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1,774 results for “Acceleration”
Dataset of ""Statistical atlases and automatic labelling strategies to accelerate the analysis of social insect brain evolution"
<p>Dataset of <em>Statistical atlases and automatic labelling strategies to accelerate the analysis of social insect brain evolution</em> by Sara Arganda, Ignacio Arganda-Carreras, Darcy G. Gordon, Andrew P. Hoadley, Alfonso Pérez-Escudero, Martin Giurfa and James F. A. Traniello.</p> <p>In this dataset, we are presenting:</p> <ul> <li>10 confocal brain images from <em>Pheidole spadonia </em>minors (in the original confocal TIFF format and in the open NRRD format), with manually segmented labels of 8 subregions (Optic Lobes, OL; Antennal Lobes, AL; Mushroom Body Medial Calyx, MB-MC; Mushroom Body Lateral Calyx, MB-LC; Mushroom Body Peduncle, MB-P; Central Complex, CX; Subesophageal zone, SEZ; and Rest of Central Brain, ROCB – in NRRD format) from one expert annotator.</li> <li>12 confocal brain images from <em>P. spadonia</em>, <em>P. rhea</em>, <em>P. tepicana</em> and <em>P. obtusospinosa</em> minors, with manually segmented labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB) from one expert annotator.</li> <li>5 confocal brain images from <em>Pheidole spadonia </em>minors (“test brains”), with five sets of manually segmented labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB) from three expert annotators (one set from annotator 1, one set from annotator 2 and three sets from annotator 3, to evaluate inter and intra person differences).</li> <li>1 group-wise template generated from the 10 confocal brain images from <em>Pheidole spadonia </em>minors, with three sets of manually segmented labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB).</li> <li>5 group-wise templates generated from the 9 confocal brain images from <em>Pheidole spadonia </em>minors, with consensus labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB).</li> <li>1 group-wise template generated from 12 confocal brain images from <em>P. spadonia</em>, <em>P. rhea</em>, <em>P. tepicana</em> and <em>P. obtusospinosa</em> minors, with consensus labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB).</li> <li>7 sets of automatic labels for the 5 “test brains”: 3 sets of “Direct Labels”, 3 sets of “Consensus Labels”, 1 set of “Multispecies Template Labels”.</li> </ul> <p>Brain of minor workers were dissected from the ant head capsule in ice cold HEPES-buffered saline and were fixed and immunohistochemically stained using SYNORF1 (a monoclonal <em>Drosophila</em> synapsin I antibody obtained from the Developmental Studies Hybridoma Bank, catalog 3C11) and secondarily stained using Alexa Fluor 488 for visualization of neuropil (slightly modified from Ott, 2008). Later, brains were mounted in methyl salicylate and imaged on an Olympus Fluoview BX50 laser scanning confocal microscope with a ×20 objective at a resolution of ~0.7 × 0.7 × 5µm/voxel. All brain tissue manipulation, staining and recording was performed by Darcy G. Gordon. Brain images were obtained in TIFF format by the confocal microscope and then opened and saved as Amira Mesh (.am) stack images in Amira (version 6.0). Manual segmentation of each brain was done using Amira (version 6.0 or 2019.2). Labels were traced on eight compartments in only one brain hemisphere, except for the CX, SEZ and ROCB, which lack a clear subdivision between hemispheres. Brain grey image stacks and labels were transformed to NRRD format for template construction using the Fiji plugin SaveAsGzipNrrd<a href="#_ftn1">[1]</a>. Volume and volume similarity of labels were calculated using the Fiji toolbox MorphoLibJ<a href="#_ftn2">[2]</a>.</p> <p><strong>Acknowledgements: </strong>We thank Ming Huang (from Dr. Diana Wheeler’s laboratory) who kindly provided access to colonies from four species of the hyperdiverse ant genus <em>Pheidole</em> (<em>P. spadonia</em>, <em>P. rhea</em>, <em>P. tepicana </em>and <em>P. obtusospinosa</em>). This research was supported by National Science Foundation grants IOS 1354291 and IOS 1953393 to JT, a Marie Skłodowska-Curie Individual Fellowship BrainiAnts-660976 and Ayudas destinadas a la atracción de talento investigador a la Comunidad de Madrid en centros de I+D. This work is supported in part by the University of the <a href="https://www.sciencedirect.com/topics/engineering/basque-country">Basque Country</a> UPV/EHU grant GIU19/027.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> https://github.com/iarganda/tefor</p> <p><a href="#_ftnref2">[2]</a> https://imagej.net/plugins/morpholibj</p>
Supplementary data to: Acceleration of diverging runoff trends on the Third Pole
<p>This archive contains data produced for the study of “Acceleration of diverging runoff trends on the Third Pole (TP)”. For now, the data is password protected and only available to the Editor and Reviewers. And we commit to making the data publicly in the event of publication.</p> <p> </p> <p>The archive is organized in two major directories (“Shape_file” and “Data”).</p> <p> </p> <p><strong>1. The directory “Shape_file” </strong>includes two specific folders.</p> <p>(1) The folder “Mountain-basins_for_TP_rivers”, provides the shape files of the mountain-outlet basins for each of the 12 major TP rivers (Mekong, Salween, Brahmaputra, Ganges, Indus, Amu Darya, Syr Darya, Yellow, Yangtze, Tarim, Heihe, and Shule).</p> <p>(2) The folder “Three_climate_domains”, provides the shape files for the mountain-outlet basins (that we investigated in the study) in the TP's westerlies domain, westerlies-monsoon transition domain, and Indian monsoon domain, respectively.</p> <p> </p> <p><strong>2. In the directory “Data”</strong>, we provide the processed annual time-series of precipitation (<em>P</em>) anomaly, evapotranspiration (ET) anomaly, total water storage change (<em>delt_S</em>) anomaly, and runoff from 1960 to 2016 that covers the mountain basins of all the 12 major TP rivers.</p> <p>For more details, please refer to the document "Data_introduction.docx" in the rar file ("Data-202202.rar").</p>
Late Eocene Subduction Initiation of the Indian Ocean in the North Sulawesi Arc, Indonesia, Induced by Abrupt Australian Plate Acceleration
<p>Supplementary Tables (S1-S3) for a manuscript submitted to Lithos entitled "Late Eocene Subduction Initiation of the Indian Ocean in the North Sulawesi Arc, Indonesia, Induced by Abrupt Australian Plate Acceleration".</p>
High-resolution GPU-accelerated Superstrings Simulation
<p>Small clip from a 4096^3 matter era simulation of U(1)xU(1) cosmic strings, a simple proxy for cosmic superstrings. Cells pierced by p-strings are colour coded in blue, those pierced by q-strings in red and pq-strings in green.</p> <p>This work was financed by FEDER---Fundo Europeu de Desenvolvimento Regional funds through the COMPETE 2020-POCI, and by Portuguese funds through FCT in the framework of the project POCI-01-0145-FEDER-028987 and PTDC/FIS-AST/28987/2017. J.R.C. is supported by an FCT fellowship (SFRH/BD/130445/2017). We acknowledge PRACE for awarding us access to Piz Daint at CSCS, Switzerland, through Preparatory Access proposal 2010PA4610, Project Access proposal 2019204986 and Project Access proposal 2020225448. Technical support from Jean Favre at CSCS is gratefully acknowledged.</p>
Data Archive for "Acceleration as a proxy for energy expenditure in a facultative-soaring bird: comparing dynamic body acceleration and time-energy budgets to heart rate"
<p>Heart rate, acceleration, and respirometry data from four wild-caught gulls during climate chamber and treadmill calibration measurements (2018), as well as heart rate and acceleration data from five free-ranging gulls from a colony on Texel, NL during the breeding season (May - July, 2019). </p> <p> </p>
Resilient consumers accelerate the plant decomposition in a naturally acidified seagrass ecosystem
<p>Anthropogenic stressors are predicted to alter biodiversity and ecosystem functioning worldwide. However, scaling up from species to ecosystem responses poses a challenge, as species and functional groups can exhibit different capacities to adapt, acclimate, and compensate under changing environments. We used a naturally acidified seagrass ecosystem (the endemic <em>Mediterranean Posidonia oceanica</em>) as a model system to examine how ocean acidification (OA) modifies the community structure and functioning of plant detritivores, which play vital roles in the coastal nutrient cycling and food web dynamics. In seagrass beds associated with volcanic CO2 vents (Ischia, Italy), we quantified the effects of OA on seagrass decomposition by deploying litterbags in three distinct pH zones (i.e., ambient, low, extreme low pH), which differed in the mean and variability of seawater pH. We replicated the study in two discrete vents for 117 days (litterbags sampled on day 5, 10, 28, 55, and 117). Acidification reduced seagrass detritivore richness and diversity through the loss of less abundant, pH-sensitive species but increased the abundance of the dominant detritivore (amphipod <em>Gammarella fucicola</em>). Such compensatory shifts in species abundance caused more than a three-fold increase in the total detritivore abundance in lower pH zones. These community changes were associated with increased consumption (52-112%) and decay of seagrass detritus (up to 67% faster decomposition rate for the slow-decaying, refractory detrital pool) under acidification. Seagrass detritus deployed in acidified zones showed increased N content and decreased C:N ratio, indicating that altered microbial activities under OA may have affected the decay process. The findings suggest that OA could restructure consumer assemblages and modify plant decomposition in blue carbon ecosystems, which may have important implications for carbon sequestration, nutrient recycling, and trophic transfer. Our study highlights the importance of within-community response variability and compensatory processes in modulating ecosystem functions under extreme global change scenarios.</p>
High-level nitrogen additions accelerate soil respiration reduction over time in a boreal forest
<p>Increased nitrogen (N) inputs are widely recognized to reduce soil respiration (Rs), but how N deposition affects the temporal dynamics of Rs remains unclear. Using a decade-long fertilization experiment in a boreal larch forest (Larix gmelini) in northeast China, we found that the effects of N additions on Rs showed a temporal shift from a positive effect in the short-term (increased by 8% on average in the first year) to a negative effect over the longer term (decreased by 21% on average in the eleventh year). The rates of decrease in Rs for the higher N-levels were almost twice as high as those of the low N-level. Our results suggest that the reduction in Rs in response to increased N input is accelerated by high-level N additions, and experimental high N applications are likely to overestimate the contribution of N deposition to soil carbon sequestration in boreal forest.</p>
Virtual Axle Detector based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network
<p>We recorded the measurement data used in the present study on a single-span steel trough railway bridge located on a long-distance traffic line in Germany. The bridge is 18.4 m long in total with a free span of 16.4 m. A total of 10 seismic uniaxial accelerometers of the type PCB-39B04 (PCB Synotech) with a sensitivity of 1000 mV/g (±10\%), a broadband resolution of 0.000003 gRMS, a measurement range of ±5 gpk and a frequency range of 0.06 to 450 Hz (±5\%) were installed. The measurements are triggered via the rising slope of the wheel load measuring point G1, the measurements from the ring buffer are stored from ten seconds before the trigger together with the 50 seconds long measurement after the triggering. The recorded signals thus all have a length of 60 seconds. All sensor signals were recorded with a sampling frequency of fs = 600 Hz using the catmanAP software and the CX22 data recorder connected to an MX1601B universal amplifier and an MX1616B strain gauge amplifier (all products are from HBK). </p>
Supporting Information for "Data analysis of the unsteadily accelerating GPS and seismic records at Campi Flegrei caldera from 2000 to 2021". Data Set S1. Extended dataset of all the analyses
<p>This compressed folder contains supporting information related to the Figures in the manuscript: "Data analysis of the unsteadily accelerating GPS and seismic records at Campi Flegrei caldera from 2000 to 2021".</p> <p>Files and folders labeled with G1…n are related to the GPS data, those labeled with H1…n are related to the seismic data.</p> <p>In particular: <br> Subfolder 1_DATA supports Figure 3 – the vertical and the horizontal moduli of ground displacement at all analyzed GPS stations; the logarithmic plots of all seismic events and of their energy. It also shows the complete plot leveling data from 1905 to 2010 (modified from del Gaudio et al., 2010). It also includes Figure 2 and Figure 6a-c.</p> <p>Subfolder 2_AnnualRate supports Figure 4 - the annual rate of the vertical and horizontal moduli of ground displacement at all analyzed GPS stations; the annual rate of all seismic events and of their energy. These detail the 2-year, the 6-month, and the 30-day average results. It also supports Figure 5 with similar data concerning 2018-2020.</p> <p>Subfolder 3_InverseRate supports Figure S3 - the inverse rate of the vertical and the horizontal moduli of ground displacement at all analyzed GPS stations; the inverse rate of all seismic events and of their energy. These detail the 2-year, 6-month, and 30-day average results, including detailed plots of 2018-2020.</p> <p>Subfolder 4_RateChange supports Figure S2 - the daily rate change of the vertical and horizontal moduli of ground displacement at all analyzed GPS stations; the daily rate change of all seismic events and of their energy. These detail the 2-year, the 6-month, and the 30-day average results, including detailed plots of 2018-2020.</p> <p>Subfolder 5_FourierCoef supports Figure 6 - the Fourier spectrum of the vertical and the horizontal moduli of ground displacement at all analyzed GPS stations. These detail the 2-year, 6-month, and 30-day average results obtained in 2000-2020, 2011-2020, 2018-2020. Also, additional plots that detail other combinations of time domain and part of the Fourier spectrum, thus testing the sensitivity of the main harmonics on the time domain selected.</p> <p>Subfolder 6_ FFM_WaitTime supports Figure 11 – waiting time examples based on vertical and horizontal moduli of ground displacement at all analyzed GPS stations; all seismic events, and their energy. These detail the 2-year, 6-month, and 30-day average rate results, and the 10-year, 5-year and 3-year regressions.</p> <p>Subfolder 7_FFM_FailTime also supports Figure 11 – all the results expressed in terms of the failure time t<sub>f</sub> instead of in terms of the waiting time [t<sub>f</sub>(t) - t].</p> <p>Subfolder 8_pFFM_Regression supports Figure 9 - the pFFM examples based on the vertical and the horizontal moduli of ground displacement at all analyzed GPS stations; all seismic events and of their energy. These detail the 2-year, 6-month, and 30-day average rate results, and the 10-year, 5-year and 3-year regression.</p> <p>Subfolder 9_pFFM_Probability supports Figure S4 - pFFM examples based on vertical and horizontal moduli of ground displacement at all analyzed GPS stations; all seismic events, and their energy. These detail the 2-year, 6-month, and 30-day average rates, and the 10-year, 5-year and 3-year regressions.</p> <p>Subfolder 10_BarplotProb supports Figure S5 - results expressed in terms of the mean failure time probability at 2, 5, 10, and 25 years.It also supports Figure S6 - examples based on 6-month, and 30-day average rate results.</p> <p>Subfolder 11_BarplotWaitTime supports Figure S6 - all the results expressed in terms of the waiting time (t<sub>f</sub> – t) barplot. It also includes Figure S5.</p>
Overexpression of VEGF in dermal fibroblast cells accelerates the angiogenesis and wound healing function: in vitro and in vivo studies
<p>Human dermal fibroblasts (Hu02) were transfected by pcDNA3.1(-)-VEGF vector. Following selecting fibroblast cells with hygromycin, recombinant cells were investigated in terms of VEGF expression by quantifying method. We used Real-Time PCR assay to quantitate gene expression of vascular endothelial growth factor from manipulated cells and represented VEGF overexpression.</p> <p>Reverse transcription was performed from 1 μg of total RNA transcribed to complementary DNA (cDNA) through 1 μL of random hexamer primer. The reactions were incubated at 70°C for 5 minutes. After that, 5X RT-buffer, dNTP, and RT-enzyme were added, and the mixtures were incubated at 42°C for 60 minutes and 70°C for 10 minutes. Reverse transcription was performed from 500 ng total RNA using the RT2 First Strand Kit (SA Biosciences). Quantitative real-time PCR was performed (Ampliqon, Denmark) with 40 cycles at 95 oC for 15 seconds and 60 oC for 60 seconds.</p> <p>The normalization and all the data analysis were performed according to RESR and Graph pad-Prism 8 software.<br> For the normalization, it uses the housekeeping gene: β-Actin.<br> Target gene signals normalized to housekeeping genes; 2^-deltaCt, where deltaCt = (Ct_Target − Ct_HKG)].<br> </p>
Black-tailed Gull GPS foraging trip data and acceleration raw data
Areas at which seabirds forage intensively can be discriminated by tracking the individuals' at-sea movements. However, such tracking data may not accurately reflect the birds' exact foraging locations. In addition to tracking data, gathering information on the dynamic body acceleration of individual birds may refine inferences on their foraging activity. Our aim was to classify the foraging behaviors of surface-feeding seabirds using data on their body acceleration and use this signal to discriminate areas where they forage intensively. Accordingly, we recorded the foraging movements and body acceleration data from seven and ten black-tailed gulls (Larus crassirostris) in 2017 and 2018, respectively, using GPS loggers and accelerometers. By referring to video footage of flying and foraging individuals, we were able to classify flying (flapping flight, gliding, and hovering), foraging (surface plunging, hop plunging, and swimming), and maintenance (drifting, preening, etc.) behaviors using the speed, body angle, and cycle and amplitude of body acceleration of the birds. Foraging areas determined from acceleration data corresponded roughly with sections of low speed and area-restricted searching (ARS) identified from the GPS tracks. However, this study suggests that the occurrence of foraging behaviors may be overestimated based on low-speed trip sections, because birds may exhibit long periods of reduced movement devoted to maintenance. Opposite, the ARS-based approach may underestimate foraging behaviors since birds can forage without conducting an ARS. Therefore, our results show that the combined use of accelerometers and GPS tracking helps to adequately determine the important foraging areas of black-tailed gulls. Our approach may contribute to better discriminate ecologically or biologically significant areas in marine environments.
Data from: Non-Adiabatic Acceleration of Injected Electrons in the Inner Magnetosphere: Joint Observations by the Van Allen Probe and the BeiDa Imaging Electron Spectrometer
<p>The dataset is associated with the study entitled "Non-adiabatic acceleration of injected electrons in the inner magnetosphere: Joint observations by the Van Allen Probe and the BeiDa Imaging Electron Spectrometer"</p> <p>The file includes directional electron flux data obtained by the BeiDa Imaging Electron Spectrometer and follows NASA's Common Data Format (CDF).</p>
Supplementary Figures 1 and 2, research article "Can Moving in a Redundant Workspace Accelerate Motor Adaptation?"
<p><strong>Supplementary Figures 1 and 2. Movement endpoints during the priming phase (figure 1) and test phase (figure 2), shown separately for each target location (colors) and target shape (background shading). </strong>Each panel shows movement endpoints for a different subjects (the order of subjects is identical in figure 1 and 2). The home position is at (0,0). Movements to different target locations are indicated by different colors (purple = left target, green = center targer, red = right target). Thick outlines around movement endpoints indicate outliers excluded from all analyses.</p>
Time-history datasets of input accelerations and experimental structural responses
<p>This repository contains the data of input accelerations and measured structural responses for ten different cyclic tests supported by JSPS KAKENHI (No. JP19H02286) and conducted by Makoto Ohsaki and others.</p>
Supporting data and code for Nakov, Beaulieu, Alverson: Accelerated diversification is related to life history and locomotion in a hyperdiverse lineage of microbial eukaryotes (Diatoms, Bacillariophyta)
<p>This archive includes:</p> <p>1. Character and species richness datasets</p> <p>2. Phylogenies and time-calibration files</p> <p>3. R scripts</p>
Additonal material for the dissertation "An Accelerated Solution Method for Two-Stage Stochastic Models in Disaster Management": Data, MATLAB codes and results
<p>File "DataImport" contains a "ReadMe" file, raw data for all case studies in Excel and the MATLAB code "ImportData.m" importing Excel data into MATLAB</p> <p>File "LShaped" contains a "ReadMe" file, all data in the form of matrices and the MATLAB code "LShaped_MultiCut.m" solving all case studies via the standard or accelerated L-shaped method using a multi-cut approach</p> <p>File "Results" contains a "ReadMe" file, results of all case studies and computation time required by Gurobi, der standard L-shaped method and accelerated L-shaped method</p>
Supplementary materials: "Synthesizing Particle-in-Cell Simulations Through Learning and GPU Computing for Hybrid Particle Accelerator Beamlines"
<p>Data archive for stage 3 of manuscript for PASC24. See Readme stage 3.txt for more details.</p> <p> </p> <p>This work was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231 and by LLNL under Contract DE-AC52-07NA27344. This material is based upon work supported by the U.S. Department of Energy, OFfice of Science, Office of High Energy Physics, General Accelerator R&D (GARD), under contract number DE-AC02-05CH11231. This material is based upon work supported by the CAMPA collaboration, a project of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program. This research was supported by the Exascale Computing Project (17-SC-20-SC), a joint project of the U.S. Department of Energy's Office of Science and National Nuclear Security Administration, responsible for delivering a capable exascale ecosystem, including software, applications, and hardware technology, to support the nation's exascale computing imperative.This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award HEP-ERCAP0023719.</p>
Reinforcing Tunnel Network Exploration in Proteins using Gaussian Accelerated Molecular Dynamics (parameters, trajectories)
<ul> <li>00_LinB-Wt.tar.gz - LinB-Wt simulation files:</li> </ul> <p> 1.cMD(Classical MD simulation):<br> <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.<br> <br> <br> 2.GaMD(Gaussian Accelerated MD simulation): <br> <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.</p> <ul> <li>01_LinB-Open.tar.gz - LinB Open mutant simulation files:</li> </ul> <p> 1.cMD(Classical MD simulation):<br> <br> 1. Stripped parameter file *.parm7. <br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.<br> <br> <br> 2.GaMD(Gaussian Accelerated MD simulation): <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.</p> <ul> <li>02_LinB-Closed.tar.gz - LinB Closed mutant simulation files:</li> </ul> <p> 1.cMD(Classical MD simulation):<br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.<br> <br> <br> 2.GaMD(Gaussian Accelerated MD simulation): <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.</p>
Dataset for AGILE Platform: A Deep Learning-Powered Approach to Accelerate LNP Development for mRNA Delivery
<p>This document contains the lipid datasets used for fine-tuning and candidate selection in the AGILE study.</p>
Large Language Models for Human-Machine Collaborative Particle Accelerator Tuning through Natural Language
<p>Autonomous tuning of particle accelerators is an active and challenging field of research with the goal of enabling novel accelerator technologies cutting-edge high-impact applications, such as physics discovery, cancer research and material sciences. A key challenge with autonomous accelerator tuning remains that the most capable algorithms require an expert in optimisation, machine learning or a similar field to implement the algorithm for every new tuning task. In this work, we propose the use of large language models (LLMs) to tune particle accelerators. We demonstrate on a proof-of-principle example the ability of LLMs to successfully and autonomously tune a particle accelerator subsystem based on nothing more than a natural language prompt from the operator, and compare the performance of our LLM-based solution to state-of-the-art optimisation algorithms, such as Bayesian optimisation (BO) and reinforcement learning-trained optimisation (RLO). In doing so, we also show how LLMs can perform numerical optimisation of a highly non-linear real-world objective function. Ultimately, this work represents yet another complex task that LLMs are capable of solving and promises to help accelerate the deployment of autonomous tuning algorithms to the day-to-day operations of particle accelerators.</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.