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FIGURE 7 in Sublittoral and bathyal sea cucumbers (Echinodermata: Holothuroidea) from the Northern Mozambique Channel with description of six new species
FIGURE 7. Holothuria (Metriatyla) kurti Ludwig, 1891. A – B: Lateral (A) and dorsal (B) views of specimen IE- 2007 - 768. C: SEM photos of ossicles from the lateral papillae. Scale bars: A, B = 1 cm; C = 20 µm.
Data used in Prefetch Side-Channel Attacks
<p>Machine-/System-/library-/version-specific data is generated as a part of the attack. The results from measurements cannot directly be applied to other systems/libraries/versions or machines. Therefore we have publish the source code to generate the dataset.</p>
Elucidating molecular mechanisms of protoxin-2 state-specific binding to the human NaV1.7 channel
<p>Human voltage-gated sodium (hNaV) channels are responsible for initiating and propagating action potentials in excitable cells and mutations have been associated with numerous cardiac and neurological disorders. hNaV1.7 channels are expressed in peripheral neurons and are promising targets for pain therapy. The tarantula venom peptide protoxin-2 (PTx2) has high selectivity for hNaV1.7 and is a valuable scaffold for designing novel therapeutics to treat pain. Here, we used computational modeling to study the molecular mechanisms of the state-dependent binding of PTx2 to hNaV1.7 voltage-sensing domains (VSDs). Using Rosetta structural modeling methods, we constructed atomistic models of the hNaV1.7 VSD II and IV in the activated and deactivated states with docked PTx2. We then performed microsecond-long all-atom molecular dynamics (MD) simulations of the systems in hydrated lipid bilayers. Our simulations revealed that PTx2 binds most favorably to the deactivated VSD II and activated VSD IV. These state-specific interactions are mediated primarily by PTx2's residues R22, K26, K27, K28, and W30 with VSD and the surrounding membrane lipids. Our work revealed important protein-protein and protein-lipid contacts that contribute to high-affinity state-dependent toxin interaction with the channel. The workflow presented will prove useful for designing novel peptides with improved selectivity and potency for more effective and safe treatment of pain.</p>
Structural modeling of hERG channel: Drug interactions using Rosetta
<p>Human Ether-a-go-go-Related Gene (hERG) encodes a potassium-selective voltage-gated ion channel essential for normal electrical activity in the heart but is also a major drug anti-target. Genetic hERG mutations and blockage of the channel pore by drugs can cause long QT syndrome (LQTS), which predisposes individuals to potentially deadly arrhythmias. However, not all hERG blocking drugs are pro-arrhythmic, and their differential affinities to discrete channel conformational states have been suggested to contribute to arrhythmogenicity. We used Rosetta electron density refinement and homology modeling to build structural models of open-state hERG channel wild-type (WT) and mutant variants (Y652A, F656A, and Y652A/F656A), and a closed state WT channel based on cryo-electron microscopy structures of hERG and EAG1 channels. These models were used as protein targets for molecular docking of charged and neutral forms of amiodarone, nifekalant, dofetilide, d/l-sotalol, flecainide, and moxifloxacin. We selected these drugs based on their different arrhythmogenic potentials and abilities to facilitate hERG current. Our docking studies and clustering provided atomistic structural insights into state-dependent drug–channel interactions l that play a key role in differentiating safe and harmful hERG blockers and can explain hERG channel facilitation through drug interactions with its open-state hydrophobic pockets.</p>
Restored off-channel pond habitats create thermal regime diversity and refuges within a Mediterranean-climate watershed
<p>Cool-water habitats provide increasingly vital refuges for cold-water fish living on the margins of their historical ranges; consequently, efforts to enhance or create cool-water habitat are becoming a major focus of river restoration practices. However, the effectiveness of restoration projects for providing thermal refuge and creating diverse temperature regimes at the watershed scale remains unclear. In the Klamath River in Northern California, the Karuk Tribe Fisheries Program, the Mid-Klamath Watershed Council, and the U.S. Forest Service constructed a series of off-channel ponds that recreate floodplain habitat and support juvenile coho salmon (<em>Oncorhynchus kisutch</em>) and steelhead (<em>Oncorhynchus mykiss</em>) along the Klamath River and its tributaries. We instrumented these ponds and applied multivariate auto-regressive time series models of fine-scale temperature data from ponds, tributaries, and the mainstem Klamath River to assess how off-channel ponds contributed to thermal regime diversity and thermal refuge habitat in the Klamath riverscape. Our analysis demonstrated that ponds provide diverse thermal habitats that are significantly cooler than creek or mainstem river habitats, even during severe drought. Wavelet analysis of long-term (10 years) temperature data indicated that thermal buffering (i.e. dampening of diel variation) increased over time but was disrupted by drought conditions in 2021. Our analysis demonstrates that in certain situations, human-made off-channel ponds can increase thermal diversity in modified riverscapes even during drought conditions, potentially benefiting floodplain-dependent cold-water species. Restoration actions that create and maintain thermal regime diversity and thermal refuges will become an essential tool to conserve biodiversity in climate-sensitive watersheds. </p>
CHAMMI: A benchmark for channel-adaptive models in microscopy imaging
<p>We present a cellular microscopic image dataset for investigating channel-adaptive models. We collected and pre-processed images from three publicly available sources: 1) the WTC-11 hiPSC dataset from the Allen Institute (Viana et al., 2023), 2) the Human Protein Atlas dataset (Thul et al., 2017), and 3) a combined Cell Painting dataset from the Broad Institute (Gustafsdottir et al., 2013; Bray et al., 2017; Way et al., 2021). These images contain 3, 4, or 5 channels with different cellular structures highlighted in each channel. The goal of this dataset is to facilitate the creation and evaluation of novel computer vision models that are invariant to channel numbers.</p>
Dragon_Pi: IoT Side-Channel Power Data Intrusion Detection Dataset and Unsupervised Convolutional Autoencoder for Intrusion Detection
<h2><strong>Dragon_Pi</strong></h2> <div> <div>For a more in depth description of the Dragon_Pi dataset, please consult the journal article of the same name:</div> <div>Lightbody <em>et al.</em>, Future Internet, 2024, <a href="https://doi.org/10.3390/fi16030088">https://doi.org/10.3390/fi16030088</a> - specifically Section 3.2: Dataset Overview.</div> <div> </div> </div> <p>Dragon_Pi is an intrusion detection dataset for IoT devices. In the field of IoT security there are few datasets, and those which do exist tend to focus solely on network traffic. The Dragon_Pi dataset seeks to provide not only more data for the field of IoT security, but also, data of a somewhat under-published type: linear time series power consumption data.</p> <p>Dragon_Pi is a fully labelled Intrusion Detection dataset for IoT devices. It is composed of both normal and under-attack power consumption data obtained from two separate testbeds - one using a DragonBoard 410c and the other a Raspberry Pi Model 3 - Hence the moniker <em>Dragon_Pi</em>. </p> <p>These testbeds were set up with predefined normal behavour as described in the attached publications. The normal linear time series power consumption was sampled from the testbed under these normal conditions. Both testbeds were then attacked using some common attacks on IoT - the linear time series power consumption captured under these condtions as well. </p> <p>Specifically, the testbeds were subjected to the Port Scan (using Nmap), SSH Brute Force (using Hydra) and SYNFlood Denial of Service (using Hping3) attacks. These attacks were repeated to gain insight to what their signatures looked like and also how varying the tool settings effected the resultant signature. A fourth type of scenario was also conducted on the testbeds - the "Capture the Flag" scenarios. In these files multiple attack types were used with a more specific target - to exfiltrate a hidden file from the testbeds.</p> <p>Each file has three hierarchical levels of annotation for <strong>each sample</strong> within:</p> <ol> <li>A simple "Normal or Anomaly" label for the specific sample</li> <li>A specifc attack type label e.g. "SSH Bruteforce", for the specific sample</li> <li>A specific tool setting for that attack e.g. "Hydra_T16", for the specific sample</li> </ol> <p>Users can decide for themselves what level of annotation they require for their specific task. </p> <p>Each file in the Dragon_Pi dataset is accompanied by its own legend file. This file explains the contents of the specific .csv file and the specific indexes of the events within.</p> <p>The Dragon_Pi dataset consists of approximately 67 files, as shown in Table 1. Compressed, the datset totals approximately 13GB. Completely decompressed the dataset is approximately 80GB ( 30GB Pi data, 50 GB Dragon data). </p> <div> </div> <div> <table> <tbody> <tr> <td>Label Type</td> <td>Specific Label </td> <td>Number of Files DragonBoard 410c</td> <td>Number of Files Raspberry Pi</td> </tr> <tr> <td>Normal </td> <td>Normal </td> <td>3 </td> <td>2</td> </tr> <tr> <td>Port Scan Attack </td> <td>Nmap_T5</td> <td>2</td> <td>1</td> </tr> <tr> <td> </td> <td>Nmap_T4</td> <td>1</td> <td>1</td> </tr> <tr> <td> </td> <td>Nmap_T3</td> <td>1</td> <td>1</td> </tr> <tr> <td> </td> <td>Nmap_T2</td> <td>1</td> <td>1</td> </tr> <tr> <td>SSH Brute Force</td> <td>Hydra_T32</td> <td>4</td> <td>2</td> </tr> <tr> <td> </td> <td>Hydra_T16</td> <td>16</td> <td>2</td> </tr> <tr> <td> </td> <td>Hydra_T3</td> <td>8</td> <td>2</td> </tr> <tr> <td> </td> <td>Hydra_T1</td> <td>5</td> <td>2</td> </tr> <tr> <td>SYNFlood DOS</td> <td>SYNFlood DOS</td> <td>1</td> <td>1</td> </tr> <tr> <td>Capture the Flag</td> <td>Misc Attacks</td> <td>3</td> <td>5</td> </tr> </tbody> </table> </div> <div>Table 1. Enumeration of the in the Dragon_Pi dataset.</div> <div> </div> <div> </div> <div>For a more in depth description of the Dragon_Pi dataset, please consult the journal article of the same name:</div> <div>Lightbody <em>et al.</em>, Future Internet, 2024, <a href="https://doi.org/10.3390/fi16030088">https://doi.org/10.3390/fi16030088</a> - specifically Section 3.2: Dataset Overview.</div> <div> </div> <div> </div> <div><strong>Publication of this dataset:</strong></div> <div> </div> <div>This dataset was published in Lightbody <em>et al.</em>, Future Internet, 2024, <a href="https://doi.org/10.3390/fi16030088">https://doi.org/10.3390/fi16030088</a>. Consult and cite this article for a more in depth dataset description, as well as an in depth review of first AI Intrusion Detection model trained on this dataset. </div> <div> </div> <div>See article Lightbody <em>et al.</em>, Future Internet, 2023, <a href="https://doi.org/10.3390/fi15050187">https://doi.org/10.3390/fi15050187</a> for a detailed investigation on the attack signatures discovered while creating this dataset. This work was an inital investigation of the dataset and can serve as a part 1 to the Dragon_Pi paper.</div> <div> </div> <div> </div> <div><strong>How to cite this dataset in your work: </strong></div> <div> </div> <div>Please cite these two DOIs when publishing using this dataset:</div> <div> <ol> <li>Dragon_Pi release publication: <a href="https://doi.org/10.3390/fi16030088">https://doi.org/10.3390/fi16030088</a> (most important)</li> <li>Zenodo Dataset DOI: https://doi.org/10.5281/zenodo.10784947</li> </ol> </div> <div> <div> </div> </div> <p> </p>
Fig. 2 in New Japanese Record of Henneguya postexilis (Cnidaria: Myxobolidae) from Gills of Alien Channel Catfish Ictalurus punctatus (Siluriformes: Ictaluridae) in Japan
Fig. 2. Line drawings (composite) of fresh myxospores of Henneguya postexilis. A, Valvular view; B, sutural view. Scale bars: 10 µm.
Fig. 3 in New Japanese Record of Henneguya postexilis (Cnidaria: Myxobolidae) from Gills of Alien Channel Catfish Ictalurus punctatus (Siluriformes: Ictaluridae) in Japan
Fig. 3. Maximum likelihood phylogenetic tree based on 18S rDNA data (1513 bp including gaps) from Henneguya postexilis and other related myxozoans using Myxobolus lepomis as an outgroup. The accession number and scientific name of the newly sequenced species in this study are indicated in bold. The corresponding ISND accession numbers are shown. Arabic numerals at nodes indicate bootstrap values.
Fig. 1 in New Japanese Record of Henneguya postexilis (Cnidaria: Myxobolidae) from Gills of Alien Channel Catfish Ictalurus punctatus (Siluriformes: Ictaluridae) in Japan
Fig. 1. Light photomicrograph of two fixed plasmodia with a gill filament of Ictalurus punctatus (A) and fresh myxospores (B–G) of Henneguya postexilis. Arrowheads indicate each plasmodium. B, D, E, Valvular view; C, F, G, sutural view. Scale bars: A, 200 µm; B–G, 10 µm.
Data for: Channel mobility and floodplain reworking across river planform morphologies
<p>Source-to-sink transfer of sediment and organic carbon (OC) is regulated by river mobility. Quantifying trends in river mobility is, however, challenging due to diverse planform morphologies (e.g., meandering, braided) and measurement methods. Here, we utilize a state-of-the-art remote-sensing method applicable to all planform morphologies to quantify the mobility timescales of 80 rivers worldwide. Results show that, across the continuum from meandering to braided rivers, there is a systematic reduction in timescale of channel mobility and—to a lesser extent—the timescale of floodplain reworking. This leads to an overall decrease in the efficiency at which braided river channels rework old floodplain material compared to their meandering counterparts. Reduced reworking efficiency of braided channels stems from their relatively smaller channel-belt areas relative to their channel area. Results suggest that river-mobility timescales can help us characterize sediment and OC storage and transit times from remote sensing.</p>
Ion permeation through a narrow cavity constriction in KCNQ1 channels, scours files of MD simulations and analysis of electrophysiological experiments.
<p>Source files of Molecular Dynamic (MD) simulations and analysis files of electrophysiology data in Igor pro software format. KCNQ1 channel pore region (G245-K354) was embedded in a lipid bilayer consisting of phosphatidylcholine phospholipids (POPC) and ion permission mechanism was analized by MD simulations using the computational electrophysiology (compEL) method implemented in GROMACS v2022.4. Ion imbalance between compartments of double-membrane system created a membrane potential of abour 300 mV which drives ion movment.</p>
The Multi-Temporal Dual Channel Algorithm (MT-DCA)
<p>I) SUMMARY</p> <p>This soil moisture and vegetation optical depth product is called the Multi-Temporal Dual Channel Algorithm (MT-DCA). It retrieves surface soil moisture and vegetation optical depth (directly related to total water volume in the vegetation canopy) from <a href="https://nsidc.org/data/SPL1CTB_E">SMAP level 1C brightness temperature</a> observations using a robust estimation technique. It is an in-house MIT algorithm and is not an official SMAP product. The data are freely available on 9km and 36km grids from April 2015 to July 2021 in daily time steps.</p> <p>No co-authorship is required for use of this data in publications. However, to properly acknowledge the dataset when publishing any research using the MT-DCA, we ask data users to (1) cite the DOI as an in-text citation and/or in the data acknowledgements in any publication and (2) reference <a href="https://www.sciencedirect.com/science/article/pii/S0034425717302961">Konings et al. (2017</a>) when referring to the MT-DCA in the text. Feel free to send us an email at <a href="mailto:afeld24@mit.edu">afeld24@mit.edu</a> to let us know how you are using the data. </p> <p>The version 5 update is a re-implementation of the MT-DCA using the updated SMAP L1C brightness temperatures. It extends the data through July 2021.</p> <p>II) CONTACT</p> <p>For questions, please email Andrew Feldman at <a href="mailto:afeld24@mit.edu">afeld24@mit.edu</a>.</p> <p>III) ALGORITHM DESCRIPTION</p> <p>The algorithmic approach uses both horizontally and vertically polarized brightness temperatures to retrieve soil moisture and VOD simultaneously. The key innovation of the MT-DCA is that it recognizes that classical dual-channel algorithms are under-determined: brightness temperature observations are correlated and cannot retrieve two unknowns (soil moisture and VOD) (as illustrated in Konings et al, RSE 2016). This creates amplifying errors in retrievals from snapshot dual-channel algorithms. The MT-DCA uses a viable assumption that VOD changes more slowly than soil moisture between overpasses, and uses information from multiple SMAP overpasses to stabilize the retrieval. It is considered a regularization approach similar to the Sobolev Norm regularization. Specifically, this approach is applied to each temporally adjacent pair of overpasses (for SMAP, two overpasses approximately 2-3-days apart), which includes four brightness temperature measurements. For each overpass pair, the soil moisture at both overpasses is retrieved, along with a constant VOD for both overpasses. This leads to two retrievals of each of soil moisture and VOD at any given overpass time: one where the parameters are retrieved using additional TB information from the overpass before and one from the overpass after. Both retrievals of VOD and soil moisture values at each overpass are averaged. Ultimately, VOD is not held constant, but rather is slowed in time between overpasses. A second key innovation of the MT-DCA is that, because the retrievals are no longer under determined, it is also possible to retrieve a constant single scattering albedo for each pixel. The single scattering albedo is estimated through model selection of the value of the parameter that minimizes the sum of all overpass cost functions. The retrieved albedo is also included in the files here. VOD is reported at nadir.</p> <p>The single scattering albedo is assumed constant over the full record of SMAP data, as is currently accepted practice across approaches with SMAP, SMOS, and AMSR. There is a high amount of computational power required to retrieve an albedo over more than three years of SMAP data. Therefore, an adjustment was made: the single scattering albedo was retrieved over the third year of SMAP data (April 1st, 2017 to March 31st, 2018). This constant value was then applied to the other years without requiring the albedo optimization loop. Tests across many individual pixels revealed that albedo in the third year does not differ greatly from albedo over all years and the other individual years. </p> <p>The algorithm is described in more detail in Konings et al. (2017). The algorithm is based on principles explained in more detail in Konings et al. (2016), which describes the original algorithm development using Aquarius observations. See also the related Konings et al. (2015) publication for quantitative justification for the approach. While the dataset has not been officially validated, the MT-DCA soil moisture retrievals show in-situ comparison statistics similarly to the official baseline SMAP soil moisture product (SMAP soil moisture retrieval in-situ assessment can be found in Chan et al. (2016)). Finally, the MT-DCA vegetation optical depth retrievals are not validated due to only sparsely available ground information related to vegetation water content. Nevertheless, information about error propagation into the MT-DCA soil moisture and VOD retrievals as well as VOD error reductions using the MT-DCA regularization technique can be found in Feldman et al. (2021).</p> <p>Chan, S.K., Bindlish, R., O’Neill, P.E., Njoku, E., Jackson, T., Colliander, A., Chen, F., Burgin, M., Dunbar, S., Piepmeier, J., Yueh, S., Entekhabi, D., Cosh, M.H., Caldwell, T., Walker, J., Wu, X., Berg, A., Rowlandson, T., Pacheco, A., McNairn, H., Thibeault, M., Martinez-Fernandez, J., Gonzalez-Zamora, A., Seyfried, M., Bosch, D., Starks, P., Goodrich, D., Prueger, J., Palecki, M., Small, E.E., Zreda, M., Calvet, J.C., Crow, W.T., Kerr, Y., 2016. Assessment of the SMAP Passive Soil Moisture Product. IEEE Trans. Geosci. Remote Sens. 54, 4994–5007. <a href="https://doi.org/10.1109/TGRS.2016.2561938">https://doi.org/10.1109/TGRS.2016.2561938</a></p> <p>Feldman, A.F., D. Chaparro, and D. Entekhabi (2021). Error propagation in microwave soil moisture and vegetation optical depth retrievals. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. In Press.</p> <p>Konings, A.G., M. Piles, N. Das, and D. Entekhabi (2017). L-band vegetation optical depth and effective scattering albedo estimation from SMAP. Remote Sensing of Environment, 198:460-470. <a href="https://doi.org/10.1016/j.rse.2017.06.037">https://doi.org/10.1016/j.rse.2017.06.037</a></p> <p>Konings, A.G., M. Piles, K. Rötzer, K.A. McColl, S. Chan, and D. Entekhabi (2016). Vegetation optical depth and scattering albedo retrieval using time-series of dual-polarized L-band radiometer observations. Remote Sensing of Environment. 172, 178-189. https://doi.org/10.1016/j.rse.2015.11.009</p> <p>Konings, A.G., K.A. McColl, M. Piles and D. Entekhabi (2015): How many parameters can be maximally estimated from a set of measurements? IEEE Geoscience and Remote Sensing Letters, 12(5), 1081-1085. https://doi.org/10.1109/LGRS.2014.2381641</p> <p>IV) QUALITY CONTROL</p> <p>Several conditions can create uncertainty in the MT-DCA retrievals including surface water bodies (lakes, rivers, coastal areas, etc.), radio frequency interference (RFI), highly sloped surfaces (mountainous regions), dense vegetation, frozen ground, and others. The MT-DCA removes time periods of frozen ground and removes pixels with water body fractions of greater than 0.5. SMAP L1C brightness temperatures are adjusted considering RFI and surface water body information. Nevertheless, the MT-DCA retrievals are purposefully not substantially quality controlled to increase the range of science applications of the data. Therefore, the retrievals are subject to uncertainty in regions where and times when these aforementioned issues occur. We suggest the data user familiarize themselves with quality flags in the SMAP algorithm theoretical basis document in <a href="https://nsidc.org/data/SPL3SMP_E">https://nsidc.org/data/SPL3SMP_E</a>. Conservative quality control can be applied using SMAP quality flag information directly applicable to the dataset here. These quality flags can be downloaded from the SMAP official product files at <a href="https://nsidc.org/data/SPL3SMP_E">https://nsidc.org/data/SPL3SMP_E</a>.</p> <p>V) DATA FORMATTING AND FILE NAMES </p> <p>Data are provided in zipped folders in both netcdf4 (.nc) and matfile (.mat) formats. Each zipped folder contains soil moisture, vegetation optical depth, single scattering albedo, latitude, longitude, and time vector information. Note that as an update in Version 5, the zipped folders for 9km .mat files are separated into soil moisture and vegetation optical depth to reduce zip folder size. The other zipped folders still have all variables within them. These variables are provided at a 9km resolution as well as upscaled to 36km. For both .nc and .mat files, the 9km data are provided in 3-month periods with a naming convention of ‘YYYYMM_YYYYMM’ where YYYY is the 4-digit year, and MM is the 2-digit month. The first YYYYMM string represents the first month and the second YYYYMM string is the final month of the period. The 36km data are provided in 12-month periods with the same naming conventions in the file names.</p> <p>Retrievals are obtained from enhanced-resolution brightness temperatures from SMAP that are gridded at 9km. As such, they are on a 9km EASE2-grid. These retrievals are upscaled to 36km and gridded on a 36km EASE2 grid. Additional information and geolocation tools are available at <a href="https://nsidc.org/data/ease/ease_grid2.html">https://nsidc.org/data/ease/ease_grid2.html</a>. </p> <p>Information specific to folders with .nc and .mat formats is given below:</p> <p>a) NETCDF Files (.nc): The folders with netcdf files contain files with the convention MTDCA_YYYYMM_YYYYMM_Xkm_VX.nc where VX is the version number, Xkm is the grid scale, and YYYYMM strings are the first and last months of the range of data saved in the file. Soil moisture, vegetation optical depth, latitude, longitude, and time index information are provided in these files. A map of single scattering albedo for the full time series is saved in a separate file as MTDCA_OMEGA_Xkm_VX.nc along with latitude and longitude information.</p> <p>b) MATFILES (.mat): The folders with matfiles contain individual files for:</p> <ol> <li>Soil moisture: MTDCA_VX_SM_YYYYXX_YYYYXX_Xkm.mat</li> <li>Vegetation Optical Depth: MTDCA_VX_TAU_YYYYXX_YYYYXX_Xkm.mat</li> <li>Single Scattering Albedo: MTDCA_VX_OMEGA_Xkm.mat</li> <li>Latitude/Longitude: SMAPCenterCoordinatesXKM.mat</li> </ol> <p>A datevector variable in each soil moisture and vegetation optical depth file contains information on the year, month, and day corresponding to the timestep of each variable.</p>
Probing Ion Channel Functional Architecture and Domain Recombination Compatibility by Massively Parallel Domain Insertion Profiling
<p>Supplementary Data for a large insertional profiling study described in Coyote-Maestas et al. (2021) Nature Communications.</p>
Functional Lake-to-Channel Connectivity Impacts Lake Ice in the Colville Delta, Alaska
<p>This data is public for a manuscript accepted in JGR Earth Surface. The article will be linked here once it is published. </p> <p>Corresponding code can be found on Github: <a href="https://github.com/whyana/colvilleConnectivity">https://github.com/whyana/colvilleConnectivity</a></p> <p>File and variable descriptions can be found in ReadMe_zenodo.docx OR on Github: <a href="https://github.com/whyana/colvilleConnectivity">https://github.com/whyana/colvilleConnectivity</a></p>
Calcium channel model
<p>This dataset contains two data tables, include "channel_model.mat", "U_potential_3D_e(60).mat". The first data table is used to construct 2D and 3D calcium channel models. The model is constructed based on the model in the Ref. (Corry et al., 2001). The second data table gives the potential distribution in 3D calcium ion channels for Brownian dynamics calculation of ion transport in calcium ion channels.</p>
Fig. 2 in An Alien Monogenean, Ligictaluridus pricei (Platyhelminthes: Ancyrocephalidae), Parasitic on the Channel Catfish Ictalurus punctatus (Actinopterygii: Siluriformes: Ictaluridae) in Japan
Fig. 2. Ligictaluridus pricei (Mueller, 1936). NSMT-Pl 6166. A, Whole mount (ventral view); B, dorsal hamuli; C, ventral hamuli; D, dorsal bar; E, ventral bar; F, marginal hook of pair I; G, marginal hook of pair II; H, marginal hook of pair III; I, marginal hook of pair IV; J, marginal hook of pair V; K, marginal hook of pair VI; L, marginal hook of pair VII; M and N, penes and accessory pieces from two specimens. Scale bars: A, 50 µm; B–N, 10 µm. Abbreviations: ap, accessory piece; cgo, opening of cephalic gland; e, eye; i, marginal hook of pair I; ii, marginal hook of pair II; iii, marginal hook of pair III; in, intestinal caeca; iv, marginal hook of pair IV; mg, Mehlis' gland; o, oötype; od, oviduct; ov, ovary; p, penis; ph, pharynx; pr, prostatic reservoir; sv, seminal vesicle; t, testis; u, uterus; v, marginal hook of pair V; vi, marginal hook of pair VI; vii, marginal hook of pair VII; vl, vitellaria; va, vagina; vp, vaginal pore; vd, vas deferens.
Fig. 1 in An Alien Monogenean, Ligictaluridus pricei (Platyhelminthes: Ancyrocephalidae), Parasitic on the Channel Catfish Ictalurus punctatus (Actinopterygii: Siluriformes: Ictaluridae) in Japan
Fig. 1. Measurement axes of hard parts of Ligictaluridus pricei (Mueller, 1936). Abbreviations: apl, accessory piece length; bl, blade length; bmw, bar median width; btl, bar length; htl, hamulus total length; ltn, length to notch; mhl, marginal hook length; pd, penis diameter; pl, penis length; srl, superficial root length.
Laboratory Open Channel Flow: Video, Waterlevel and Surface Velocity
<p>Video footage of an open channel flow in a laboratory setting, associated with the surface velocity and water level.</p> <p><br> - Video footage was collected using a Raspberry Pi Camera Module v2 attached to a Raspberry Pi 4 at 25fps from three positions and split into roughly 15s chunks.<br> - A "mic+35/IU/TC" ultrasonic sensor (accuracy: ±1%) measured the water level<br> - A "Nortek Vectrino" (accuracy: ±1% ±1mm/s) velocimeter measured the velocity at the surface</p> <p> </p> <p>- The video files can be found in the folders position1, position2 and position3, each file name contains the initial timestamp to match frames to the measurements<br> - The file "waterlevel.csv" contains the timestamps, Waterlevel [mm] and Percentage Full [%]. The waterlevel column is reversed, as the distance between the sensor and the surface was measured. This means, that lower values correspond to higher water levels.<br> - The file "velocity.csv" contains the timestamps and surface velocity measurements [m/s]</p>
All data of the manuscript "A self-sustained charge neutrality lightning model containing the channel decay and reactivation process" submitted to Geophysical Research Letters
<p>The data supports the manuscript entitled "A self-sustained charge neutrality lightning model containing the channel decay and reactivation process”. Microsoft Notepad can open the *.txt files, they contain the channel information of two intracloud flashes (IC1 and IC2) and the channel elctrical parameters at the first fork of positive or negative leader channels. A normal video player software can open Movies S1.avi, and it shows the entire development process of IC1 discharge.</p> <p>The data can be used freely for scientific purposes with the appropriate citation.</p>
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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.