi A, Gerstner W, Petersen CCH, Crochet S (2024) Distributed and specific encoding of sensory, motor and decision information in the mouse neocortex during goal-directed behavior. Cell Reports 43: 113618. https://doi.org/10.1016/j.celrep.2023.113618</p> <p> </p> <p>There are 2 files in this upload:</p> <p> </p> <p>1. The file named "2024_Oryshchuk_CellReports.pdf" is the Open Access pdf of the online publication in Cell Reports.</p> <p> </p> <p>2. The file named " Oryshchuk _data_code.zip" (~1.8 GB) is a zipped version of a folder "Oryshchuk _data_code" (~2.3 GB), which contains the preprocessed data analyzed in the study along with the Matlab and Python codes used to generate the published figures. To access the data and codes, first unzip the file.</p> <p>· The subfolder “Atlas” contains templates from the Allen Mouse Brain Reference Altas of anatomical brain sections used to map the location of the silicon probes (Supplementary Figure S1).</p> <p>· The subfolder “Clustering-master” contains the Matlab codes used for the clustering on neuronal activity (Figure 1). The output is the data structure ‘Data_Clustering.mat’ file already provided in the folder ‘Data’.</p> <p>· The subfolder “Code” contains the main Matlab codes used to analyze the data and plot the figures. The ouput from the clustering and decoding analyses are provided in the ‘Data’ folder, thus the Matlab codes can be run independently, without running the ‘clustering’ or ‘decoding’ codes first.</p> <p>· The subfolder “Data” contains the Matlab data structures containing the electrophysiological and behavioral data from whisker rewarded (‘DataWR.mat’) and non-rewarded (‘DataWnonR.mat’) mice, the behavioral data for optogenetic inactivation in rewarded mice, the clustering results (‘Data_Clustering.mat’) and a subfolder containing the results from the decoding analyses (“Decoding”).</p> <p>· The subfolder “decoding” contains the Python codes used for the decoding analyses. The required configuration can be found in the file ‘requirements.txt’. To run the codes, follow instructions from the ‘README.md’ file.</p> <p>· The subfolder “Figures” will be populated with figures saved in .png and .eps formats as well as a ‘Methods.txt’ files when running the main Matlab codes.</p> <p>· The subfolder “Functions” contains subfunctions used by the main Matlab codes to analyze the data and plot the figures.</p> <p>· The subfolder “Results” will be populated with Matlab data structures as well as a ‘.xlsx’ files when running the main Matlab codes.</p> <p>When running the code, you need to set the Matlab file path to be "Oryshchuk _data_code". In addition, you should add the folder "Oryshchuk_data_code" with subfolders to the Matlab path. Some parts of the code rely upon previous results, and need to be executed sequentially in the order of the figure panels in the journal publication. Please note that some of the code can take several hours to execute.</p>
Data Sets: Unsteady Land-Sea Breeze Circulations in the Presence of a Synoptic Pressure Forcing
<p>{Mg (m/s): 0, 0.4, 1.2, 2 and α: 0°, and 180°}</p> <p>Consult the details in Allouche et al. (2023): https://doi.org/10.1002/qj.4552. The latter corresponds to the steady state simulations of these transient ones here.<br>All of these simulations have a domain extent (L_x, L_y, L_z=z_i) of (80 km, 5 km, 1.6 km). The numerical mesh (Nx, Ny, Nz) is (384, 24, 64). For plotting, the vertical levels vary for each variable. A variable at 'c_s nodes' is plotted at dz/2, dz, 2*dz, 3*dz, and so forth up to z_i. A variable on 'w nodes' is plotted at 0, dz, 2*dz, 3*dz, and so forth up to z_i. This information is given in the variable table below.</p> <p>These simulations are given in a netcdf format (.nc). One is able to download and reshape these matrices.</p>
ClimateAnalyzer: set of scripts to delimit regions based on bioclimatic variables
<p>Habitat stability is important for maintaining biodiversity by preventing species extinction, but this stability is being challenged by climate change. The tropical alpine ecosystem is currently one of the ecosystems most threatened by global warming, and the flora close to the permanent snow line is at high risk of extinction. The tropical alpine ecosystem, found in South and Central America, Malesia and Papuasia, Africa, and Hawaii, is of relatively young evolutionary age, and it has been exposed to changing climates since its origin, particularly during the Pleistocene. Estimating habitat loss and gain between the Last Glacial Maximum (LGM) and the present allows us to relate current biodiversity to past changes in climate and habitat stability. In order to do so, 1) we developed a unifying climate-based delimitation of tropical alpine regions across continents, and 2) we used this delimitation to assess the degree of habitat stability, i.e. the overlap of suitable areas between the LGM and the present, in different tropical alpine regions. Finally, we discuss the link between habitat stability and tropical alpine plant diversity. Our climate-based delimitation approach can be easily applied to other ecosystems using our developed code, facilitating macro-comparative studies of habitat dynamics through time.</p>
ACHILLES: Ancient and Historical Language Evaluation Set
<p>The dataset used in the <a href="https://sigtyp.github.io/st2024.html">SIGTYP 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages</a>. The task included four problems; problems 1-3 were offered in both <a href="https://codalab.lisn.upsaclay.fr/competitions/16822">constrained</a> and <a href="https://codalab.lisn.upsaclay.fr/competitions/16818">unconstrained</a> tracks on CodaLab, while problem 4 was only a part of the unconstrained track.</p> <ol> <li>POS-tagging</li> <li>Lemmatisation</li> <li>Morphological feature prediction</li> <li>Mask filling <ul> <li>Word-level</li> <li>Character level</li> </ul> </li> </ol> <p>For problems 1-3, data from <a href="http://hdl.handle.net/11234/1-5150">Universal Dependencies v.2.12</a> was used for Ancient Greek, Ancient Hebrew, Classical Chinese, Coptic, Gothic, medieval Icelandic, Latin, Old Church Slavonic, Old East Slavic, Old French and Vedic Sanskrit. Old Hungarian texts, annotated to the same standard as UD corpora, were added to the dataset from the <a href="http://oldhungariancorpus.nytud.hu/en-codices.html">MGTSZ website</a>. In Old Hungarian data, tokens which were POS-tagged PUNCT were altered so that the form matched the lemma to simplify complex punctuation marks used to approximate manuscript symbols; otherwise, no characters were changed.</p> <p>As the ISO 639-3 standard does not distinguish between historical stages of Latin, as it does between other languages like Irish, but it was desirable to approximate this distinction for Latin, we further split Latin data. This resulted in two Latin datasets: Classical and Late Latin, and Medieval Latin. This split was dictated by the composition of the <a href="http://perseusdl.github.io/treebank_data/">Perseus</a> and<a href="http://dev.syntacticus.org/proiel.html"> PROIEL</a> treebanks that served as a source for Latin UD treebanks.</p> <p>Historical forms of Irish were only included in mask filling challenges (problem 4), as the quantity of historical Irish text data which has been tokenised and annotated to a single standard to date is insufficient for the purpose of training models to perform morphological analysis tasks. The texts were drawn from <a href="https://celt.ucc.ie/publishd.html">CELT</a>, <a href="http://corpas.ria.ie/index.php?fsg_function=1">Corpas Stairiúil na Gaeilge</a>, and digital editions of the <a href="http://www.stgallpriscian.ie/">St. Gall glosses</a> and the <a href="https://wuerzburg.ie/">Würzburg glosses</a>. Each Irish text taken from CELT is labelled "Old", "Middle" or "Early Modern" in accordance with the language labels provided in CELT metadata. Because CELT metadata relating to language stages and text dating is reliant on information provided by a variety of different editors of earlier print editions, this metadata can be inconsistent across the corpus and on occasion inaccurate. To mitigate complications arising from this, texts drawn from CELT were included in the dataset only if they had a single Irish language label and if the dates provided in CELT metadata for the text match the expected dates for the given period in the history of the Irish language.</p> <p>The upper temporal boundary was set at 1700 CE, and texts created later than this date were not included in the dataset. The choice of this date is driven by the fact that most of the historical language data used in word embedding research dates back to the 18th century CE or later, and our intention was to focus on the more challenging and yet unaddressed data. The resulting datasets for each language were then shuffled at the sentence level and split into training, validation and test subsets at the ratio of 0.8 : 0.1 : 0.1.</p> <p>A <a href="https://github.com/sigtyp/ST2024/blob/main/list_of_text_sources.md">detailed list of text sources</a> for each language in the dataset, as well as <a href="https://github.com/sigtyp/ST2024/tree/main#data">other metadata</a> and the <a href="https://github.com/sigtyp/ST2024/tree/main#data-format-for-tasks-1-3">description of data formats</a> used for each problem, is provided on the <a href="https://github.com/sigtyp/ST2024/">Shared Task's GitHub</a>. The structure of the dataset is as follows:</p> <pre>📂 <strong>morphology</strong> <em>(data for problems 1-3)</em> ├── 📂 test<br> ├── 📂 ref <em>(reference data used in CodaLab competitions)</em><br> ├── 📂 lemmatisation<br> ├── 📂 morph_features<br> └── 📂 pos_tagging<br> └── 📂 src <em>(source test data with labels)</em> ├── 📂 train └── 📂 valid<br><br>📂 <strong>fill_mask_word</strong> <em>(data for problem 4a)</em><br> ├── 📂 test<br> ├── 📂 ref <em>(reference data used in CodaLab competitions)</em><em><br> └── </em>📂 src<em> (source test data with labels in 2 different formats)<br> ├── </em>📂 json<br> └── 📂 tsv<br> ├── 📂 train <em>(train data in 2 different formats)</em><br><em> ├── </em>📂 json<br> └── 📂 tsv<br> └── 📂 valid <em>(validation data in 2 different formats)</em><br><em> ├── </em>📂 json<br> └── 📂 tsv<br><br>📂 <strong>fill_mask_char </strong>(data for problem 4b) </pre> <pre> ├── 📂 test<br> ├── 📂 ref <em>(reference data used in CodaLab competitions)</em><em><br> └── </em>📂 src<em> (source test data with labels in 2 different formats)<br> ├── </em>📂 json<br> └── 📂 tsv<br> ├── 📂 train <em>(train data in 2 different formats)</em><br><em> ├── </em>📂 json<br> └── 📂 tsv<br> └── 📂 valid <em>(validation data in 2 different formats)</em><br><em> ├── </em>📂 json<br> └── 📂 tsv<br><br></pre> <p>We would like to thank Ekaterina Melnikova for suggesting the name for the dataset.</p>
The AFFECT-HRI data set: physiological data for affective computing in human-robot interaction with anthropomorphic service robots
<p>We provide a comprehensive data set <strong>AFFECT-HRI </strong>containing physiological data labeled with human affect (i.e., mood and emotion) gathered during an empirical study consisting of a complex human-robot interaction (HRI). A realistic retail scenario served as an experimental environment. In prior research, we showed the necessity to combine the expertise of the research fields of psychology, computer science, and law in the design of a responsible human-centered HRI. Therefore, we implemented five conditions (neutral, transparency, liability, moral, and immoral) covering the perspectives from these three research fields and used two different anthropomorphic service robots. Our study followed a multi-method approach, resulting in a data set containing and combining objective physiological sensor data with subjective human-affect assessments. Additionally, the data set includes insights from 146 participants regarding affect, demographics, and socio-technical questionnaire ratings, as well as robot gestures and robot speech. Our study can be split into three scenes: a consultation regarding products, a request for sensitive personal information while opening a customer account, and a successful or failing handover when buying a mold remover. Thus, this data set offers for the first time the possibility to prove established or develop new emotion recognition methods and technological capabilities for HRI. Further, our data set provides the possibility to combine affective computing with research about robot behavior (gestures, speech, and handover), liability (questionnaire), transparency (questionnaire), and psychological aspects, allowing an encompassing, human-centered view of HRI.</p> <p>The detailed data descriptor has been published in Nature Scientific Data. For more details on the data set, please check the paper below.</p> <p><strong>Please cite the following paper if the dataset is used in a publication:</strong><br>Heinisch, J.S., Kirchhoff, J., Busch, P. <em>et al.</em> Physiological data for affective computing in HRI with anthropomorphic service robots: the AFFECT-HRI data set. <em>Sci Data</em> <strong>11</strong>, 333 (2024). https://doi.org/10.1038/s41597-024-03128-z</p> <p><strong>Acknowledgements</strong><br>This research was conducted as part of RoboTrust, a project of the Centre Responsible Digitality, supported by the Hessian Minister for Digital Strategy and Innovation. The authors would like to thank all participants for their participation in the study. We particularly want to thank Ruth Stock-Homburg for her support and for making Elenoide available. Further, we want to thank Mona Kegel, Vignesh Prasad, and all the research assistants who supported the study. We also thank the leap in time lab for serving as study location. A special thanks goes to Amer Altizini, who supported us by helping to prepare the data for publication. We want to thank Niklas Jungermann for his valuable comments on the statistical evaluation.</p>
Real testing sets for Visual Affordance Segmentation of hand-occluded objects
<p>[<a href="https://arxiv.org/abs/2308.11233">arXiv</a>] [<a href="https://apicis.github.io/projects/acanet.html">webpage</a>] [<a href="https://github.com/SEAlab-unige/acanet">code</a>] [<a href="https://doi.org/10.5281/zenodo.8364197">trained model</a>][<a href="https://doi.org/10.5281/zenodo.5085800">mixed-reality data</a>]</p> <p>RGB images with the corresponding affordance annotation to test affordance segmentation models. Images are selected from two datasets for hand-object pose estimation: <a href="https://www.tugraz.at/institute/icg/research/team-lepetit/research-projects/hand-object-3d-pose-annotation/">HO-3D</a> and <a href="https://corsmal.eecs.qmul.ac.uk/containers_manip.html">CCM</a>.</p> <p>For HO3D we selected 150 frames from the dataset and enriched the annotation of the hand and object segmentation masks with new annotations specific for the affordance segmentation problem.</p> <p>For CCM we selected 150 frames from the dataset and created the annotation specific for the affordance segmentation problem. The forearms and hands in contact with the offered container are annotated. </p> <p>File names are formatted as: <em><videoname>_<framenumber>.png</em></p> <p>Segmentation classes values:</p> <ul> <li> 0: background</li> <li> 1: graspable</li> <li> 2: contain</li> <li> 3: arm</li> </ul> <p> </p> <p><strong>References. </strong></p> <p><strong>Affordance segmentation of hand-occluded containers from exocentric images</strong><br>T. Apicella, A. Xompero, E. Ragusa, R. Berta, A. Cavallaro, P. Gastaldo<br>IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2023</p> <pre><code>@inproceedings{apicella2023affordance, title={Affordance segmentation of hand-occluded containers from exocentric images}, author={Apicella, Tommaso and Xompero, Alessio and Ragusa, Edoardo and Berta, Riccardo and Cavallaro, Andrea and Gastaldo, Paolo}, booktitle={IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)}, year={2023}, } </code></pre> <p><strong>HOnnotate: A method for 3D Annotation of Hand and Objects Poses<br></strong>S. Hampali, M. Rad, M. Oberweger, V. Lepetit<strong><br></strong>IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020</p> <pre><code>@inproceedings{hampali2020honnotate, title={Honnotate: A method for 3d annotation of hand and object poses}, author={Hampali, Shreyas and Rad, Mahdi and Oberweger, Markus and Lepetit, Vincent}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={3196--3206}, year={2020} }</code></pre> <p><strong>CORSMAL Containers Manipulation (1.0) [Data set]</strong><br>A. Xompero, R. Sanchez-Matilla, R. Mazzon, and A. Cavallaro<br>Queen Mary University of London. <a href="https://doi.org/10.17636/101CORSMAL1"><u>https://doi.org/10.17636/101CORSMAL1</u></a></p> <p> </p> <p><strong>License. </strong>Creative Commons<strong> </strong>Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)</p> <p><strong>Enquiries, Question and Comments. </strong>For enquiries, questions, or comments, please contact <a href="mailto:tommaso.apicella@edu.unige.it">Tommaso Apicella</a>.</p>
Pasillo and Sanjuanito Performance Data Set
<div> <p>These data sets contain timbre, tempo, harmonic complexity, tonal complexity and instrumentation data from a corpus of 74 recordings of Ecuadorian <em>pasillos</em> and <em>sanjuanitos</em>. The recordings span the years 1949 to 2023, and consist of ‘standards’ that have been performed and recorded consistently during the last 73 years. These data sets were produced for a research project investigating the stylistic changes over time in these two genres of Ecuadorian popular music, which, according to musicological consensus, have great symbolical and cultural importance in this country.</p> </div>
CD crossdock benchmark set for DiffBindFR
<p>The CD benchmark set represents a comprehensive, large-scale benchmark dataset specifically designed for cross-docking evaluations in DiffBindFR paper. It is uniquely equipped to handle a variety of cross-docking scenarios, including Apo-Holo, Holo-Holo, and AF2-Holo cross-docking. The dataset, as released, encompasses several subsets: Ensemble (including three targets: CDK2, EGFR, and FXA), ApoRef, CASF2016, GPCR-AF2, and DUDE27-HoloEns. Each cross-docking system within these subsets is comprehensively equipped with a well prepared receptor PDB file, ligand SDF file, and Mol2 file. Every system has been assigned a unique ID with the format of [Lig ID][Alt ID]_[Apo PDB ID]_[Cross-Docking ID], and Cross-Docking ID can help users identify cross-docking scenario.</p>
dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning
<p>dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning</p>
Train and Evaluation Code, Road Classification Models and Test set of the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification"
<p>This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road classification models corresponding to the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification". The scripts make use of the Tensorflow with Keras framework and the additional required dependencies.</p> <p>The training and validation set is based on the binary SROADEX dataset (https://zenodo.org/records/6482346) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 546 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area of 28.5 km * 18.5 km and features binary road labels. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study.</p> <p>The structure of the information shared in this repository is as follows:<br>The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders.</p> <p>The material is distributed under a CC-BY 4.0 license.</p>
Weekly noise estimate of the residual of the LDC2a data set
<p>Weekly noise estimate of the residual of the LDC2a data set. The recovered Galactic binaries and massive black hole binaries are subtracted for each week.</p>
Complete PM Compositional Data Set from Kevo, Finland
<p>These are the complete set of chemical composition data from weekly samples collected in Kevo Finland from October 1964 to December 2010</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.