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30,131 results for “Control”
Dataset for simulations of a beamline that controls longitudinal phase space whilst transporting LWFA electrons to an undulator
<p>This dataset relates to a design for a particle accelerator beamline. The beamline transports particles (electrons) from a laser wakefield accelerator (LWFA) source to an undulator. The unique design allows the 'chirp' or 'longitudinal phase space' of the electron distribution to be sheared during transport.<br> The dataset contains: 1) Initial bunch distributions created by the ASTRA generator program and conversion to MAD8 program format; 2) a working MAD8 batch file; 3) Three set-ups of the beamline to provide positive, negative or no shear; 4) Tracking simulation input files to track the initial distributions through each beamline set-up; 5) Output electron distributions that result from each tracking simulation.</p>
Data for nocturnal plant respiration is under strong non-temperature control
<p>Data set contains</p> <p>1- Annual output (2000-2018) of simulated plant respiration and net primary productivity from JULES with standard (Q10=2) and temperature dependent Q10 (TDQ<sub>10</sub>) with and without incorporation of nocturnal non-temperature control of respiration. Related readme file is included (Readme JULES output.txt).</p> <p>2-Python code (manuscript-code.zip) and data sets (Source Data.zip) to produce all manuscript figures including supplementary material. Readme file is included within mansucript-code.zip.</p> <p> </p>
Cerebellum spiking data during simulated saccade control
<p>This repository contains data generated by simulating the cerebellum spiking neural network for saccade motor control. Please refer to the article at https://doi.org/10.1101/2022.03.14.483471.</p> <p>Please check the journal of PLOS computational biology for the published version of this article (inpress).</p> <p>The python scripts will generate plots from the simulation datasets. In order to do so, please unzip the datasets and put each of the data subfolders into the current folder along with the plotting scripts.</p> <p>The spiking neuronal circuit datasets have been generated using the NEST spiking neural network library (https://www.nest-simulator.org/), in .gdf format. Users can refer to the presented python scripts to understand how to read from and plot the data. Additionally, for interested users, the trained PF-PC synaptic weights from saccade simulations are also available as EXCEL files in the same dataset folders.</p> <p>In case of queries or any problems, please email me at the email id given in the article linked above.</p>
Dataset and codes for 'Climatic control on seasonal variations of glacier surface velocity'
<p><strong>This repository contains the codes and processed data used to retrieve 10-day changes in glacier surface velocity over the Western Pamir.</strong></p> <p>The supp_CODES.zip contains all details and codes to use COSI-CORR (<a href="http://www.tectonics.caltech.edu/slip_history/spot_coseis/">http://www.tectonics.caltech.edu/slip_history/spot_coseis/</a>) to process a large batch of satellite images. The images can be downloaded directly via <a href="https://earthexplorer.usgs.gov/">https://earthexplorer.usgs.gov/</a> or <a href="https://scihub.copernicus.eu/">https://scihub.copernicus.eu</a>. Please read the Methods and Data section of the associated manuscript for details.</p> <p> </p> <p>The Matrix_velocities.zip contains, for each of the 48 investigated glaciers, the DEM, X, Y (NANNI_2022_supp_glacier_centreline_DEM_XY_1px_30m_1.txt) as well as a matrix of n*m with m the distance along flow and n the number of time step over which the velocity is calculated (NANNI_2022_supp_glacier_centreline_vel_matrix_1px_30m_1.txt), ans the associated figure that show the multi year velocity changes together with the one year average and the along centreline profiles. An example is shown in the two figures for glacier 48 in the main repository.</p> <p> </p> <p>The NANNI_2022_supp_glacier_characteristics file contains the glacier characteristics (48*8), as shown in the associated figures.</p> <p> </p> <p>The NANNI_2022_supp_pickedpoints_migration_AUTUMN/SPRING contains the automatically picked points for the onset of the acceleration in Spring and Autmun for each glacier. The headers contains the information, and the files contains is shown in the associated figure.</p> <p>the temperature profiles used to calculate the Iso 0C are in NANNI_2022_supp_temp_perday_fedchenko_2400m</p> <p>The position of each 48 glacier is shown in the associated figure.</p> <p> </p> <p>You can also find the processed velocity fields (velocity magnitude) under the different path an row: p151r33.zip and p152r33.zip for Landsat8, T42SYJ.zip and T43SBD.zip for Sentinel 2. In these folder you will a find a .tif file names similar to:</p> <p><em>Working_cosicorr_windows_FCorr_16days_p152r33_159_175_AB_1101110_Filtered_correlations_p152r33_filtered_abs.tif</em></p> <p>The name of the files gives information about the time span used (16days), the path and raw (p152r33), the data of the slave in DOY from 2013 (159) and of the master (175).</p> <p>The Statistics.zip file contains for each path and row the associated DEM, glacier mask (RGI), median magnitude (ABS), median NS displacement (NS), median EW displacemnt (EW), with the associated median absolute deviation (MAD). The files containing 'bflt' corresponds to the values computed before the filtering procedure, and the one without, after the filtering procedure. </p> <p>The .tif files are not georeferenced, but are all projected on the same grid with a 30m square pixel size on a UTM 33 42N projection.</p> <p> </p> <p> </p> <p>Please contact me for any question.</p> <p> </p> <div class="notranslate"> </div>
Occipital Nerve Stimulation Selectively Modulates Top-down Inhibitory Control
<p><strong>Objective:</strong> Here we investigate the effect of occipital nerve stimulation using low-gamma range alternating current on goal-directed and stimulus-driven attention and inhibitory training and performance. We sought to determine if stimulation modulated performance over a two-day period. <strong>Methods</strong>: We studied this effect in 47 participants recruited in one of two experiments. The goal-directed task used the stop-signal reaction time task (SSRT) during stimulation and stop-change reaction time (SCRT) in a 24-hour follow-up. Stop-signal reaction time (SSRT) and Stop-change reaction time (SCRT) were recorded in seconds, calculated using a non-integration method. SSRT/SCRT and accuracy were used as outcome measures. The stimulus-driven task used a sustained-attention reaction time task (SART), and reaction time and inhibition (NoGo) accuracy were used as outcome measures. <strong>Results</strong>: Compared to the control group, the stimulation group had improved SCRT 24 hours after combined stimulation and training. No difference in accuracy on either day were present. No difference between groups arose in the SART during training or testing. </p>
Flow manipulation in a Hele-Shaw cell with an electrically-controlled viscous obstruction
<p>The dataset named “Dataset: Flow manipulation in a Hele-Shaw cell with an electrically-controlled viscous obstruction” consists of Raw time-averaged images, which are generated by sequence of 100 frames extracted from experimental videos captured at various voltages (5V, 10V, 15V, 20V, and 50V), and saved as .tif files. These images were analysed to produce the data used in figure 2 and 3 of the article. The dataset also includes two Excel files named as “Figure 2_Experimental data.xlsx” and “Figure 3_Experimental data.xlsx”. These excel files contain the data used to create the experimental plots shown in Figure 2C, and Figure 3 of the research article respectively.</p> <p>In the “Figure 2C_Experimental Data.xlsx” excel file, each sheet corresponds to a different voltage value shown in the figure, and contains three columns: A, B, and C. which represents the X-location, Y-location, and orientation angle (in degrees) of the experimental plot (red rods in the figure) respectively. This plot is overlaid on the model data (black rods in the figure) and displayed in Figure 2C given in the article.</p> <p><span>The “Figure 3_Experimental data.xlsx” file contains three sheets for each voltage (5V, 10V, 15V, 20V, and 50V) and each of these three sheets provide data at three different X-locations (X=579, X= 1079, and X= 1779) as a function of Y-location as shown in the Figure 3 of the article. Each sheet has five columns: A, B, C, D, and E. These columns represent the X-location, Y-location, Orientation angle (in degrees), Coherency, and Error in the orientation angle (in degrees), respectively. These data points are used to create the experimental scatter plot shown in Figure 3 of the article.</span></p>
Preindustrial Control (PIC) Run for OSU-UVic2.9.10 (MOBI2.2) Input Data
<p>Input data required for a simulation of the preindustrial control (PIC) simulation with the OSU version of the University of Victoria climate model (version 2.9) with the Model of Ocean Biogeochemistry and Isotopes (MOBI2.2).</p>
Catalyst Supraparticles: Tuning the Structure of Spray‐Dried Pt/SiO2 Supraparticles via Salt‐Based Colloidal Manipulation to Control their Catalytic Performance
<p>This data publication is based on the metadata and raw datasets underlying the manuscript: P. Groppe, J. Reichstein, S. Carl, C. Cuadrado Collados, B.-J. Niebuur, K. Zhang, B. Apeleo Zubiri, J. Libuda, T. Kraus, T. Retzer, M. Thommes, E. Spiecker, S. Wintzheimer, K. Mandel, Catalyst Supraparticles: Tuning the Structure of Spray-Dried Pt/SiO2 Supraparticles via Salt-Based Colloidal Manipulation to Control their Catalytic Performance. Small 2024, 2310813. https://doi.org/10.1002/smll.202310813</p> <p>A detailed description of the dataset is given in the attached "Raw data assignment.xlsx"</p>
Dataset SUC1/S1-S4: Cyberattack scenarios on DER energy management and control
<p><span>This sandboxing use case focuses on advanced energy management and control applications for DERs, which are essential for optimizing their operation and achieving key objectives. These objectives include tracking the awarded power generation according to energy market clearing processes, avoiding intense power imbalances caused by intermittent weather-based RES, and increasing the profitability of DER owners. By leveraging real-time control strategies, the management system can dynamically adjust flexible DER operations to improve the overall response of aggregated DERs, based on both RES and Battery Storage Systems (BSS). Since this use case requires active control of an BSS and its operation can be severely affected in case of a cyber-attack, it is crucial to examine this scenario in a controlled and non-invasive environment enabled by a sandboxing testing environment (the KIOS CoE Cyber-physical sandbox) to avoid any disturbance to the actual power infrastructure.</span></p> <p><span>This dataset collection is related to the four comprehensive cyber attack scenarios which target the Modbus TCP communication ptotocol used for transmitting m<span>easurements from smart meters of RESs and DERs to DER controller, as well as s<span>et-points sent to flexible DER inverters from DER controller, aiming to </span></span>affect the proper operation of the DER energy managemnet and control function. The four scenarios are:</span></p> <p><span><span>SUC1/S1 - MITM with FDI cyber-attack on wind farm measurements<br></span></span><span>SUC1/S2 - MITM with FDI cyber-attack on BSS measurements</span></p> <p><span>SUC1/S3 - MITM with FDI cyber-attack on BSS set-points</span></p> <p><span>SUC1/S4 - MITM with DoS cyber-attack</span></p> <p><span><span>The dataset includes electrical measurements of the active power generation of the wind farm (attacked signal and actual state) and the BSS operation, as well as the set-point generated by the DER controller. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files. The measurements were recorded with a 5-second time resolution by the DER controller. <span> </span></span></span></p>
Smart Home Sensor and HVAC Control Dataset from the SHAL Demonsrtator of the Aegis Project
<p>The example dataset was produced within the Smart Home and Assisted Living demonstrator of the AEGIS project. IT contains measurements of indoor temperature and HVAC control actions (ON/OFF status and setpoint values), which were be used to extract comfort profiles.</p>
IMU data captured unobtrusively and in-the-wild by Parkinson's disease patients and healthy controls
<p><strong>DATASET</strong></p> <p>The dataset contains IMU signals captured in-the-wild via the accelerometer sensor embedded in modern smartphones, for the purpose of detecting tremorous episodes, related to Parkinson's Disease (PD). A group of 31 PD patients and 14 Healthy controls contributed accelerometer data using their personal smartphones, for a period spanning many months.Tri-axial acceleration values were recorded automatically whenevera phone call was realized. The recording lasted for 75 seconds at the most. Each phone call thus resulted in one recorded accelerometer signal, also referred to as session. Each subject contributed a different amount of sessions depending on the number of phone calls they realized during the data collection period as well as their participation time (they were free to drop-out at any time). A detailed description of the capturing process as well as analysis results, can be found in the related research article.</p> <p>The data is presented as a list of python dictionaries, stored in a pickle file. Each dictionary in the list, corresponds to one subject and containes the following fields:</p> <p>1. subject_id: scalar<br> A numerical value that uniquely identifies the subject.</p> <p>2. subject_sessions: list of numpy.array<br> A list of numpy arrays of shape (N, 4) that contains the tri-axial accelerometer sessions that the subject contributed. N denotes the total length of the session in samples (which varies from session to session) Column 0 of the array contains the timestamps of the accelerometer samples. Columns 1-3 contain the acceleration values across the x,y,z directions.</p> <p>3. session_datetimes: list of datetime objects <br> A list of datetime objects that denote the capturing date and time of the corresponding entries in the subject_sessions field.</p> <p>4. annotation: dict<br> A dictionary containing the following tremor-related annotation values:<br> * updrs16: scalar int<br> The value related to tremor as described in item 16 of the part II of the MDS-UPDRS scale, as reported by the subject.</p> <p>* updrs20_right: scalar int in range [0, 4]<br> The value related to rest tremor in the right hand as described in item 20 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist.</p> <p>* updrs20_left: scalar int in range [0, 4]<br> Same as above but for left hand.</p> <p>* updrs21_right: scalar int in range [0, 4]<br> The value related to action/postural tremor in the right hand as described in item 21 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist.</p> <p>* updrs21_left: scalar int in range [0, 4]<br> Same as above but for left hand.</p> <p>* sp_expert: scalar int in range [0, 1]<br> A binary tremor annotation created by a group of signal processing experts, upon visually examining the contributed signals in both time and frequency domain and taking into consideration the UDPRS scores of each subject. This was necessary due to the intermittent nature of tremor, as well as a number of considerations related to the in-the-wild nature of the data capturing process. For more details, we refer the reader to the dataset description in the related research article.<br> A '1' value indicates that the subject has tremor.<br> A '0' value indicates that the subject doesn't have tremor.</p> <p>* pd_status: scalar int in range [0, 1]<br> A '1' value indicates that the subject is a PD patient.<br> A '0' value indicates that the subject is a Healthy Control</p> <p>Note: Each annotation value refers to the subject as a whole, and not in any one session.<br> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected is a multi-center study approved in each country available (for more info visit: <a href="http://www.i-prognosis.eu/?page_id=3606">http://www.i-prognosis.eu/?page_id=3606</a>). Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu/">i-prognosis.eu</a>).</p> <p> </p> <p><strong>CORRESPONDANCE</strong></p> <p>Any inquiries regarding this dataset should be adressed to:</p> <p>Mr. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate)</p> <p>Multimedia Understanding Groupmug<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996359, 996365 <br> Fax: +30 2310 996398<br> E-mail: alpapado@mug.ee.auth.gr</p> <p> </p> <p><strong>LICENSE</strong></p> <p>This is an open access dataset, licensed under Creative Commons Attribution 4.0 International (<a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>).</p> <p> </p> <p><strong>WARRANTY</strong></p> <p>This dataset comes without any warranty. Administrators of this dataset can not be held accountable for any damage (physical, financial or otherwise) caused by the use of this dataset. </p>
Artifact for "Does Task Complexity Moderate the Benefits of Liveness? - A Controlled Experiment"
<p>This artifact includes the setup, data, and analysis for the experiment described in the article "Does Task Complexity Moderate the Benefits of Liveness? - A Controlled Experiment" published in the journal "The Art, Science, and Engineering of Programming<em>" </em>Volume 9.</p> <p>The artifact has the following structure:</p> <ul> <li> <p>experiment-setup</p> <ul> <li>experiment-environment.zip: Archive including the fully configured Squeak/Smalltalk environment</li> <li>experiment-protocol.pdf: Full protocol for conducting the experiment</li> <li>system: Includes the base system used for the experiment without any seeded faults</li> <li>tasks <ul> <li>task-descriptions.txt: The task descriptions of steps to reproduce and symptoms for the tasks used in the experiment</li> <li>patches: Patch files for all generated tasks</li> </ul> </li> <li>infrastructure: Contains source code of the tools used for controlling tasks in the development environment</li> <li>questionnaire <ul> <li>survey.dfglive.2023-01-06.xml: Questionnaire configuration for a SoSci survey server, includes demographics, experience, and skill questionnaire</li> </ul> </li> </ul> </li> <li> <p>results</p> <ul> <li>data <ul> <li>export-NN.zip: Each archive contains the raw data from one run <ul> <li>starTrackData-NN: Contains the detailed event log</li> <li>experimentState.json: Contains the measurements and the final state for each task (completed, notStarted, etc.)</li> <li>skillTestResult-NN.json: Contains the score reached in the skill test</li> <li>task-X-NN.cs: The submitted patch for task X</li> </ul> </li> </ul> </li> <li>questionnaire <ul> <li>data_dfglive-analysis.ods: Demographics and experience questionnaire results (gender column is redacted due to potential deanonymization)</li> <li>results of skill test are only available in graded form in the participant export files</li> </ul> </li> </ul> </li> <li> <p>analysis</p> <ul> <li>analysis-r: Main analysis scripts <ul> <li>main.R, main-contrast-based.R: Two versions of the main and moderation effect analysis using two different processes</li> <li>live-tools-usage-time.R: Main and moderation effect analysis on tool usage</li> <li>demographics.R: Analysis of demographics and rendering of charts</li> </ul> </li> <li>analysis-tool-usage-correlation: Includes scripts to extract tool usage frequencies from the event logs. Assumes that export-NN.zip files are in the parent folder.</li> </ul> </li> </ul>
Graphics for implanted brain-computer interfaces for communication and sensorimotor control applications.
<p>Updated information from Nature Reviews Bioengineering, doi: 10.1038/s44222-024-00239-5. Current as of 27 September 2024. Please reference the original publication if using these graphics. As the field moves into the "Translational Era", the original publication reviews the clinical trials up to December 2023. </p>
SERENA EJPSoil Soil erosion control in Tuscany (Italy)
<div> <p><span><span>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</span></span></p> <p><span><span>One of the </span><span>objective</span><span> of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. The present data was prepared according to the </span><span>methodology</span><span> of the SERENA Soil erosion and soil erosion control cookbook</span><span>. </span><span>Soil loss was used as an indicator for soil erosion (ST). T</span><span>he map of soil mass not eroded was based on the RUSLE model. </span><span>Soil erosion control was calculated as the difference between potential and actual soil erosion (SES, ecosystem service of soil erosion protection, i.e. soil eroded mass </span><span>retained</span><span> by vegetation, Mg/ha/y)</span><span>. For Italy, the cookbook was applied in the Tuscany region. </span></span><span> </span></p> </div> <div> <p><span><span>To create the soil loss map we used:</span></span><span> </span></p> </div> <div> <ul> <li><span><span>for R-factor, not freely available database of meteorological parameters spatialized at 250 m (minimum and maximum daily air temperature; cumulate daily precipitation) over Tuscany region (period 1990–2022, Lamma </span><span>Consortium) and</span><span> a local linear equation between R and mean annual precipitation (P);</span></span> </li> <li><span>for C -factor, Regional Land use map 1:10.000 (2018, freely available at: </span><span>https://www502.regione.toscana.it/geoscopio/usocoperturasuolo.html</span><span>) and ESDAC method (</span><span>https://doi.org/10.1016/j.landusepol.2015.05.021</span><span>) ;</span> <span> </span></li> <li><span>for K-factor, sand, silt, clay, and O.C. (%) maps (built from 4.000 soil profiles, following FAO’s </span><span>methodology</span><span> in GSP-GSOC map, Lamma Consortium), and Torri et al. (1997) function;</span><span> </span></li> <li><span>for LS-factor, DEM 10 m of Tuscany, (freely available at </span><span>https://www502.regione.toscana.it/geoscopio/cartoteca.html99</span><span>) and Desmet & </span><span>Govers</span><span> (1996) SAGA tool (applied at 10 m and upscaled);</span><span> </span></li> <li><span>for P-factor, not freely available database 1:10.00 of terraced areas (Lamma Consortium, 2020) (for terraced areas a multiplication factor </span><span>of 0.5</span><span> was considered, based on expert evaluation)</span><span> </span></li> <li><span>for P-factor, not freely available database 1:10.00 of terraced areas (Lamma Consortium, 2020) (for terraced areas a multiplication factor </span><span>of 0.5</span><span> was considered, based on expert evaluation)</span><span> </span></li> </ul> </div> <div> <p><span><span>Maps was delivered in the </span><span>GeoTIFF</span><span> format in the resolution of 100m. </span></span><span> </span></p> </div> <div> <p><span><span>Delivered data will be </span><span>validated</span><span> by stakeholders from Italy (scientist) in </span><span>October,</span><span> 2024.</span></span><span> </span></p> </div>
Data associated with following publication: "In situ optical sub-wavelength thickness control of porous anodic aluminum oxide"
<p>Data associated with following publication: "In situ optical sub-wavelength thickness control of porous anodic aluminum oxide" (DOI: <a href="https://doi.org/10.3762/bjnano.15.12" target="_blank" rel="noopener">https://doi.org/10.3762/bjnano.15.12</a>)</p>
Dataset: Co-composting rose waste as a sustainable waste management strategy: Nutrient availability and disease control
<p>This dataset and these scripts supports the article 'Assessing the potential of co-composting rose waste as a sustainable waste management strategy: Nutrient availability and disease control' as published in Journal of Cleaner production. https://doi.org/10.1016/j.jclepro.2023.136685</p> <p>Roses are an important crop for the floricultural sector of Kenya and roses are a perennial crop and under continuous production for six to ten years. The cultivation produces large quantities of green waste, up to 50 kg per hectare per day. In this experiment we investigated the potential of co-composting rose waste on a small scale. The objective was to increase the understanding of composting lignocellulosic rose waste, which will help with the implementation of composting practices within Kenyan rose production and thereby reduce its negative ecological impact.</p> <p>In a small-scale composting system (30L) the evolution of five mixtures was closely monitored in terms of their physico-chemical parameters. Furthermore, the in-vitro disease suppressive capacity of mature rose waste was assessed. </p>
New datasets obtained from experimental installations with centralized control
<p>The dataset contains the data that local controller 1 (LC1) received from local controller 2 (LC2) during normal system operation. The system was created and it is located in the Laboratory for Manufacturing Automation at the Faculty of Mechanical Engineering, University of Belgrade. The system is based on a smart sensor (electromagnetic linear encoder with local controller) and a smart actuator (rodless pneumatic cylinder with electro-pneumatic pressure regulator and local controller), and the main goal is to achieve the desired position of the piston on the pneumatic cylinder. The dataset includes 7 signals that represent a combination of different piston trajectories. Each signal was recorded during the 200 minutes of piston movement along a defined trajectory, where the length of each signal is 400,000 samples. Table 2 shows the list of collected signals, whereas a detailed description of the system can be found in [1].</p> <p>This dataset was developed with the support of the Science Fund of the Republic of Serbia, Grant No. 6523109, AI - MISSION4.0, 2020-2022.</p>
Database of infection control and surveillance program, 2011-2020
<p>A full anonymized data set was collected as a part of the ICU infection control and surveillance program; 01/01/2011-12/31/2020</p> <p>File "Zenodo_DB_v4<a href="https://zenodo.org/api/files/6d089d03-7a43-4513-b476-92f438837941/VAE_Data_Main_0821_1338.csv">.csv</a>" contains daily data (one row is one day) on infection surveillance ordered by date.</p> <p>File "<a href="https://zenodo.org/api/files/6d089d03-7a43-4513-b476-92f438837941/Data_Dictionary_MainDB.csv">Data_Dictionary_MainDB_2021.csv</a>" contains the description of all variables from the data set.</p> <p> </p>
ValRun: GMP-grade Manufacturing and Quality Control of a Non-Virally engineered Advanced Therapy Medicinal Product for Personalized Treatment of Age-Related Macular Degeneration
<p><strong>VaLRun: </strong></p> <p><strong>Raw data of "GMP-grade Manufacturing and Quality Control of a Non-Virally engineered Advanced Therapy Medicinal Product for Personalized Treatment of Age-Related Macular Degeneration"</strong></p> <p>(Excel-, pdf-, GraphPad-files, mp4 videos and a READ-ME text file)</p> <p>The introduction of new therapeutics requires validation of Good Manufacturing Practice (GMP)-grade manufacturing including suitable quality controls. This is challenging for Advanced Therapy Medicinal Products (ATMP) with personalized batches. We have developed a person-alized, cell-based gene therapy to treat age-related macular degeneration and established a vali-dation strategy of the GMP-grade manufacture for the ATMP; manufacturing and quality control were challenging due to a low cell number, batch-to-batch variability and short production duration. Instead of patient iris pigment epithelial cells, human donor tissue was used to produce the transfected cell product (“tIPE”). We implemented an extended validation of 104 tIPE productions. Procedure, operators and devices have been validated and qualified by determining cell number, viability, extracellular DNA, sterility, duration, temperature and volume. Transfected autologous cells were transplanted to rabbits verifying feasibility of the treatment. A container has been engineered to insure a safe transport from the production to the surgery site. Criteria for successful validation and qualification were based on tIPE’s Critical Quality Attributes and Process Parameters, its manufacture and release criteria. The validated process and qualified operators are essential to bring the ATMP into clinic and offer a general strategy for the transfer to other manufacture centers and personalized ATMPs.</p>
Outputs from Control REACT Workstream 2 [Data]
<p>Outputs from the Network Innovation Allowance project "Control REACT" (workstream 2), sponsored by National Grid Electricity System Operator (NGESO). This deposit contains underlying data used in this project. The R code (Rmarkdown) and html renders of these workbooks are available in a separate deposit linked below. See description there for further details. In order to run the R scripts, data and code must be arranged in the directory structure given in "Directory Structure.pdf".</p> <p>Wind, solar and net-demand data are derived from raw data made available by <a href="https://www.bmreports.com/">Elexon</a> and <a href="https://www.solar.sheffield.ac.uk/pvlive/">Solar Sheffield</a> via public APIs. See respective websites for details, our processed (aggregated and cleaned) versions of this data are shared here under a CC-BY license. Weather forecast data are derived from historic operational forecasts from the <a href="https://www.ecmwf.int/">ECMWF</a> HRES model and are shared under a CC-BY licence. For details on how these were processed please see references.</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.