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528 results for “correspondence”
Data from: Matrix correspondence tests on the DNA phylogeny of the Tenerife lacertid elucidate both historical causes and morphological adaptation
Previous studies using partial regression Mantel tests of matrix correspondence on within-island geographic variation in the color pattern of the Tenerife (Canary Islands) lacertid lizard (Gallotia galloti) support natural selection for different north--south climatically determined biotopes but do not support any historical cause. However, tests on the DNA phylogeny based primarily on population data from 57 localities on Tenerife support the hypothesis that there were populations on two putative precursor islands that have come into secondary contact and introgressed after these islands were joined to form Tenerife by the eruption of the Canadas edifice. Subsequent partial Mantel tests continue to support the hypothesis that color pattern is adapted to the climatic biotopes even when this phylogenetic information is taken into account by (1) testing for color pattern adaptation separately within each lineage and (2) testing for color pattern adaptation across the entire island while considering the molecular phylogenetic relationships as representing an alternative explanation. Selection has largely expunged any trace of the geological history from current morphological variation, and the introgression of these island populations after an estimated 0.7 million years of separation gives an insight into the relationships between allopatric divergence and reproductive isolation.
Data from: Study on the optimization of the deposition rate of planetary GaN-MOCVD films based on CFD simulation and the corresponding surface model
Metal-organic chemical vapour deposition (MOCVD) is a key technique for fabricating GaN thin film structures for light-emitting and semiconductor laser diodes. Film uniformity is an important index to measure equipment performance and chip processes. This paper introduces a method to improve the quality of thin films by optimizing the rotation speed of different substrates of a model consisting of a planetary with seven 6-inch wafers for the planetary GaN-MOCVD. A numerical solution to the transient state at low pressure is obtained using computational fluid dynamics. To evaluate the role of the different zone speeds on the growth uniformity, single factor analysis is introduced. The results show that the growth rate and uniformity are strongly related to the rotational speed. Next, a response surface model was constructed by using the variables and the corresponding simulation results. The optimized combination of the matching of different speeds is also proposed as a useful reference for applications in industry, obtained by a response surface model and genetic algorithm with a balance between the growth rate and the growth uniformity. This method can save time, and the optimization can obtain the most uniform and highest thin film quality.
Data from: A genetic discontinuity in moose (Alces alces) in Alaska corresponds with fenced transportation infrastructure
The strength and arrangement of movement barriers can impact the connectivity among habitat patches. Anthropogenic barriers (e.g. roads) are a source of habitat fragmentation that can disrupt these resource networks and can have an influence on the spatial genetic structure of populations. Using microsatellite data, we evaluated whether observed genetic structure of moose (Alces alces) populations were associated with human activities (e.g. roads) in the urban habitat of Anchorage and rural habitat on the Kenai Peninsula, Alaska. We found evidence of a recent genetic subdivision among moose in Anchorage that corresponds to a major highway and associated infrastructure. This subdivision is most likely due to restrictions in gene flow due to alterations to the highway (e.g. moose-resistant fencing with one-way gates) and a significant increase in traffic volume over the past 30 years; genetic subdivision was not detected on the Kenai Peninsula in an area not bisected by a major highway. This study illustrates that anthropogenic barriers can substructure wildlife populations within a few generations and highlights the value of genetic assessments to determine the effects on connectivity among habitat patches in conjunction with behavioral and ecological data.
Huntingtin linker sequence determination by computational methods - correspondence with Alex Holehouse
<p>Huntingtin open lab notebook project</p>
FIGURE 13 in Fieber's original drawings and their corresponding types for the family Issidae (Hemiptera, Fulgoromorpha) in the Muséum national d'Histoire naturelle of Paris, France
FIGURE 13—Tshurtshurnella striolata (Fieber, 1877), male genitalia: A, penis, lateral view; B, anal tube, lateral view.
FIGURE 9 in Fieber's original drawings and their corresponding types for the family Issidae (Hemiptera, Fulgoromorpha) in the Muséum national d'Histoire naturelle of Paris, France
FIGURE 9—Thabena fissala (Fieber, 1876) (as FIGURE 11—Tshurtshurnella striolata (Fieber, "fissala"). 1877) (as "striolatum").
FIGURE 4 in Fieber's original drawings and their corresponding types for the family Issidae (Hemiptera, Fulgoromorpha) in the Muséum national d'Histoire naturelle of Paris, France
FIGURE 4—Latissus dilatatus (Fourcroy, 1785) FIGURE 8—Palmallorcus phaeophleps (Fieber, (as "luteus Fieb"). 1877) (as "phaeophleps").
FIGURE 3 in Fieber's original drawings and their corresponding types for the family Issidae (Hemiptera, Fulgoromorpha) in the Muséum national d'Histoire naturelle of Paris, France
FIGURE 3—Issus muscaeformis (Schrank, 1781) FIGURE 7—Bubastia obsoleta (Fieber, 1877) (as (as "truncatus Fieb"). "H. obsoletum Fieb").
The EEG and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech
<p>One of the key features of human interpersonal communication is our ability to integrate information communicated by speech and accompanying gestures. However, it is still not fully understood how this essential combinatory process is represented in the human brain. Functional magnetic resonance imaging (fMRI) studies have unanimously attested the relevance of activation in the posterior superior temporal sulcus/middle temporal gyrus (pSTS/MTG), while electroencephalography (EEG) studies have shown oscillatory activity in specific frequency bands to be associated with multisensory integration. In the current study, we used fMRI and EEG to separately investigate the anatomical and oscillatory neural signature of integrating intrinsically meaningful gestures (IMG; e.g. “Thumbs-up gesture”) and corresponding speech (e.g., “The actor did a good job”). In both the fMRI (<em>n</em>=20) and EEG (<em>n</em>=20) study, participants were presented with videos of an actor either: performing IMG in the context of a German sentence (GG), IMG in the context of a Russian (as a foreign language) sentence (GR), or speaking an isolated German sentence without gesture (SG). The results of the fMRI experiment confirmed that gesture–speech processing of IMG activates the posterior MTG (GG>GR∩GG>SG). In the EEG experiment we found that the identical integration process (GG>GR∩GG>SG) is related to a centrally-distributed alpha (7–13 Hz) power decrease within 700–1400 ms post-onset of the critical word. These new findings suggest that BOLD response increase in the pMTG and alpha power decrease represent the neural correlates of integrating intrinsically meaningful gestures with their corresponding speech.</p>
Raw data of 'He, Y., Gebhardt, H., Steines, M., Sammer, G., Kircher, T., Nagels, A., & Straube, B. (2015). The EEG and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech. Neuropsychologia, 72, 27-42.'
<p>This is the raw data for</p> <p>He, Y., Gebhardt, H., Steines, M., Sammer, G., Kircher, T., Nagels, A., & Straube, B. (2015). The EEG and fMRI signatures of neural integration: An investigation of meaningful gestures and corresponding speech. Neuropsychologia, 72, 27-42.</p> <p>Both second level fMRI data and EEG data for single subjects are provided.</p> <p></p>
GIS files corresponding to the Riverine Nitrogen Modeling in the San Antonio and Guadalupe Basin using the NHDPlus v2 Dataset
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to GIS files that were generated in the study published by Journal of American Water Resources Association (JAWRA):</p> <p>Tavakoly, A. A., D. R. Maidment, J. W. McClelland, T. Whiteaker, Z.-L. Yang, C. Griffin, and C. H. David (2016), "A GIS Framework for Regional Modeling of Riverine Nitrogen Transport: Case Study, San Antonio and Guadalupe Basins”, Journal of the American Water Resources Association, Journal of the American Water Resources Association (JAWRA) 52(1):1-15. DOI: 10.1111/1752-1688.12355</p> <p><em>Please cite the aforementioned article and the dataset herein, when using of any of these files in this dataset.</em></p>
Data of 3D MHD Simulation for manuscript "Characteristics of Transpolar Arc Motion and its Corresponding Magnetospheric Dynamic Process"
<p>Data of 3D MHD Simulation for manuscript "Characteristics of Transpolar Arc Motion and its Corresponding Magnetospheric Dynamic Process"</p> <p>There are 6 types of data files:</p> <p>1) -3)MHD simulation results for FAC, plasma density, and temperature, projected at the x = -40RE position, with the viewpoint from the magnetotail towards the earth</p> <p>4) FAC mapping.rar. These data are the parametters in the plane of about Z=0 RE, which were mapped to the 7.2 Re, along the magnetic field lines.</p> <p>5) The simulation results of the model are plotted for FAC on Z=0RE.</p> <p>The results of the above data simulation plot are from 20171115 23:00 UT to 20171116 02:00 UT.</p> <p>6) XXBDd0142.rar, which is full 3D Simulation data at 2017.11.16 01:22 UT;</p> <p>All of these data include the following parameters:</p> <p>time, x, y, z, logrho, Vx, Vy, Vz, Bx, By, Bz, Pr, Jx, Jy, Jz, Edj</p> <p>7) SSUSI data at 2017.11.16.</p> <p> </p>
Data of decomposition, topology, and the corresponding graphs of Random Woody Crown Networks with size from 10 to 248 nodes
<p>Data of decomposition, topology and the corresponding graphs of Random Woody Crown Networks with size from 10 to 248 nodes</p>
Multiple Correspondence Analysis Bibliography (2017-2021)
<p>Dataset studied in </p><p>Koutsoupias N., (2022). Multiple Correspondence Analysis in Scientific Research: A Longitudinal Mapping, Proceedings of the 11th Panhellenic Data Analysis Conference with international participation. </p>
RRR input and output files corresponding to "Global patterns in river water storage dependent on residence time"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all the RRR input and output files that were used in the study reported in:</p> <ul> <li>Collins, E. L., C. H. David, R. Riggs, G. H. Allen, T. M. Pavelsky, P. Lin, M. Pan, D. Yamazaki, R. K. Meentemeyer, and G. M. Sanchez (2024), Global patterns in river water storage dependent on residence time.</li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.</p> <p><strong>Input datasets</strong></p> <p>The dataset herein benefited from and was built upon other datasets:</p> <ul> <li>MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7) available under a CC BY-NC-SA 4.0 license. <a href="https://www.reachhydro.org/home/params/merit-basins">https://www.reachhydro.org/home/params/merit-basins</a></li> <li>GLDAS VIC Land Surface Model L4 monthly 1.0 x 1.0 degree (V2.0) available under a CC0 license. <a href="https://science.nasa.gov/spd-41/">https://science.nasa.gov/spd-41/</a>. DOI: <a href="https://doi.org/10.5067/ZRIHVF29X43C">10.5067/ZRIHVF29X43C</a></li> <li>GLDAS Noah Land Surface Model L4 monthly 1.0 x 1.0 degree (V2.0) available under a CC0 license. <a href="https://science.nasa.gov/spd-41/">https://science.nasa.gov/spd-41/</a>. DOI: <a href="https://doi.org/10.5067/QN80TO7ZHFJZ">10.5067/QN80TO7ZHFJZ</a></li> <li>GLDAS Catchment Land Surface Model L4 monthly 1.0 x 1.0 degree (V2.0) available under a CC0 license. <a href="https://science.nasa.gov/spd-41/">https://science.nasa.gov/spd-41/</a>. DOI: <a href="https://doi.org/10.5067/SGSL3LNKGJWW">10.5067/SGSL3LNKGJWW</a></li> </ul> <p><strong>Known bugs and limitations in this dataset or the associated manuscript</strong></p> <p>In the original files for MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7), as obtained from the pfaf_level_02 zip files, two river reaches do not have a corresponding catchment: COMID=31000001 and COMID=61000003. These two river reaches were removed from our analysis, hence bringing the total number of river reaches used from 2,938,143 to 2,938,141, the same value as the total number of catchments.</p> <p>In the original files for MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7), as obtained from the pfaf_level_02 zip files, the attribute table for the river reach with COMID=67072432 contains only four upstream reaches (COMID=64072433, COMID=64072735, COMID=64073772, and COMID=64073786), while the reach actually has five upstream reaches on the map (those above and COMID=64073793). This was modified herein because all connectivity in this dataset was created by associating upstream and downstream nodes rather than using the attribute table.</p> <div> <div> <div> <p>Our monthly dataset of river gauges with 95% daily data availability for 1980–2009 and with average discharge greater than or equal to 100 m3/s contains 1,148 stations. After mapping gauges to the MERIT Hydro river network using a joint criteria of 1) within a distance of 0.05 degrees and 2) uncorrected 30-yr average simulated discharge of the river reach within one order of magnitude relative to the observed average discharge of the gauge; the number of gauges reduces to 1129. In cases of multiple gauges per reach, we selected the gauge that had the closest observed average discharge to the uncorrected 30-yr averages simulated, resulting in 1,001 gauges. The dataset was further split in calibration gauges (702) and validation gauges (299). Three specific gauges are located in places with 0 accumulation of runoff in their specific sub-basin, they are located at river reaches with ID 41003992, 41001545, and 24005996. These three gauges were removed from the overall analysis at all gauges, resulting in 998 total gauges. All three gauges are part of the 299 validation gauges, not the 702 calibration gauges. They were retained in the analysis of 299 validation gauges. </p> <p> </p> </div> </div> </div> <p><strong>Files included in this version</strong></p> <p>All monthly discharge observation files for 1148 gauges, in shapefile and CSV formats:</p> <ul> <li>sites_1980-01_2009-12_100cms_095p.zip</li> <li>timeseries_obs_1980-01_2009-12_100cms_095p_monthly.zip</li> </ul> <p>All CSV files corresponding to the hydrography of all pfaf_level_02 regions of MERIT-Hydro v0.7 Basins v1.0 (i.e. the 61 values of <em>ii</em>):</p> <ul> <li>coords_pfaf_<em>ii</em>.zip</li> <li>kfac_pfaf_<em>ii</em>_1km_hour.zip</li> <li>k_pfaf_<em>ii</em>_low.zip</li> <li>k_pfaf_<em>ii</em>_nrm.zip</li> <li>k_pfaf_<em>ii</em>_hig.zip</li> <li>rapid_catchment_pfaf_<em>ii</em>.zip</li> <li>rapid_connect_pfaf_<em>ii</em>.zip</li> <li>rapid_coupling_pfaf_<em>ii</em>_GLDAS.zip</li> <li>riv_bas_id_pfaf_<em>ii</em>_topo.zip</li> <li>sort_pfaf_<em>ii</em>_topo.zip</li> <li>xfac_pfaf_<em>ii</em>_0.1.zip</li> <li>x_pfaf_<em>ii</em>_low.zip</li> <li>x_pfaf_<em>ii</em>_nrm.zip</li> <li>x_pfaf_<em>ii</em>_hig.zip</li> </ul> <p>All netCDF4 files with monthly surface and subsurface runoff that were combined from GLDAS 2.0 and corresponding to the following land surface models for the years 1980 to 2009 (both included):</p> <ul> <li>GLDAS_CLSM_M_1980-01_2009-12_utc.zip</li> <li>GLDAS_NOAH_M_1980-01_2009-12_utc.zip</li> <li>GLDAS_VIC_M_1980-01_2009-12_utc.zip</li> <li>GLDAS_ENS_M_1980-01_2009-12_utc.zip (the 3-model ensemble average based on the three above models).</li> </ul> <p>All netCDF4 files with monthly lateral inflows (based on the GLDAS data above), corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code:</p> <ul> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_CLSM_M_1980-01_2009-12_utc.zip</li> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_NOAH_M_1980-01_2009-12_utc.zip</li> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_VIC_M_1980-01_2009-12_utc.zip</li> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc.zip</li> </ul> <p>All netCDF4 files with monthly discharge (based on the m3_riv data above), corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code: </p> <ul> <li>Qout_pfaf_<em>ii</em>_GLDAS_CLSM_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_<em>ii</em>_GLDAS_NOAH_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_<em>ii</em>_GLDAS_VIC_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc.zip</li> </ul> <p>All netCDF4 files with monthly storage (based on the Qout_pfaf_<em>ii</em>_GLDAS_ENS data above), corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code, and to each characteristic value of residence time (low, nrm, hig):</p> <ul> <li>V_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc_low.zip</li> <li>V_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc_nrm.zip</li> <li>V_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc_hig.zip</li> </ul> <p>All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) river network on which the 30-year mean of lumped discharge was appended (based on the Qout_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc.zip data above):</p> <ul> <li>riv_pfaf_<em>ii</em>_MERIT_Hydro_v07_Basins_v01_GLDAS_ENS.zip</li> </ul> <p>All monthly discharge observation files for 1148 gauges, in shapefile format, with observed monthly mean appended:</p> <ul> <li>sites_1980-01_2009-12_100cms_095p_meanQ.zip</li> </ul> <p>All monthly discharge observation files for 1001 gauges snapped on the MERIT Hydro (v0.7) Basins (v1.0) river network, in shapefile and CSV formats, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code: </p> <ul> <li>sites_1980-01_2009-12_100cms_095p_meanQ_COR_pfaf_<em>ii</em>.zip</li> <li>sites_1980-01_2009-12_100cms_095p_meanQ_COR.zip</li> <li>obs_tot_id_1980-01_2009-12_100cms_095p_meanQ_COR_pfaf_<em>ii</em>.zip</li> <li>Qobs_1980-01_2009-12_100cms_095p_meanQ_COR_pfaf_<em>ii</em>.zip</li> </ul> <p>All monthly discharge observation files for 1001 gauges snapped on the MERIT Hydro (v0.7) Basins (v1.0) river network, separated into calibration (CAL) and validation (VAL) gauges, in shapefile and CSV formats, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code: </p> <ul> <li>sites_1980-01_2009-12_100cms_095p_meanQ_CAL.zip</li> <li>obs_bas_id_1980-01_2009-12_100cms_095p_meanQ_CAL_pfaf_<em>ii</em>.zip</li> <li>sites_1980-01_2009-12_100cms_095p_meanQ_VAL.zip</li> </ul> <p>All netCDF4 files with monthly lateral inflows (based on the m3_riv_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc.zip data above) corrected using Long-Term Inverse Routing for calibration (CAL) gauges only and associated monthly discharge data, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code:</p> <ul> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_CAL_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_<em>ii</em>_GLDAS_CAL_M_1980-01_2009-12_utc.zip</li> </ul> <p>All netCDF4 files with monthly lateral inflows (based on the m3_riv_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc.zip data above) corrected using Long-Term Inverse Routing for all correction (COR) gauges only and associated monthly discharge data, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code:</p> <ul> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc.zip</li> </ul> <p>All netCDF4 files with monthly storage (based on the Qout_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc.zip data above), corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code, and to each characteristic value of residence time (low, nrm, hig):</p> <ul> <li>V_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc_low.zip</li> <li>V_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc_nrm.zip</li> <li>V_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc_hig.zip</li> </ul> <p>All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) river network on which the 30-year mean of lumped discharge was appended (based on the Qout_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc.zip data above):</p> <ul> <li>riv_pfaf_<em>ii</em>_MERIT_Hydro_v07_Basins_v01_GLDAS_COR.zip</li> </ul> <p>All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) catchments that have been dissolved and of which the perimeter was extracted, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code; as well as global combined files:</p> <ul> <li>cat_pfaf_<em>ii</em>_MERIT_Hydro_v07_Basins_v01_disso.zip</li> <li>cat_ MERIT_Hydro_v07_Basins_v01_disso.zip</li> <li>cat_pfaf_<em>ii</em>_MERIT_Hydro_v07_Basins_v01_perim.zip</li> <li>cat_ MERIT_Hydro_v07_Basins_v01_perim.zip</li> </ul> <p>All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) river network retaining only those reaches that flow to the global coast, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code:</p> <ul> <li>riv_pfaf_<em>ii</em>_MERIT_Hydro_v07_Basins_v01_coast.zip</li> </ul>
Updated Data corresponding to a study on 'Rapid Patient-Specific FEM Meshes from 3D iPhone Scans'
Open the record for dataset details and reuse information.
Correspondence on Li Yumei et al.: Exaggerated false positives by popular differential expression methods when analyzing human population samples.
<p>Scripts for manuscript</p>
Average values of S(B) and their corresponding 90% confidence intervals
<p>The animation displays the average values of S(B) for B < 10^6 along with their corresponding 90% confidence intervals, <br>for elliptic curves with ranks 0 or 1 in the conductor range [N,N+10\sqrt(n)]. The cross indicates the location of the local maximum, <br>while circles represent predictions for the first three local maxima at 0.08N, 0.65N, and 1.7N.</p>
Optimal forest management schemes and the corresponding forest biomass carbon sink projections in China
Open the record for dataset details and reuse information.
Predicting the crossmodal correspondences of odors using an electronic nose
<p>Odour Recordings</p> <p>There are 100 recordings in total of 10 different essential oils; five were from Mystic Moments™ (caramel, cherry, coffee, freshly cut grass, and pine) and five from Miaroma™ (black pepper, lavender, lemon, orange, and peppermint).<br> Each recording is 10 minutes in duration (600 seconds). Columns in each of the .csv files are in the following order: time, air quality, pollution level, temperature, pressure, humidity, gas, MQ3, MQ5, MQ9, and HCHO. The file's name denotes the odour being recorded and the record number (1 - 10).<br> For more information, please view the publication - R.J. Ward, S. Rahman, S.M. Wuerger, A. Marshall, Predicting the crossmodal correspondences of odors using an electronic nose, Heliyon.</p> <p>Perceptual Data </p> <p>The underlying perceptual data used from R.J. Ward, S.M. Wuerger, A. Marshall, Smelling Sensations: Olfactory Crossmodal Correspondences, J. Percept. Imaging. 4 (2021) 1–12. https://doi.org/10.2352/j.percept.imaging.2021.4.2.020402.<br> The data used from the later paper is the (angularity of shapes, smoothness of texture, perceived pleasantness, pitch, and the colour ratings in L*a*b* space).</p> <p>Each file contains the raw perceptual ratings for the ten different odours (columns) from sixty-eight different participants (rows) in the following order: black pepper, caramel, cherry, coffee, freshly cut grass, lavender, lemon, orange, peppermint, and pine.<br> NOTE: the pitch ratings only contain data from sixy participants due to it being added to the experiment at a later date.</p> <p>The folder regression models contains the required MATLAB code to train and test the regression models for predicting the crossmodal correspondences of odors using physicochemical data.</p> <p>The folder raw perceptual data contains the raw unprocessed perceptual data in .csv format.</p> <p>The folder e-nose code contains the code to drive the Arduino circuit and additional libraries required by the sensors.</p> <p>The folder e-nose recorder contains a Unity project and code to receive the UDP packets from the e-nose and log them.</p> <p><br> If you use this data please cite the following papers;</p> <p>Perceptual Data<br> R.J. Ward, S.M. Wuerger, A. Marshall, Smelling Sensations: Olfactory Crossmodal Correspondences, J. Percept. Imaging. 4 (2021) 1–12. https://doi.org/10.2352/j.percept.imaging.2021.4.2.020402</p> <p>Chemical Data<br> R. Ward, S. Rahman, S. Wuerger, A. Marshall, Predicting the colour associated with odours using an electronic nose, in: 1st Work. Multisensory Exp. - SensoryX’21, 2021: pp. 1–6. https://doi.org/10.5753/sensoryx.2021.15683.<br> R.J. Ward, S. Rahman, S.M. Wuerger, A. Marshall, Predicting the crossmodal correspondences of odors using an electronic nose, Heliyon, (under review as of file upload).</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.