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Figure 5 in A TIME-CALIBRATED PHYLOGENY OF VERBESINA (HELIANTHEAE - ASTERACEAE) BASED ON NUCLEAR RIBOSOMAL ITS AND ETS SEQUENCES
Figure 5. Ancestral area reconstruction produced by means of the RASP–Bayesian binary Markov Chain Monte Carlo method and based on the occurrence of Verbesina L. (Heliantheae–Asteraceae) species. Nodes with a predicted dispersal component are circled in blue, and those with a significant vicariance component are circled in green. The green bar indicates the consensus age of the closure of the Panama Isthmus (age based on Leigh et al., 2013; O'Dea et al., 2016). BOL, Bolivia; BRA, Brazil; CAR, Caribbean; COL, Colombia; CRC, Costa Rica; ECU, Ecuador; MEX, Mexico; NIC, Nicaragua; PER, Peru; USA, United States of America; VEN, Venezuela.
Figure 4 in A TIME-CALIBRATED PHYLOGENY OF VERBESINA (HELIANTHEAE - ASTERACEAE) BASED ON NUCLEAR RIBOSOMAL ITS AND ETS SEQUENCES
Figure 4. Time-calibrated Bayesian inference tree generated using concatenated internal and external transcribed spacer data. The values at the nodes indicate mean divergence dates, and the horizontal bars indicate 95% highest posterior density ranges for the age at each node. The green bar indicates the consensus age of the closure of the Panama Isthmus (age based on Leigh et al., 2013; O'Dea et al., 2016). BOL, Bolivia; BRA, Brazil; CAR, Caribbean; COL, Colombia; CRC, Costa Rica; ECU, Ecuador; MEX, Mexico; NIC, Nicaragua; PER, Peru; USA, United States of America; VEN, Venezuela.
Figure 3 in A TIME-CALIBRATED PHYLOGENY OF VERBESINA (HELIANTHEAE - ASTERACEAE) BASED ON NUCLEAR RIBOSOMAL ITS AND ETS SEQUENCES
Figure 3. Bayesian inference tree of combined internal and external transcribed spacer data. Posterior probabilities> 0.9 are given above the branches. The red text indicates South American species. Leaf figures indicate opposite and alternate phyllotaxy. Chromosome numbers are based on Moreira & Cavalcanti (2020) and Panero & Strother (2021). BOL, Bolivia; BRA, Brazil; CAR, Caribbean; COL, Colombia; CRC, Costa Rica; ECU, Ecuador; MEX, Mexico; NA, North America; NIC, Nicaragua; PER, Peru; SA, South America; VEN, Venezuela.
Figure 2 in A TIME-CALIBRATED PHYLOGENY OF VERBESINA (HELIANTHEAE - ASTERACEAE) BASED ON NUCLEAR RIBOSOMAL ITS AND ETS SEQUENCES
Figure 2. Total distribution of Verbesina in North America, Central America, the Caribbean, and South America. (Data accessed from GBIF, https://www.gbif.org/).
Figure 1 in A TIME-CALIBRATED PHYLOGENY OF VERBESINA (HELIANTHEAE - ASTERACEAE) BASED ON NUCLEAR RIBOSOMAL ITS AND ETS SEQUENCES
Figure 1. Morphological characters in Verbesina. A, Tree of Verbesina floribunda Gardner; B, subshrubby habit of V. bipinnatifida Baker; C, capitulum of V. luetzelburgii Mattf.; D, capitulum of V. bipinnatifida; E, cypsela of V. bipinnatifida; F, cypsela of V. floribunda; G, scanning electron micrograph (SEM) of the verrucous cypsela surface of V. macrophylla (Cass.) S.F.Blake; H, inflorescence of V. macrophylla; I, pinnatipartite leaf of V. macrophylla; J, SEM of the scalariform cypsela surface of V. glabrata Hook. & Arn.; K, inflorescence of V. glabrata; L, entire leaf of V. glabrata. Vouchers: C, G.L. Moreira et al. 118 (CEN); D–E, G.L. Moreira et al. 116 (CEN); F, G.L. Moreira et al. 101 (CEN). Scale bars: C, 8 mm; D, 4 mm; E, 2 mm; F, 4 mm; G and J, 200 μm. Photographs: G. L. Moreira.
FIGURE 1. The 25 in Twenty-five well-justified fossil calibrations for primate divergences
FIGURE 1. The 25 fossil calibrations shown in phylogenetic context. 1 = Crown Euarchontoglires (Euarchonta-Glires split), 2 = Crown Glires (Rodentia-Lagomorpha split), 3 = Crown Euarchonta (Primatomorpha-Scandentia split), 4 = Crown Primatomorpha (Primates-Dermoptera split), 5 = Crown Primates (Haplorhini-Strepsirhini split), 6 = Crown Strepsirrhini (Lorisiformes-[Lemuriformes+Chiromyiformes] split), 7 = Lemuriformes+Chiromyiformes (Lemuriformes- Chiromyiformes split), 8 = Crown Lorisiformes (Lorisidae-Galagidae split), 9 = Crown Lorisidae (Lorisinae-Perodicticinae split), 10 = Crown Haplorhini (Anthropoidea-Tarsiiformes split), 11 = Crown Anthropoidea (Catarrhini-Platyrrhini split), 12 = Crown Catarrhini (Cercopithecoidea-Hominoidea split), 13 = Crown Cercopithecidae (Cercopithecinae- Colobinae split), 14 = Crown Colobinae (Colobini-Presbytini split), 15 = Crown Cercopithecinae (Cercopithecini-Papionini split), 16 = Crown Papionini (Macacina-Papionina split), 17 = Crown Cercopithecini(uncertain split), 18 = Crown Hominoidea (Hominidae-Hylobatidae split), 19 = Crown Hominidae (Homininae-Ponginae split), 20 = Homo+Pan (Homo-Pan split), 21 = Crown Platyrrhini (Pitheciidae-[Aotidae+Atelidae+Callitrichidae+Cebidae] split), 22 = Crown Pitheciidae (Callicebinae-Pitheciinae split), 23 = Callitrichidae+Cebidae (Callitrichidae-Cebidae split), 24 = Crown Cebidae (Cebinae-Saimirinae split), 25 = Crown Atelidae (Alouattinae-Atelinae split).
Rawdata for: Preparation of low-concentration H2 test gas mixtures in ambient air for calibration of H2 sensors, Karbach et al., 2024
<p>Rawdata for publication: Karbach et al.: Preparation of low-concentration H2 test gas mixtures in ambient<br>air for calibration of H2 sensors, AMT, 2024</p> <p>DOI: 10.5194/amt-17-4081-2024</p>
Fig. 1. The modified time calibrated Bayesian tree and a in Fig. 3 in Fig. 4 in Fig. 4 in Responses of Phyllostomid Bats to Traditional Agriculture in Neotropical Montane Forests of Southern Mexico.
Fig. 1. The modified time calibrated Bayesian tree and a plot of four major avian developmental modes (Prum et al. 2015). The complete tree is divided into parts A and B. Scale in the Y-axis: millions of years ago.
Fig. 1. The modified time calibrated Bayesian tree and a in Fig. 3 in Fig. 4 in Fig. 4 in Responses of Phyllostomid Bats to Traditional Agriculture in Neotropical Montane Forests of Southern Mexico.
Fig. 1. The modified time calibrated Bayesian tree and a plot of four major avian developmental modes (Prum et al. 2015). The complete tree is divided into parts A and B. Scale in the Y-axis: millions of years ago.
Dataset for "Atmospheric CFC-11 and CCl4: a Free Calibration Standard for PTR-MS"
<p>Dataset for the publication: Notø and Holzinger (2024), “Atmospheric CFC-11 and CCl4: a Free Calibration Standard for PTR-MS”, <br><a title="Atmospheric CFC-11 and CCl4: a Free Calibration Standard for PTR-MS" href="https://doi.org/10.1016/j.ijms.2024.117311">https://doi.org/10.1016/j.ijms.2024.117311</a></p> <p>The data consists of raw data files of measurements and the processing code to calculate pseudo reaction rate constants of CFC-11 and CCl4 with H3O+.</p>
Calibration of test diameter and area as proxies for body size in the planktonic foraminifera Globoconella puncticulata
<p>Here we provide an extensive image library of<em> Globoconella puncticulata</em>, with accompanying 2D and 3D coordinate data and morphometric measurements. This data was generated using high-throughput imaging methods (<em>AutoMorph</em>) developed in P.M. Hull's lab at Yale University. This dataset accompanies the manuscript: Brombacher, J.A., Elder, L.E., Hull, P.M., Wilson, P.A. and Ezard, T.H.(In Press) Calibration of test diameter and area as proxies for body size in the planktonic foraminifera <em>Globoconella puncticulata.</em> <em>Journal of Foraminiferal Research</em>. The manuscript describes important details related to data collection and usage and should be consulted before using the data provided here. </p> <p>Samples were obtained from three sites in the Atlantic Ocean: equatorial Ocean Drilling Program (ODP) Site 925, subtropical ODP Site 659, and mid-latitude Integrated Ocean Drilling Program (IODP) Site U1313. 1233 individual foraminifera of the species <em>Globoconella puncticulata</em> were picked from these samples to be imaged. Nine slides of microfossils were imaged at multiple focal heights (z-planes; 31.1um step distance) using a light microscope with an automated stage and processed with the image processing models of <em>AutoMorph</em>. <em>AutoMorph</em> software and tutorials can be accessed here: https://github.com/HullLab. For an example of a raw slide scan see: Hsiang, Allison Y., Nelson, Kaylea, Elder, Leanne E., Liu, Yusu, & Hull, Pincelli M. (2016). Slide scan example for Automorph. Zenodo. http://doi.org/10.5281/zenodo.167557. Slides were named with the IODP or ODP site number. Each slide was imaged with the foraminifera arranged in 2-3 orientations (i.e., umbilical, spiral, and/or edge).</p> <p>One of the nine slide was imaged with both light and computed tomography in order to compare the volumetric data obtained from the two approaches. This slide had 6 individual foraminifera. All individuals imaged with this combination of approaches have ‘CTscan’ included in the file name. This <em>AutoMorph</em>/CT scanned slide was imaged from three orientations (umbilical, spiral, and edge) and with two z-step distances (distance between imaged focal planes on the z-axis) of 11.2 um and 31.31 um.</p> <p>Images and morphometric data are provided in 9 datasets detailed below. Do note: our slide scanning technique often identifies background light scatter and/or other slide debris (glue, shell fragments, etc.) as ‘objects’, and these objects are numbered in sequence. We have excluded all non-foraminiferal objects from the datasets below, so the object numbers of the foraminifera will often be discontinuous (i.e.: 2,3,4,10,11,16).</p> <p><strong>1) 2d_coordinates.tar</strong> provides the 2D coordinates of each successfully extracted orientation from the 1233 individual <em>Globoconella puncticulata</em> (2811 total successfully extracted orientations) in a single csv (all2dcoordinates.csv). 2d_coordinates.tar also provides a csv for each slide scan at a particular orientation (26 slide scans: 4 samples imaged in 3 orientations, 4 samples imaged in 2 orientations, and 1 sample imaged from 3 orientations with two different 2 Z-step sizes per orientations (i.e., 6 scans for the one sample)).</p> <p><strong>2) 2dmorph_data_all.tar</strong> contains all <em>Globoconella puncticulata</em> with 2D measurements extracted by the<em> AutoMorph</em> routine run2morph (2811 total successfully extracted orientations), and a text file listing all objects with failed 2D extractions and non-forminiferal objects.</p> <p><strong>3) 2d_outline_check.tar</strong> provides an overlay of the extracted 2D outline on the <em>Globoconella puncticulata</em> EDF for quality control purposes for all extracted <em>Globoconella puncticulata</em> (2811 total successfully extracted orientations).</p> <p><strong>5) 3d_obj_files.tar</strong> provides directories for each slide scan with the 3D mesh coordinates as obj files for each extracted <em>Globoconella puncticulata</em> (2270 total successfully extracted orientations). Note: more 3D extractions failed than 2D extractions, accounting for the difference in the number of 2D extractions (2811) and 3D extractions (2270).</p> <p><strong>6) 3d_pdfs.tar</strong> provides directories of each slide scan with the 3D pdfs of each <em>Globoconella puncticulata</em> extracted (2270 total successfully extracted orientations) for quality control purposes. 3D pdfs, meshes and shape measurements were generated by the <em>AutoMorph</em> module run3dmorph. Note that only some pdf viewers are able to display 3d pdfs properly. </p> <p><strong>4) 3dmorph_data.tar</strong> contains a csv file for each slide scan with the 3d measurements generated for each <em>Globoconella puncticulata</em> by the<em> AutoMorph</em> module run3dmorph (2270 total successfully extracted objects in 26 directories representing each slide scan at a particular orientation).</p> <p><strong>7) object_ edf_images.tar</strong> contains the extended depth of focus (EDF)images in 26-directories for each slide scan at a particular orientation. The EDFs are 2D image composites created from the z-stacked photographic images by the <em>AutoMorph</em> focus module. Together, the 26-directories contain 2811 total images representing the successfully extracted 2D orientations.</p> <p><strong>8) </strong><strong>sam</strong><strong>pleID.csv</strong> is a csv of all sample information. This includes the Slide name for the physical slide each sample is on, the Object number on that slide scan for each foraminifera used in the study, and the Ocean Drilling Program information for each object. Ocean Drilling Program details consist of the Leg: the Leg number for the drilling cruise, Site: the Ocean Drilling Program collection site number, the Hole: the drilling hole ID, the Core: the Core number from that drilling site, the core Type: the type of drilling equipment used (H for all these samples which is an advanced hydraulic piston core), the Section: the section number on the core, and the Top and Bottom: the sample top and bottom interval in cm from the top of the section. </p> <p><strong>9) slide_images_boxed.tar</strong> contains one image for each slide view at a particular orientation 26 slide scans: 4 samples imaged in 3 orientations, 4 samples imaged in 2 orientations, and 1 sample imaged from 3 orientations with two different 2 Z-step sizes per orientations (i.e., 6 scans for the one sample). A red box delineates each object extracted using the <em>AutoMorph</em> segment module. Slides are named according to their ocean drilling sample identification (see dataset #10).</p> <p><strong>10) z-stacks.tar.gz </strong>contains the original z-stack images of each <em>Globoconella puncticulata </em>(2811 total representing the successfully extracted 2D orientations) in 26 directories representing each slide scan at a particular orientation).</p>
Supplementary Data for "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" (abridged version)
<p>This is a supplementary data set associated with the publication "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" from the Center for Atmospheric Particle Studies, submitted to Atmospheric Measurement Techniques. This is an abbreviated version which does not include the calibrated models; these models must be re-generated by running the codes contained with the data set.</p> <p> </p>
Updated MCMC chains and subsamples for Hector calibration paper
<p>These csvs contain MCMC chains and sampled subsets for calibration of the Hector simple climate model (<a href="https://github.com/JGCRI/hector">https://github.com/JGCRI/hector</a>, DOI:10.5194/gmd-8-939-2015). This series of five calibrations with different observational constraints and free parameters is described in Vega-Westhoff et al. (2019, DOI:10.1029/2018EF001082; see their table 1).</p> <p>The calibrations use a version of Hector that includes the BRICK sea-level module (<a href="https://github.com/scrim-network/BRICK">https://github.com/scrim-network/BRICK</a>, DOI:10.5194/gmd-10-2741-2017). Hector with BRICK is available on my fork of the Hector model (https://github.com/bvegawe/hector/tree/dev_slr). The calibration process is also adapted from BRICK. The code used to produce these chains can be found at https://github.com/bvegawe/hector_probabilistic, DOI:10.5281/zenodo.3236411.</p> <p>These five sets of MCMC chains were produced using hector_calib_driver.R. Inputs used to create each calibration are specified below: </p> <p>T.csv: Rscript hector_calib_driver.R -f *output folder* -n 200000 --forcing TRUE --np 10 --model_set onlyT_model --obs_set onlyT_obs</p> <p>TOHC.csv: Rscript hector_calib_driver.R -f *output folder* --forcing TRUE --np 10 --model_set doeclim_model --obs_set doeclim_obs</p> <p>TOHCGICGISAIS.csv: Rscript hector_calib_driver.R -f *output folder* --forcing TRUE --np 10 --model_set noTE_model --obs_set noTE_obs</p> <p>TTE.csv: Rscript hector_calib_driver.R -f *output folder* --forcing TRUE --np 10 --model_set onlyTE_model --obs_set doeclimTE_obs</p> <p>TTEGICGISAIS.csv: Rscript hector_calib_driver.R -f *output folder* --forcing TRUE --np 10 --model_set all_model --obs_set noOcheat_obs</p> <p> </p> <p>V1.1 - Updated with new calibrations. Previously only included two calibration files, <a href="https://zenodo.org/api/files/5f877c29-3990-4d12-92f0-1a7c6f7d271c/wSLR_calib.csv?versionId=b9c5ea10-6f52-4b91-b30a-be68cb58fba6">wSLR_calib.csv</a>, which was a 'full' model calibration, including both ocean heat and thermosteric sea level historical constraints, and woSLR_calib.csv, which included only ocean heat and temperature constraints. After review, we redid the calibration experiment, including additional combinations of constraints, and removing the 'full' calibration, which treated the ocean heat and thermosteric sea level constraints as independent.</p>
Brain Invaders calibration-less P300-based BCI with modulation of flash duration Dataset (bi2015a)
<p><strong>Summary:</strong></p> <p>This dataset contains electroencephalographic (EEG) recordings of 50 subjects playing to a visual P300 Brain-Computer Interface (BCI) videogame named <em>Brain </em><em>Invaders</em>. The interface uses the oddball paradigm on a grid of 36 symbols (1 Target, 35 Non-Target) that are flashed pseudo-randomly to elicit the P300 response. EEG data were recorded using 32 active wet electrodes with three conditions: flash duration 50ms, 80ms or 110ms. The experiment took place at GIPSA-lab, Grenoble, France, in 2015. A full description of the experiment is available at <a href="https://hal.archives-ouvertes.fr/hal-02172347">https://hal.archives-ouvertes.fr/hal-02172347</a>. Python code for manipulating the data is available at <a href="https://github.com/plcrodrigues/py.BI.EEG.2015a-GIPSA">https://github.com/plcrodrigues/py.BI.EEG.2015a-GIPSA</a>. The ID of this dataset is <em>bi2015a.</em></p> <p> </p> <p><strong>Full description of the experiment and dataset: </strong><a href="https://hal.archives-ouvertes.fr/hal-02172347">https://hal.archives-ouvertes.fr/hal-02172347</a></p> <p> </p> <p><strong><em>Investigators</em>:</strong> Eng. Louis Korczowski, B. Sc. Martine Cederhout</p> <p> </p> <p><strong><em>Technical</em></strong> <strong><em>Support</em></strong>: Eng. Anton Andreev, Eng. Grégoire Cattan, Eng. Pedro. L. C. Rodrigues, M. Sc. Violette Gautheret</p> <p> </p> <p><strong><em>Scientific Supervisor:</em></strong> Ph.D. Marco Congedo</p> <p> </p> <p><strong>ID of the dataset: </strong><em>bi2015a</em></p>
Brain Invaders calibration-less P300-based BCI using dry EEG electrodes Dataset (bi2014a)
<p><strong>Summary:</strong></p> <p>This dataset contains electroencephalographic (EEG) recordings of 71 subjects playing to a visual P300 Brain-Computer Interface (BCI) videogame named <em>Brain </em><em>Invaders</em>. The interface uses the oddball paradigm on a grid of 36 symbols (1 Target, 35 Non-Target) that are flashed pseudo-randomly to elicit the P300 response. EEG data were recorded using 16 active dry electrodes with up to three game sessions. The experiment took place at GIPSA-lab, Grenoble, France, in 2014. A full description of the experiment is available at <a href="https://hal.archives-ouvertes.fr/hal-02171575">https://hal.archives-ouvertes.fr/hal-02171575</a>. Python code for manipulating the data is available at <a href="https://github.com/plcrodrigues/py.BI.EEG.2014a-GIPSA">https://github.com/plcrodrigues/py.BI.EEG.2014a-GIPSA</a>. The ID of this dataset is <em>bi2014a.</em></p> <p> </p> <p><strong>Full description of the experiment and dataset: </strong><a href="https://hal.archives-ouvertes.fr/hal-02171575">https://hal.archives-ouvertes.fr/hal-02171575</a></p> <p> </p> <p><strong><em>Investigators</em>:</strong> Eng. Louis Korczowski, B. Sc. Ekaterina Ostaschenko</p> <p> </p> <p><strong><em>Technical</em></strong> <strong><em>Support</em></strong>: Eng. Anton Andreev, Eng. Grégoire Cattan, Eng. Pedro. L. C. Rodrigues, M. Sc. Violette Gautheret</p> <p> </p> <p><strong><em>Scientific Supervisor:</em></strong> Ph.D. Marco Congedo</p> <p> </p> <p><strong>ID of the dataset: </strong><em>bi2014a</em></p>
Mechanical drawings and calibration code for a custom built staircase to patient biomechanics
<p>This repository accompanies a preprint (doi: , and hopefully one day a peer-reviewed manuscript) that describes the steps we took to design, fabricated, calibrate and validate a costume built staircase that we then integrated into a clinical gait analysis test session. This repository has 4 sub-directories that each provide specific information neccesary to recreate our staircase or build your own.</p> <p>1. matlab.zip - this archived folder has 3 subfolders. i) calibrate_validate, ii) transfer_mocap_2_osim, and iii) batch_osim. This archived folder also has a readme. please read it. I cannot provide any warranty on this software but am happy to help reasonable requests. My contact info is in the readme. Also - check out my github < github.com/joshrbaxter > for updated code</p> <p>2. stairs_drawings.zip - this archived folder has the bill of materials, mechanical drawings, and a cute 3D PDF.</p> <p>3. trc_forces.zip - this folder has the motion capture data exported from Motion Analysis's Cortex software (version 7). </p> <p>4. osim.zip - this folder contains all of the files required to run the opensim model along with all of the output files from inverse dynamics and inverse kinematics. these files were all generated from matlab routines provided in the matlab.zip folder.</p> <p> </p>
Thermocouple inhomogeneity data underpinning the recommendations given in Euramet Calibration Guide No. 8 Version 3.0
<p>Thermoelectric inhomogeneity data from diverse sources, compiled to inform the advice given in Euramet Calibration Guide No. 8 concerning the uncertainty to assume from inhomogeneity.</p> <p>Filename indicates file type; data is the magnitude of the inhomogeneity, in units of °C.</p> <p>More details can be found in the publication Meas. Sci. Technol. 29 (2018) 067002, https://doi.org/10.1088/1361-6501/aabaa3.</p> <p> </p>
A calibrated database of kinematics and EMG of the forearm and hand during activities of daily living
<p>KIN-MUS UJI Dataset contains 572 recordings with anatomical angles and forearm muscle activity of 22 subjects while performing 26 representative activities of daily living. This dataset is, to our knowledge, the biggest currently available hand kinematics and muscle activity dataset to focus on goal-oriented actions. Data were recorded using a CyberGlove instrumented glove and surface EMG electrodes, both properly synchronised. Eighteen hand anatomical angles were obtained from the glove sensors by a validated calibration procedure. Surface EMG activity was recorded from seven representative forearm areas. The statistics verified that data were not affected by the experimental procedures and were similar to the data acquired under real-life conditions.</p> <p> </p> <p><strong>Data Sets</strong>:</p> <p>Data are presented as a Matlab data structure (<em>.mat</em> file). This structure contains all the recorded kinematic and muscle activity data classified as: ADL, phase (reaching, manipulation or release) and subject. The fields contained in the structure are those detailed in the following scheme:</p> <ul> <li>Subject: subject ID;</li> <li>ADL: ADL ID;</li> <li>Phase: phase of movement. 1 corresponds to reaching; 2 corresponds to manipulation; 3 corresponds to releasing.</li> <li>Time: Time stamp</li> <li>Angles (18 columns): Calibrated anatomical angles</li> <li>Muscle activity (7 columns): Normalised signal for the seven representative spot areas, according to [1].</li> </ul> <p>RAW_EMG struct provides the raw sEMG data, without any filter and not resampled, so that researchers may choose to condition the signals as they please<strong>.</strong> The fields contained in this structure are those detailed in the following scheme:</p> <ul> <li>Subject: subject ID;</li> <li>ADL: ADL ID, according to Table 1;</li> <li>Time: Time stamp; this field corresponds with the time stamp of the previous structure.</li> <li>Raw EMG data (7 columns): Raw sEMG data without any filter and not resampled, for the seven representative spot areas according to [1].</li> </ul> <p>[1] Jarque-Bou, N. J., Vergara, M., Sancho-Bru, J. L., Alba, R.-S. & Gracia-Ibáñez, V. Identification of forearm skin zones with similar muscle activation patterns during activities of daily living. <em>J. NeuroEngineering Rehabil. </em>(2018).</p>
Code and data for two Hector calibration papers
<p>Code and data used in two Hector calibration papers:</p> <p>1. "Calibrating simple climate models to individual Earth system models: Lessons learned from calibrating Hector" <br> 2. "Calibrating the Hector simple climate model II: Bayesian calibration of Hector parameters" (working title)<br> </p>
An investigation into a calibration scheme for a light pipe based temperature probe
<p>Excel file containing all the data that went into the figures in the publication. The only processing that has been done is to discard all the values recorded in between the interesting blocks (the data was continuously sampled over a week or so).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.