Find research datasets worth reusing

Data for: Observing dynamical phases of BCS superconductors in a cavity QED simulator

<p>In conventional Bardeen-Cooper-Schrieffer (BCS) superconductors, electrons with opposite momenta bind into Cooper pairs due to an attractive interaction mediated by phonons in the material. While superconductivity naturally emerges at thermal equilibrium, it can also emerge out of equilibrium when the system's parameters are abruptly changed. The resulting out-of-equilibrium phases are predicted to occur in real materials and ultracold fermionic atoms but have not yet all been directly observed. Here we realise an alternate way to generate the proposed dynamical phases using cavity quantum electrodynamics (cavity QED). Our system encodes the presence or absence of a Cooper pair in a long-lived electronic transition in <sup>88</sup>Sr atoms coupled to an optical cavity and represents interactions between electrons as photon-mediated interactions through the cavity. To fully explore the phase diagram, we manipulate the ratio between the single-particle dispersion and the interactions after a quench and perform real-time tracking of subsequent dynamics of the superconducting order parameter using non-destructive measurements. We observe regimes where the order parameter decays to zero (phase I), assumes a non-equilibrium steady-state value (phase II), or exhibits persistent oscillations (phase III). This opens up exciting prospects for quantum simulation, including the potential to engineer unconventional superconductors and to probe beyond mean-field effects like the spectral form factor, and for increasing coherence time for quantum sensing.</p>

Dynamically coupled kinetic chemistry in brown dwarf atmospheres - II. Cloud and chemistry connections in directly imaged sub-Jupiter exoplanets

<p>Gifs of GCM output from the paper, model is Teff = 1000 K, log g = 3, M/H = 1. </p><p>The atmos_daily_2980.nc file contains the GCM NETCDF output at 2080 days.</p>

Data from: Disparate home range dynamics reflect nutritional inadequacies on summer range for a large herbivore

<p>The spatial distribution of animals has consequences for nutrition, predator-prey dynamics, spread of diseases, and population dynamics in general. Animals must establish a home range to secure adequate resources to fuel their energetic needs. Home ranges, therefore, are temporally and spatially dynamic given the changing requirements of an animal and the availability of resources on the landscape. We used data from two populations of bighorn sheep with contrasting population dynamics following pneumonia epizootics and different habitat quality on their summer range to test the hypothesis that the distribution and size of home ranges are influenced by environmental conditions and reproductive status. We used a combination of data from 768 vegetation transects and remotely sensed metrics to index forage quality of consecutive biweekly home ranges for 27 bighorn sheep, June–August 2019–2021. There were population differences in space use that were consistent with resource limitations in the population declining in abundance. Animals in both populations increased the area of their space use through the summer in association with declining forage quality indexed by plant phenology. Furthermore, animals in the Whiskey Mountain population without live offspring used areas more than twice the size of animals with offspring, whereas there were no differences in the area of space use between animals with and without offspring in Jackson. We demonstrated that limitations young offspring impose on space use of a mother may have consequences for animals living where larger home ranges are needed to secure adequate resources—sheep in Whiskey Mountain had to travel 1,000 m from escape terrain to access the same amount of biomass that the Jackson sheep could access directly adjacent to escape terrain. Forage quality and availability influence movement and home range dynamics. In the presence of disease, movement and home range dynamics may influence pathogen transmission and persistence. Thus, forage availability may play an indirect role in population dynamics in the presence of disease, which is another line of evidence for how environmental and nutritional conditions may influence population dynamics of populations coping with disease.</p>

Phospho-seq: Integrated, multi-modal profiling of intracellular protein dynamics in single cells

<p> </p><p>Datasets to go along with the publication listed:</p><p>full_object.rds: Brain Organoid Phospho-Seq dataset with ATAC, Protein and imputed RNA data</p><p>rna_object.rds: Reference whole cell scRNA-Seq object on Brain organoids</p><p>multiome_object.rds: Bridge dataset containing RNA and ATAC modalities for Brain organoids</p><p>metacell_allnorm.rds: Metacell object for finding gene-peak-protein linkages in Brain organoid dataset</p><p>fullobject_fragments.tsv.gz: fragment file to go with the full object</p><p>fullobject_fragments.tsv.gz.tbi:index file for the full object fragment file</p><p>multiome_fragments.tsv.gz: fragment file to go with the multiome object</p><p>multiome_fragments.tsv.gz.tbi:index file for the multiome object fragment file</p><p>K562_Stem.rds : object corresponding to the pilot experiment including K562 cells and iPS cells</p><p>K562_stem_fragments.tsv.gz:  fragment file to go with the K562_stem object</p><p>K562_stem_fragments.tsv.gz.tbi: index file for the K562_stem object fragment file</p><p>retina.rds : object corresponding to the retinal organoid phospho-seq experiment</p><p>retina_fragments.tsv.gz:  fragment file to go with the retina object</p><p>retina_fragments.tsv.gz.tbi: index file for the retina object fragment file</p><p>retina_multi.rds : object corresponding to the retinal organoid phospho-seq-multiome experiment</p><p>retina_multi_fragments.tsv.gz:  fragment file to go with the retina_multi object</p><p>retina_multi_fragments.tsv.gz.tbi: index file for the retina_multi object fragment file</p><p>To use the K562, multiome, retina and retina_multiome datasets provided, please use these lines of code to import the object into Signac/Seurat and change the fragment file path to the corresponding downloaded fragment file:</p><p>obj <- readRDS("obj.rds") # remove fragment file information Fragments(obj) <- NULL # Update the path of the fragment file Fragments(obj) <- CreateFragmentObject(path = "download/obj_fragments.tsv.gz", cells = Cells(obj))</p><p>To use the K562 and multiome datasets provided, please use these lines of code to import the object into Signac/Seurat and change the fragment file path to the corresponding downloaded fragment file:</p><p>obj <- readRDS("obj.rds") # remove fragment file information Fragments(obj) <- NULL # Update the path of the fragment file Fragments(obj) <- CreateFragmentObject(path = "download/obj_fragments.tsv.gz", cells = Cells(obj))</p><p>To use the "fullobject" dataset provided, please use these lines of code to import the object into Signac/Seurat and change the fragment file path to the corresponding downloaded fragment file: </p><p>#load the stringr package library(stringr) #load the object obj <- readRDS("obj.rds") # remove fragment file information Fragments(obj) <- NULL #Remove unwanted residual information and rename cells obj@reductions$norm.adt.pca <- NULL obj@reductions$norm.pca <- NULL obj <- RenameCells(obj, new.names = str_remove(Cells(obj), "atac_")) # Update the path of the fragment file Fragments(obj) <- CreateFragmentObject(path = "download/obj_fragments.tsv.gz", cells = Cells(obj))</p><p> </p><p> </p>

Static and Dynamic Model Calibration for Upper Thermosphere Determination (2023SW003810)

<p>The dataset contains the background and static calibration models, associated with the figure data and routines. </p>

Supplemental data to AALE 2024 publication "Adaptive manufacturing: dynamic resource allocation using multi-agent reinforcement learning"

<p>Release as supplementary material for our contribution at AALE 2024: "Adaptive manufacturing: dynamic resource allocation using multi-agent reinforcement learning"<br><br>The evaluation datasets stored in this collection are used to compare the performance of multi-agent reinforcement learning. In addition, the performance of other methods such as (meta-) heuristic algorithms or single agent reinforcement learning algorithms or novel methods of search space reduction can also be compared.</p>

Data from: Quantifying the impacts of management and herbicide resistance on regional plant population dynamics in the face of missing data

<p>A key challenge in the management of populations is to quantify the impact of interven-tions in the face of environmental and phenotypic variability. However, accurate estima-tion of the effects of management and environment, in large-scale ecological research is often limited by the expense of data collection, the inherent trade-off between quality and quantity, and missing data.</p> <p>In this paper we develop a novel modelling framework, and demographically informed imputation scheme, to comprehensively account for the uncertainty generated by miss-ing population, management, and herbicide resistance data. Using this framework and a large dataset (178 sites over 3 years) on the densities of a destructive arable weed (Alo-pecurus myosuroides) we investigate the effects of environment, management, and evolved herbicide resistance, on weed population dynamics.</p> <p>In this study we quantify the marginal effects of a suite of common management prac-tices, including cropping, cultivation, and herbicide pressure, and evolved herbicide re-sistance, on weed population dynamics.</p> <p>Using this framework, we provide the first empirically backed demonstration that herbi-cide resistance is a key driver of population dynamics in arable weeds at regional scales. Whilst cultivation type had minimal impact on weed density, crop rotation, and earlier cultivation and drill dates consistently reduced infestation severity.</p> <p>Synthesis and applications: As we demonstrate that high herbicide resistance levels can produce extremely severe weed infestations, monitoring of herbicide resistance is a pri-ority for famers across western Europe. Furthermore, developing non chemical control methods is essential to control current weed populations, and prevent further resistance evolution. We recommend that planning interventions that center on crop rotation and incorporate spring sewing and cultivation to provide the best reductions in weed densi-ties. More generally, by directly accounting for missing data our framework permits the analysis of management practices with data that would otherwise be severely compro-mised.</p>

Simulations for "Molecular Dynamics-Based Identification of Binding Pathways and Two Distinct High-Affinity Sites for Succinate in the Succinate Receptor 1 SUCNR1/GPR91"

Open the record for dataset details and reuse information.

SI Movies for "Dynamics of dominance: maneuvers, contests, and assessment in the posture-scale movements of interacting zebrafish"

<p>This is a repository of the Supplementary Movies from the paper "Dynamics of dominance: maneuvers, contests, and assessment in the posture-scale movements of interacting zebrafish".</p><h3>SM1</h3><p><br>This movie is a visualization of the tracking pipeline output, namely the 3D bodypoint positions of the two zebrafish over time. In this animation, we show the 3D locations of the head (circle), pec (square) and tail (cross) bodypoints of the winner (red) and loser (blue) throughout an entire experiment, the same experiment shown in Fig3(A) of the paper. The movie is downsampled from the 100fps recordings to 25fps to reduce file size. (1hr 23min 29sec)</p><h3>Notes on SM2-6</h3><p>In these movies we show two panels. The left panel shows an animation of the contestant 3D bodypoint positions. The right panel shows the XZ camera view of the tank with the XZ image coordinates of bodypoints drawn over the fish. The movies run at 100fps and are all 2 minutes long. The movies are each centered at a different time during the FishTank20200130_153857 recording, with one minute before, and one minute after, the chosen centered point.</p><h3>SM2</h3><p>This video is centred at the frame identified as the start of the first fight bout.</p><h3>SM3</h3><p>This video is centered at the frame identified as the middle of the first fight bout.</p><h3>SM4</h3><p>This video is centered at the frame identified as the end of the first fight bout.</p><h3>SM5</h3><p>This video is centered at the frame identified as the start of the second fight bout.</p><h3>SM6</h3><p>This video is centered at the frame identified as the end of the second fight bout.</p>

Data from: programming co-assembled peptide nanofiber morphology via anionic amino acid type: insights from molecular dynamics simulations

<p>Co-assembling peptides can be crafted into supramolecular biomaterials for use in biotechnological applications, such as cell culture scaffolds, drug delivery, biosensors, and tissue engineering. Peptide co-assembly refers to the spontaneous organization of two different peptides into a supramolecular architecture. Here we use molecular dynamics simulations to quantify the effect of anionic amino acid type on co-assembly dynamics and nanofiber structure in binary CATCH(+/-) peptide systems. CATCH peptide sequences follow a general pattern: CQCFCFCFCQC, where all C's are either a positively charged or a negatively charged amino acid. Specifically, we investigate the effect of substituting aspartic acid residues for the glutamic acid residues in the established CATCH(6E-) molecule, while keeping CATCH(6K+) unchanged. Our results show that structures consisting of CATCH(6K+) and CATCH(6D-) form flatter β-sheets, have stronger interactions between charged residues on opposing β-sheet faces, and have slower co-assembly kinetics than structures consisting of CATCH(6K+) and CATCH(6E-). Knowledge of the effect of sidechain type on assembly dynamics and fibrillar structure can help guide the development of advanced biomaterials and grant insight into sequence-to-structure relationships.</p>

Molecular dynamics results of the complex 3CLpro allosteric groove with compound 5

<p>Molecular dynamics results of the complex 3CLpro allosteric groove with compound <strong>5</strong>. The protein structure is shown in gray and the compound <strong>5</strong> is shown in magenta.</p>

Photoactivation of the Orange Carotenoid Protein Requires Two Light-Driven Reactions Mediated by a Metastable Monomeric Intermediate – Absorption Spectra and Global Analysis Results, Molecular Dynamics Simulations

<p>Time-resolved absorption and molecular dynamics trajectory datasets associated with: Rose, J. B.; Gascón, J. A.; Sutter, M.; Sheppard, D. I.; Kerfeld, C. A.; Beck, W. F. Photoactivation of the Orange Carotenoid Protein Requires Two Light-Driven Reactions Mediated by a Metastable Monomeric Intermediate. <i>Phys. Chem. Chem. Phys.</i> <strong>2023</strong>, DOI: 10.1039/d3cp04484j.</p>

Spatiotemporal profiling defines persistence and resistance dynamics during targeted treatment of melanoma

<p>Processed data:</p><p>inferCNV_output_WM4237.zip, inferCNV_output_WM4007.zip - InferCNV-derived CNV profiles for 12 Visium samples of model WM4237 or WM4007.</p><p>ad_all_human_clustered_cnv_WM4237.h5ad, ad_all_human_clustered_cnv_WM4007.h5ad - AnnData object containing integration, dimensionality reduction and clustering of the inferCNV-derived CNV profiles for 12 Visium samples of model WM4237 or WM4007.</p><p>ad_all_human_clustered_im_st_WM4237.h5ad, ad_all_human_clustered_im_st_WM4007.h5ad - AnnData object containing integration, dimensionality reduction and clustering of imaging and nuclear morphometric features (output of STQ pipeline) for 12 Visium samples of model WM4237 or WM4007. "_im" for imaging, "_st" for Visium.</p><p>ad_all_human_clustered_im_ad_WM4237_m.h5ad, ad_all_human_clustered_im_ad_WM4007_m.h5ad - AnnData object containing integration, dimensionality reduction and clustering of imaging and nuclear morphometric features (output of STQ pipeline) for additional H&E slides (non-Visium) of model WM4237 or WM4007. "_im" for imaging, "_m" for Macenko normalization of the H&E slides with STQ.</p><p>ids_WM4237_AD.txt, ids_WM4007_AD.txt, ids_WM4237_ST.txt, ids_WM4237_ST.txt - list of identifiers of all samples (tissue sections).</p><p>ad_all_human_clustered_st_WM4237.h5ad, ad_all_human_clustered_st_WM4007.h5ad -  AnnData object containing integration, dimensionality reduction and clustering of RNA profiles for 12 Visium samples of model WM4237 or WM4007.</p><p>CNV_burden_WM4237.csv, CNV_burden_WM4007.csv -  Per-spot values of InferCNV-derived CNV burden for samples of models WM4237 or WM4007.</p><p>ad_all_scaled_filtered_st_WM4237.h5ad, ad_all_scaled_filtered_st_WM4007.h5ad -  AnnData objects containing concatenated and pre-processed samples of WM4237 or WM4007.</p><p>WM4237_3_AD_m-Imaging-STQ.zip, WM4007_3_AD_m-Imaging-STQ.zip - Imaging portion of STQ pipeline output derived from additional (non-Visium) H&E-stained tissue sections of model WM4237 or WM4007.</p><p>WM4237_ST-Imaging-STQ.tar.gz, WM4007_ST-Imaging-STQ.tar.gz -  Imaging portion of STQ pipeline output derived from Visium H&E-stained tissue sections of model WM4237 or WM4007.</p><p>WM4237-ST-downstream-output.tar.gz, WM4007-ST-downstream-output.tar.gz - ST-downstream-processing pipeline output for Visium samples of model WM4237 or WM4007.</p><p>WM4237-STQ-sequencing.tar.gz, WM4007-STQ-sequencing.tar.gz - RNA sequencing portion of STQ pipeline output derived from Visium samples of model WM4237 or WM4007.</p><p>rna-pseudotime-ordered.zip -  Pseudotime ordering of RNA profiles of spots for each time point (T0, T1, T2, T3, T4, TC) for samples of models WM4237 and WM4007.</p>

opencc-by-nc-nd-4.0May 2023View details →

Supplementary animations for "Dynamical friction in self-interacting ultralight dark matter"

<p>Animations to supplement "Dynamical friction in self-interacting ultralight axion dark matter", <em>Phys.Rev.D</em> 109 (2024) 6, 063501.</p> <p>These animations are also available from <a href="https://www.youtube.com/playlist?list=PLHrf0iQS5SY6COihRVYJkz31smUyDvFwW">https://www.youtube.com/playlist?list=PLHrf0iQS5SY6COihRVYJkz31smUyDvFwW</a>.</p>

Exploring Cellular Dynamics: A Comprehensive Analysis of SH-SY5Y Cell Growth in Variable Polycarbonate Chambers and Comparative Insights with Standard Petri Dishes

<div> <div> <div> <div> <div> <div> <p>The provided illustrations delineate the proliferation of SH-SY5Y cells within polycarbonate chambers with varying diameters, ranging from 3 mm to 50 mm. HBE and HeLaGFP cells serve as points of comparison. Five microscopic images were captured for each culture plate, one at the central location and four at random sites. To trace the growth trajectory across different chamber sizes, images from various culture days underwent manual binarization using GIMP and/or ImageJ software. The resulting binarized images can be accessed in the 'masks' directories within the zipped files, corresponding to the attached images. Comparative analysis of cell growth within these chambers was conducted against growth observed in standard 60 mm Petri dishes from Corning.</p> <p>For monitoring cell growth, we employed an Olympus phase-contrast microscope (CKX53) equipped with a DLT camera (DLT-Cam PRO 1080 HDMI USB).</p> <div> <div> <div> <p>The file nomenclature employed herein serves to represent the primary experimental conditions under consideration. It comprises a hierarchical structure, encompassing various parameters as follows: cellsName_passageNumber_cellSuspension_measurementDayNumber_chamberDiameter_regionNumber_hOfFluids_number.</p> <p>The cellsNames parameter encapsulates the complete nomenclature of the cells utilized in the experiment.</p> <p>The passageNumber parameter represents the passage number and is encoded as 'pNumber.'</p> <p>The cellSuspension parameter is denoted by distinct codes:</p> <ul> <li>'c1' designates a cell suspension with a concentration of 5x10^5 cells/ml.</li> <li>'c3' signifies a cell suspension with a concentration of 1x10^5 cells/ml.</li> <li>'c4' denotes a cell suspension with a concentration of 5x10^4 cells/ml.</li> </ul> <p>The measurementDayNumber parameter is encoded as 'afterXXXdays,' signifying the number of culture days.</p> <p>The 'd' followed by a number (e.g., d6, d8, d10, d12, d15) indicates the diameter of the chamber in millimeters. In a given series of files (e.g., 1day_d6_1, 1day_d6_2, 1day_d6_3, 1day_d6_4, 1day_d6_5), the trailing numeral (_1, _2, _3, _4, _5) corresponds to a photograph captured in a distinct region of the sample.</p> <p>The hOfFluids parameter is employed to signify the height ('h') of the fluid above the cells in the chamber, expressed in millimeters.</p> <div> <div> <div> <p>In adherence to the aforementioned conventions, every file name serves to distinctly characterize a particular collection of experimental conditions. This practice enhances both the comprehensibility and systematic organization of the data. It is noteworthy that the complete set of parameters may not always be provided; for instance, the height of the column of medium is assumed to be 2 mm unless otherwise specified.</p> </div> </div> </div> </div> </div> </div> <br> <p> </p> </div> </div> </div> </div> </div> </div>

Molecular dynamics simulation of CFTR with inhibitor CFTRinh-172

<p>Molecular dynamics simulation trajectory, parameter files for the systems of human CFTR protein with inhibitor CFTRinh-172. CFTRinh-172 was tested with two different poses.</p>

Data from: Temporal dynamics in psychological assessments: a novel dataset with scales and response times

<p>This dataset is collected from February 27 to March 17, 2021, it includes responses from 24,292 students to four recognized psychological scales: PHQ-9, GAD-7, ISI, and PSS. A unique aspect of this dataset is the inclusion of response time data, which reflects the duration students took to answer each question. This temporal information offers a new dimension in understanding respondent behavior and enhances the reliability of these scales.</p>

Data repository for: Frictional weakening leads to unconventional singularities during dynamic rupture propagation

<p>Laboratory data to accompany publication <span>Frictional weakening leads to unconventional singularities during dynamic rupture propagation</span>, submitted to Earth and Planetary Science Letters.</p> <p>For any further queries please contact federica.paglialunga@epfl.ch</p>

opencc-zeroDec 2023View details →

Dynamics of leaching of POPs and additives from plastic in a Procellariiform gastric model: Diet and polymer dependent effects and implications for long-term exposure

<p>Procellariiform seabirds are known to have high rates of plastic ingestion. We investigated the bioaccessibility of plastic-associated chemicals [plastic additives and sorbed persistent organic pollutants (POPs)] leached from plastic over time using an in vitro Procellariiform gastric model. High-density polyethylene (HDPE) and polyvinyl chloride (PVC), commonly ingested by Procellariiform seabirds, were manufactured with one additive [decabrominated diphenyl ether (PBDE-209) or bisphenol S (BPS)]. HDPE and PVC added with PBDE-209 were additionally incubated in salt water with 2,4,4'-trichloro-1,1'-biphenyl (PCB-28) and 2,2',3,4,4',5'-hexachlorobiphenyl (PCB-138) to simulate sorption of POPs on plastic in the marine environment. Our results indicate that the type of plastic (nature of polymer and additive), presence of food (i.e., lipids and proteins) and gastric secretions (i.e., pepsin) influence the leaching of chemicals in a seabird. In addition, 100% of the sorbed POPs were leached from the plastic within 100 hours, while only 2-5% of the additives were leached from the matrix within 100 hours, suggesting that the remaining 95% of the additives could continue to be leached. Overall, our study illustrates how plastic type, diet and plastic retention time can influence a Procellariform's exposure risk to plastic-associated chemicals.</p>