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Supplemental Raw Data for Imboden et al., High-speed mechano-active multielectrode array for investigating rapid stretch effects on cardiac tissue
<p>The file contains the central raw data underlying Figure 5 and, in the supplementary material, Figure 13 of the article 'High-speed mechano-active multielectrode array for investigating rapid stretch effects on cardiac tissue'.</p>
P-wave arrival data of Ilan Texan Array
<p>The file contains P-wave arrival data used to invert for the P-wave velocity model of NE Taiwan which is presented in:</p> <p>High-Resolution 3D P-wave Velocity Structures under NE Taiwan and their Tectonic Implications</p> <p>Po-Li Su, Po-Fei Chen, Chien-Ying Wang</p>
Shaping photovoltaic array output to align with changing wholesale electricity price profiles
<p>This repository includes python scripts and input/output data associated with the following publication:</p> <p>[1] Brown, P.R.; O'Sullivan, F. "Shaping photovoltaic array output to align with changing wholesale electricity price profiles." Applied Energy 2019. <a href="http://doi.org/10.1016/j.apenergy.2019.113734">https://doi.org/10.1016/j.apenergy.2019.113734</a></p> <p>Please cite reference [1] for full documentation if the contents of this repository are used for subsequent work.</p> <p>Some of the scripts and data are also used in the following working paper:</p> <p>[2] Brown, P.R.; O'Sullivan, F. "Spatial and temporal variation in the value of solar power across United States electricity markets". Working Paper, MIT Center for Energy and Environmental Policy Research. 2019. <a href="http://ceepr.mit.edu/publications/working-papers/705">http://ceepr.mit.edu/publications/working-papers/705</a></p> <p>All code is in python 3 and relies on a number of dependencies that can be installed using pip or conda.</p> <p><strong>Contents</strong></p> <ul> <li>pvvm.zip : Python module with functions for modeling PV generation, calculating PV revenues and capacity factors, and optimizing PV orientation.</li> <li>notebooks.zip : Jupyter notebooks, including: <ul> <li>pvvm-pvtos-data.ipynb: Example scripts used to download and clean input LMP data, determine LMP node locations, and reproduce some figures in reference [1]</li> <li>pvvm-pvtos-analysis.ipynb: Example scripts used to perform the calculations and reproduce some figures in reference [1]</li> <li>pvvm-pvtos-plots.ipynb: Scripts used to produce additional figures in reference [1]</li> <li>pvvm-example-generation.ipynb: Example scripts demonstrating the usage of the PV generation model and orientation optimization</li> </ul> </li> <li>html.zip : Static images of the above Jupyter notebooks for viewing without a python kernel</li> <li>data.zip : Day-ahead and real-time nodal locational marginal prices (LMPs) for CAISO, ERCOT, MISO, NYISO, and ISONE. <ul> <li>At the time of publication of this repository, permission had not been received from PJM to republish their LMP data. If permission is received in the future, a new version of this repository will linked here with the complete dataset.</li> </ul> </li> <li>results.zip : Simulation results associated with reference [1] above, including modeled revenue, capacity factor, and optimized orientations for PV systems at all LMP nodes</li> </ul> <p><strong>Data terms and usage notes</strong></p> <ul> <li>ISO LMP data are used with permission from the different ISOs. Adapting the MIT License (<a href="http://opensource.org/licenses/MIT">https://opensource.org/licenses/MIT</a>), "The data are provided 'as is', without warranty of any kind, express or implied, including but not limited to the warranties of merchantibility, fitness for a particular purpose and noninfringement. In no event shall the authors or sources be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the data or other dealings with the data." Copyright and usage permissions for the LMP data are available on the ISO websites, linked below.</li> <li>ISO-specific notes: <ul> <li>CAISO data from <a href="http://oasis.caiso.com/mrioasis/logon.do">http://oasis.caiso.com/mrioasis/logon.do</a> are used pursuant to the terms at <a href="http://www.caiso.com/Pages/PrivacyPolicy.aspx#TermsOfUse">http://www.caiso.com/Pages/PrivacyPolicy.aspx#TermsOfUse</a>.</li> <li>ERCOT data are from <a href="http://www.ercot.com/mktinfo/prices">http://www.ercot.com/mktinfo/prices</a>.</li> <li>MISO data are from <a href="http://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/">https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/</a> and <a href="https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/market-report-archives/">https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/market-report-archives/</a>.</li> <li>PJM data were originally downloaded from <a href="https://www.pjm.com/markets-and-operations/energy/day-ahead/lmpda.aspx">https://www.pjm.com/markets-and-operations/energy/day-ahead/lmpda.aspx</a> and <a href="https://www.pjm.com/markets-and-operations/energy/real-time/lmp.aspx">https://www.pjm.com/markets-and-operations/energy/real-time/lmp.aspx</a>. At the time of this writing these data are currently hosted at <a href="https://dataminer2.pjm.com/feed/da_hrl_lmps">https://dataminer2.pjm.com/feed/da_hrl_lmps</a> and <a href="https://dataminer2.pjm.com/feed/rt_hrl_lmps">https://dataminer2.pjm.com/feed/rt_hrl_lmps</a>.</li> <li>NYISO data from <a href="http://mis.nyiso.com/public/">http://mis.nyiso.com/public/</a> are used subject to the disclaimer at <a href="https://www.nyiso.com/legal-notice">https://www.nyiso.com/legal-notice</a>.</li> <li>ISONE data are from <a href="https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-da-hourly">https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-da-hourly</a> and <a href="https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-rt-hourly-final">https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-rt-hourly-final</a>. The Material is provided on an "as is" basis. ISO New England Inc., to the fullest extent permitted by law, disclaims all warranties, either express or implied, statutory or otherwise, including but not limited to the implied warranties of merchantability, non-infringement of third parties' rights, and fitness for particular purpose. Without limiting the foregoing, ISO New England Inc. makes no representations or warranties about the accuracy, reliability, completeness, date, or timeliness of the Material. ISO New England Inc. shall have no liability to you, your employer or any other third party based on your use of or reliance on the Material.</li> </ul> </li> <li>Data workup: LMP data were downloaded directly from the ISOs using scripts similar to the pvvm.data.download_lmps() function (see below for caveats), then repackaged into single-node single-year files using the pvvm.data.nodalize() function. These single-node single-year files were then combined into the dataframes included in this repository, using the procedure shown in the pvvm-pvtos-data.ipynb notebook for MISO. We provide these yearly dataframes, rather than the long-form data, to minimize file size and number. These dataframes can be unpacked into the single-node files used in the analysis using the pvvm.data.copylmps() function.</li> </ul> <p><strong>Code license and usage notes</strong></p> <ul> <li>Code (*.py and *.ipynb files) is provided under the <a href="https://opensource.org/licenses/MIT">MIT License</a>, as specified in the pvvm/LICENSE file.</li> <li>Updates to the code, if any, will be posted in the non-static repository at <a href="https://github.com/patrickbrown4/pvvm_pvtos">https://github.com/patrickbrown4/pvvm_pvtos</a>. The code in the present repository has the following version-specific dependencies: <ul> <li>matplotlib: 3.0.3</li> <li>numpy: 1.16.2</li> <li>pandas: 0.24.2</li> <li>pvlib: 0.6.1</li> <li>scipy: 1.2.1</li> <li>tqdm: 4.31.1</li> </ul> </li> <li>To use the NSRDB download functions, modify the "settings.py" file to insert a valid NSRDB API key, which can be requested from <a href="https://developer.nrel.gov/signup/">https://developer.nrel.gov/signup/</a>. Locations can be specified by passing latitude, longitude floats to pvvm.data.downloadNSRDBfile(), or by passing a string googlemaps query to pvvm.io.queryNSRDBfile(). To use the googlemaps functionality, request a googlemaps API key (<a href="https://developers.google.com/maps/documentation/javascript/get-api-key">https://developers.google.com/maps/documentation/javascript/get-api-key</a>) and insert it in the "settings.py" file.</li> <li>Note that many of the ISO websites have changed in the time since the functions in the pvvm.data module were written and the LMP data used in the above papers were downloaded. As such, the pvvm.data.download_lmps() function no longer works for all ISOs and years. We provide this function to illustrate the general procedure used, and do not intend to maintain it or keep it up to date with the changing ISO websites. For up-to-date functions for accessing ISO data, the following repository (no connection to the present work) may be helpful: <a href="https://github.com/catalyst-cooperative/pudl">https://github.com/catalyst-cooperative/pudl</a>.</li> </ul>
Fig. 1 in Streetlights attract a broad array of beetle species
Fig. 1. Boxplots of beetle collections in each trap (each record here is a night of collection). These are standard boxplots, with whiskers representing the more extreme values within 1.5 inter-quartile range away from each quartile. For phylogenetic diversity, we superimpose 100 boxplots obtained from different random phylogenetic trees, to highlight the sensitivity of results to phylogenetic uncertainty. Phylogenetic uncertainty has little impact on the results.
Fig. 2. Correlations between diversity and abundance across collections. Each record represents a in Streetlights attract a broad array of beetle species
Fig. 2. Correlations between diversity and abundance across collections. Each record represents a night of collection in a given trap. Colors represent traps, with color code being the same as in Figs. 1, 3 and 4. Line represents the regression line between the two variables with intercept forced to 0.
Fig. 4 in Streetlights attract a broad array of beetle species
Fig. 4. Preferences estimated by LDA. Boxes enclose graphs associated with preference classes estimated by LDA, numbered as 1–3. Each pie chart indicates the posterior probability of preference for each trap for a given preference class (e.g. preference class 1 includes individuals exclusively attracted to Hg). Underneath each pie chart, bars indicate the number of species with>0.5 posterior probability of being associated with the corresponding preference class, binned by higher taxon.
Fig. 3. Rarefaction curves. Solid lines indicate mean and dashed lines indicate 95 in Streetlights attract a broad array of beetle species
Fig. 3. Rarefaction curves. Solid lines indicate mean and dashed lines indicate 95% confidence intervals. Inset shows the full curves for Hg and Na.
Cryo-EM/Cryo-ET raw images and tilt series for the figures in the paper entitled "Angle Between DNA Linker and Nucleosome Core Particle Regulates Array Compaction by Individual-Particle Cryo-Electron Tomography"
<p>Cryo-EM and cryo-ET raw images and tilt-series for the 3D reconstructions showed in the Figures of the paper entilted "Angle between DNA linker and nucleosome core particle regulates array compaction by individual-particle cryo-electron tomography"</p>
TaqMan array sequence set for 52-target respiratory pathogen array
<p>Sequence list for probes and primers used in the respiratory Taqman array card investigated in project, protocol 10.5281/zenodo.5081880</p>
Additional dataset concerning "Observations of mantle seismic anisotropy using array techniques: shear-wave splitting of beamformed SmKS phases"
<p>SplitRacer input and output for beams and single-station splitting measurements for event 201007290731. This dataset was used in "Observations of mantle seismic anisotropy using array techniques: shear-wave splitting of beamformed SmKS phases" by Jonathan Wolf, Daniel A. Frost, Maureen D. Long, Ed Garnero, Adeolu O. Aderoju, Neala Creasy and Ebru Bozdag. The manuscript is available at <a href="https://doi.org/10.1029/2022JB025556">https://doi.org/10.1029/2022JB025556</a>.</p>
A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata
<p><strong>Summary</strong></p> <p>Photovoltaic (PV) energy generation plays a crucial role in the energy transition. Small-scale, residential PV installations are deployed at an unprecedented pace, and their safe integration into the grid necessitates up-to-date, high-quality information. Overhead imagery is increasingly used to improve the knowledge of residential PV installations with machine learning models capable of automatically mapping these installations. However, these models cannot be reliably transferred from one region or imagery source to another without incurring a decrease in accuracy. To address this issue, known as distribution shift, and foster the development of PV array mapping pipelines, we propose a dataset containing aerial images, segmentation masks, and installation metadata. We provide installation metadata for more than 28000 installations. We provide ground truth segmentation masks for 13000 installations, including 7000 with annotations for two different image providers. Finally, we provide installation metadata that matches the annotation for more than 8000 installations. Dataset applications include end-to-end PV registry construction, robust PV installations mapping, and analysis of crowdsourced datasets.</p> <p>This dataset contains the complete records associated with the article "A crowdsourced dataset of aerial images of solar panels, their segmentation masks, and characteristics", published in Scientific data. The article is accessible here : <a href="https://www.nature.com/articles/s41597-023-01951-4">https://www.nature.com/articles/s41597-023-01951-4</a> These complete records consist of:</p> <ol> <li>The complete training dataset containing RGB overhead imagery, segmentation masks and metadata of PV installations (folder <strong>bdappv</strong>),</li> <li>The raw crowdsourcing data, and the postprocessed data for replication and validation (folder <strong>data</strong>).</li> </ol> <p><strong>Data records</strong></p> <p>Folders are organized as follows:</p> <ul> <li><strong>bdappv/</strong> Root data folder <ul> <li><strong>google / ign:</strong> One folder for each campaign <ul> <li><strong>img/</strong>: Folder containing all the images presented to the users. This folder contains 28807 images for Google and 17325 images for IGN.</li> <li><strong>mask/</strong>: Folder containing all segmentations masks generated from the polygon annotations of the users. This folder contains 13303 masks for Google and 7686 masks for IGN.</li> </ul> </li> <li><em>metadata.csv</em> The <code>.csv</code> file with the installations' metadata.</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>data/ </strong>Root data folder <ul> <li><strong>raw/</strong> Folder containing the raw crowdsourcing data and raw metadata; <ul> <li><em>input-google.json</em>: <code>.json </code>input data data containing all information on images and raw annotators’ contributions for both phases (clicks and polygons) during the first annotation campaign;</li> <li><em>input-ign.json</em>:<em> </em><code>.json </code>input data containing all information on images and raw annotators’ contributions for both phases (clicks and polygons) during the second annotation campaign;</li> <li><em>raw-metadata.json</em>: <code>.json </code>output containing the PV systems’ metadata extracted from the BDPV database before filtering. It can be used to replicate the association between the installations and the segmentation masks, as done in the notebook metadata.</li> </ul> </li> <li><strong>replication/</strong> Folder containing the compiled data used to generate the segmentation masks; <ul> <li><strong>campaign-google/campaign-ign</strong>: One folder for each campaign <ul> <li><em>click-analysis.json</em>: <code>.json </code>output on the click analysis, compiling raw input into a few best-guess locations for the PV arrays. This dataset enables the replication of our annotations,</li> <li><em>polygon-analysis.json</em>: <code>.json </code>output of polygon analysis, compiling raw input into a best-guess polygon for the PV arrays.</li> </ul> </li> </ul> </li> <li><strong>validation/</strong> Folder containing the compiled data used for technical validation. <ul> <li><strong>campaign-google/campaign-ign</strong>: One folder for each campaign <ul> <li><em>click-analysis-thres=1.0.json</em>: <code>.json </code>output of the click analysis with a lowered threshold to analyze the effect of the threshold on image classification, as done in the notebook annotation;</li> <li><em>polygon-analysis-thres=1.0.json</em>: <code>.json </code>output of polygon analysis, with a lowered threshold to analyze the effect of the threshold on polygon annotation, as done in the notebook annotations.</li> </ul> </li> <li><em>metadata.csv</em>: the <code>.csv </code>file of filtered installations' metadata.</li> </ul> </li> </ul> </li> </ul> <p><strong>License</strong></p> <p>We extracted the thumbnails contained in the <strong>google/img/</strong> folder using Google Earth Engine API and we generated the thumbnails contained in the <strong>ign/img</strong><strong>/</strong> folder from high resolution tiles downloaded from the online IGN portal accessible here: <a href="https://geoservices.ign.fr/bdortho">https://geoservices.ign.fr/bdortho</a>. Images provided by Google are subjet to Google's terms and conditions. Images provided by the IGN are subject to an open license 2.0.</p> <p>Access the terms and conditions of Google images at this URL: <a href="https://www.google.com/intl/en/help/legalnotices_maps/">https://www.google.com/intl/en/help/legalnotices_maps/</a></p> <p>Access the terms and conditions of IGN images at this URL: <a href="https://www.etalab.gouv.fr/wp-content/uploads/2018/11/open-licence.pdf">https://www.etalab.gouv.fr/wp-content/uploads/2018/11/open-licence.pdf</a></p>
Data Repository for MYRiAD: A Multi-Array Room Acoustic Database
<p>In the development of acoustic signal processing algorithms, their evaluation in various acoustic environments is of utmost importance. In order to advance evaluation in realistic and reproducible scenarios, several high-quality acoustic databases have been developed over the years. In this paper, we present another complementary database of acoustic recordings, referred to as the Multi-arraY Room Acoustic Database (MYRiAD). The MYRiAD database is unique in its diversity of microphone configurations suiting a wide range of enhancement and reproduction applications (such as assistive hearing, teleconferencing, or sound zoning), the acoustics of the two recording spaces, and the variety of contained signals including 1214 room impulse responses (RIRs), reproduced speech, music, and stationary noise, as well as recordings of live cocktail parties held in both rooms. The microphone configurations comprise a dummy head (DH) with in-ear omnidirectional microphones, two behind-the-ear (BTE) pieces equipped with 2 omnidirectional microphones each, 5 external omnidirectional microphones (XMs), and two concentric circular microphone arrays (CMAs) consisting of 12 omnidirectional microphones in total. The two recording spaces, namely the SONORA Audio Laboratory (SAL) and the Alamire Interactive Laboratory (AIL), have reverberation times of 2.1s and 0.5s, respectively. Audio signals were reproduced using 10 movable loudspeakers in the SAL and a built-in array of 24 loudspeakers in the AIL. MATLAB and Python scripts are included for accessing the signals as well as microphone and loudspeaker coordinates. For a detailed description, please refer to the paper (<a href="https://arxiv.org/abs/2301.13057">preprint</a>, <a href="https://asmp-eurasipjournals.springeropen.com/articles/10.1186/s13636-023-00284-9">published</a>).</p> <p>Two files are provided, containing two different versions of the database:</p> <table> <tbody> <tr> <td><strong>MYRiAD_V2.zip</strong></td> <td>The full version of the database (31.3 GB).</td> </tr> <tr> <td><strong>MYRiAD_V2</strong><strong>_econ</strong><strong>.zip </strong></td> <td>The economy-sized version, containing source signals and RIRs only (201.7 MB).</td> </tr> </tbody> </table> <p>If you use the database, please cite the paper as follows:</p> <p>@article{dietzen2023myriad,<br> author = {Dietzen, T. and Ali, R. and Taseska, M. and van Waterschoot, T.},<br> title = {{MYRiAD}: A Multi-Array Room Acoustic Database},<br> journal = {EURASIP J. Audio Speech Music Process.},<br> volume = {2023, article no. 17},<br> number = {},<br> month = {Apr.},<br> year = {2023},<br> pages = {1--14}<br> }</p> <p>___________________________________________________________________________________________________________</p> <p>Change log (as compared to Version 1.0):</p> <ol> <li>Fixed erroneous file names in /audio/AIL/SU1/P2/.</li> <li>In the full version, applied a time shift to some of the speech, noise, and music recordings in the SAL (at most 2 samples, compensating for a slow phase drift, see manuscript for further details).</li> <li>Created an economy-sized version of the database containing source signals and RIRs only.</li> <li>Adjusted the following scripts for the economy-sized version: <br> - /tools/MATLAB/load_audio_data.m<br> - /tools/Python/load_audio_data.py</li> </ol>
Supplementary Data for 'A sensitive array for microRNA expression profiling (miChip) based on locked nucleic acids (LNA).'
<p>Supplementary Data for 'Castoldi, M., Schmidt, S., Benes, V., Noerholm, M., Kulozik, A.E., Hentze, M.W. and Muckenthaler, M.U., 2006. A sensitive array for microRNA expression profiling (miChip) based on locked nucleic acids (LNA). <em>Rna</em>, <em>12</em>(5), pp.913-920.'</p>
Figures of Influence of Time-Varying Atmospheric Channel on Time Reversal Spatial Power Combination of Sparse Array on Ground
<p>A mathematical model of TR (Time Reversal) power combination in the slowly time-varying atmospheric channel and the concept of channel time-varying factor are proposed to study the feasibility of TR technique for spatial power combination in incompletely reciprocal channels. Combining the theoretical analysis and Monte Carlo simulations, we analyze the relationship between the power combination efficiency and the permittivity of the time-varying atmospheric channel, which follows a normal distribution.</p> <p>In this data file, simulation results obtained by commercial software MATLAB are listed. The results indicate that the power combination efficiency of the TR technique will be lower than that of the completely reciprocal channel if the atmospheric channel is slowly varied, and the faster the channel changes, the lower the efficiency becomes. In order to keep the power combination efficiency above 50%, the channel time-varying factor needs to be less than about 54.3% of the signal period. And when it takes more than 90%, the signals radiated by each antenna have been completely incoherent at target point.</p>
The Thousand-Pulsar-Array programme on MeerKAT - VIII. The subpulse modulation of 1198 pulsars
<p>These datasets and document are online appendices beyond those provided in the main journal manuscript "The Thousand-Pulsar-Array programme on MeerKAT - VIII. The subpulse modulation of 1198 pulsars" by Song et al. published in Monthly Notices of the Royal Astronomical Society in 2023.</p> <p>File supplementary_appendices.pdf contains supplementary online appendices.</p> <p>Files appendix_a1_table_observationalparameters.pdf and appendix_a2_table_spectralmeasurements.pdf are two tables summarising the observational related parameters, and subpulse modulation measurements respectively. </p> <p>File appendix_spectralfigures.pdf contains spectral figures for all sources.</p> <p>Zip files (RA*_spectralfigures.zip) contain original and shuffled spectral figures for individual pulsars within a specific RA range.</p>
Topological kinetic crossover in a nanomagnet array
<p>Common explanations of equilibrium thermodynamics depend critically on ergodicity, i.e., the ability to explore all available states, but ergodic kinetics can be constrained by a system's topology. We report an experimental study of a model nanomagnetic array, in which such constraints visibly affect the behavior. In this system, magnetic excitations are connected in thermally active one-dimensional strings whose motion can be imaged in real-time. At high temperatures, the kinetics include the merging, breaking, and reconnecting of strings, resulting in the system transitioning between topologically distinct configurations. Below a crossover temperature, however, the string motion is dominated by simple changes in length and shape. In this low-temperature regime, the system is energetically stable because of the inability to explore the full set of possible topological configurations. This kinetic crossover suggests a generalizable conception of topologically broken ergodicity and limited equilibration.</p>
Measurements of nearshore waves through coherent arrays of free-drifting wave buoys
<p>Surface gravity wave breaking occurs along coastlines in complex spatial and temporal patterns that significantly impact erosion, scalar transport, and flooding. Numerical models are used to predict these processes, but many models lack sufficient evaluation with observations during storm events. To fill the need for more nearshore wave measurements during extreme conditions, we deployed coherent arrays of small-scale, free-drifting wave buoys named microSWIFTs. The result is a large dataset covering a range of conditions. The microSWIFT is a small wave buoy with a GPS module, and Inertial Measurement Unit (IMU) used to directly measure the buoy's global position, horizontal velocities, rotation rates, accelerations, and heading. We use an Attitude and Heading Reference System (AHRS), 9 degrees-of-freedom Kalman filter to rotate the measured accelerations from the reference frame of the buoy to the Earth reference frame. We then use the corrected accelerations to compute the vertical velocity and sea surface elevation. The measurements were collected over a 27-day field experiment in October 2021 at the US Army Corps of Engineers Field Research Facility in Duck, NC. The microSWIFTs were deployed as a series of coherent arrays. They all sampled simultaneously with a common time reference, leading to a robust spatial and temporal dataset during each deployment. We evaluate wave spectral energy density estimates from individual microSWIFTs by comparing them with a nearby acoustic waves and currents (AWAC) sensor. We also compare significant wave height estimates from the coherent arrays with the nearby AWAC estimates. A zero crossing algorithm is applied to each buoy time series of sea surface elevation to extract realizations of measured surface gravity waves, yielding 116,307 wave realizations throughout the experiment. These measurements spanned offshore significant wave heights ranging from 0.5 meters to 3 meters and peak wave periods ranging from 5 to 15 seconds over the entire experiment. </p>
High-throughput crystallography for rapid early-stage fragment growth from crude arrays by low-cost robotics
<p>Data to support the paper - <em>High-throughput crystallography for rapid early-stage fragment growth from crude arrays by low-cost robotics</em>. Data includes a summary of X-ray and LCMS results for the reactions executed on the OpenTrons, output reports and summaries from MSCheck (semi-automated LCMS analyzer tool) and the Python scripts used to execute single and multistep chemistry on the OpenTrons.</p> <p><strong>Abstract</strong></p> <p>We demonstrate that a simple workflow of array synthesis, combining low-cost robotics with analytic techniques to deconvolute crude reaction mixtures, is an effective way to collect structural data on a binding site. Starting from the high information content of the crystallographic fragment screens on PHIP(2) (second bromodomain of the pleckstrin homology domain interacting protein), a collection of more than 1800 compounds was enumerated. Several thousand <em>Crude Reaction Mixtures</em> (CRMs) were synthesized on one robotic platform, an OpenTrons OT-1 liquid handler, using reaction sequences of up to 5 chemical steps. Analysis via MScheck, an algorithm-based system for finding a m/z in a CRM, significantly shortened product identification protocol times. 957 usable X-ray diffraction datasets were acquired, which resolved as 22 reaction products binding to the protein, 19 with conserved poses relative to the original fragment and 3 with a new, unexpected binding pose. The 22 crystallographic hit compounds were subsequently tested with peptide displacement alpha-screen assay and time-resolved grating-coupled interferometry-based biosensor assays, which confirmed one molecule with an IC<sub>50</sub> = 34 μM and K<sub>D</sub> = 50 μM, from an inactive fragment. The procedures described are entirely formulaic and engineerable and the method is eminently scalable. We anticipate that this cheap, low solvent-use approach will yield vast amounts of data, enabling rapid SAR landscape exploration around fragments, leading to faster fragment to lead times.</p>
Experimental characterization of TERIPHIC 4-fold and 8-fold InP photodiode arrays - Detection of 50Gbaud signals with NRZ and PAM-4 modulation formats
<p>The data sets refer to the results from the experimental characterization of the TERIPHIC 4-fold and 8-fold InP-photodiode arrays, which operate in the O-band. The experimental setup consisted of a commercially available off-the-shelf (COTS) laser source, a COTS Mach-Zehnder modulator, and the TERIPHIC PD arrays. For the purposes of the experiments, signals with NRZ and PAM-4 modulation formats and 50 Gbaud symbol rates were generated, transmitted, and detected by the TERIPHIC photodetectors. Two different subassemblies were evaluated. The first subassembly had four InP photodiodes and the second eight. The generated photocurrents were sampled by a real-time oscilloscope with a 256 Gbaud sample rate. The data sets are actually the samples extracted from the oscilloscope. The data sets have a filename in the format “Symbol rate#Modulation formats#PDn#Transmission length#version#Subassemblyn.bin”, where<br> Symbol rate = 50 Gbaud<br> PDn, for n= 1,2, ...,8<br> Transmission length = B2B (0 km), 2 km, and 10 km<br> Subassembly for n=1 and 2<br> <br> The data sets are parts of the Open data management plan of the TERIPHIC ICT project (GA No. 825502)</p>
Experimental characterization of TERIPHIC quad InP EML array - Generation and transmission of 50Gbaud signals with NRZ and PAM-4 modulation formats
<p>The data sets refer to the results from the experimental characterization conducted on the TERIPHIC quad InP-EML array, which operates in the O-band. Each EML incorporates a DFB laser, an Electroabsorption modulator, and a Semiconductor Optical Amplifier to provide additional power boost, all on the same die. Three out of the four EMLs were characterized successively and generated signals at symbol rates of 50 Gbaud and transmitted them over various fiber lengths. The modulated optical signals were detected by a single commercially avalaible photodetector with more than 70 GHz 3-dB bandwidth. The generated electrical signals were then sampled by a real-time oscilloscope with a 256 Gbaud sample rate. A low-pass filter with a 50 GHz 3-dB cut-off frequency was digitally applied to the oscilloscope to improve the signal-to-noise ratio.<br> The data sets have a filename in the format “Symbol rate#Modulation formats#EMLn#Transmission length#version.bin”, where<br> SYmbol rate = 50 Gbaud<br> EMLn, for n= 2, 3, 4<br> Transmission length = B2B (0 km), 2 km, and 10 km<br> Version: v1, v2 and v3. Same operating parameters but at different time index<br> <br> The data sets are parts of the Open data management plan of the TERIPHIC ICT project (GA No. 825502)</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.