Find research datasets worth reusing

opencc-by-4.0Dec 2017View details →

Flying under the LiDAR: relating forest structure to bat activity and call diversity

Bat call data collected at Ordway-Swisher Biological Station from June - September 2015.

Hyper-crosslinked polyphosphines as microporous macroligands for catalytic CO2 hydrogenation – a systematic study on structure-activity relations

This dataset contains all raw data to the manuscript "Hyper-crosslinked polyphosphines as microporous macroligands for catalytic CO2 hydrogenation – a systematic study on structure-activity relations" of Arne Nisters, Steffen Schleuning, Gerd Buntkowsky, Torsten Gutmann, Marcus Rose.

A qualitative assessment of limits of active flight in low density atmospheres

Supplementary video files for the "A qualitative assessment of limits of active flight in low density atmospheres".  Supplementary Dataset S1 contains the following video files recorded from the stereo camera no.1:  1) flies_2_N2_gstream1.mp4 - video showing the experiment with flushing with N2 (first repetition). 2) flies_2_N2_2_gstream1_1.mp4 - video showing the experiment with flushing with N2 (second repetition). 3) flies_2_N2_3_gstream1_1.mp4 - video showing the experiment with flushing with N2 (third repetition). 4) flies_1__He_gstream1_1.mp4 - video showing the experiment with flushing with He (first repetition) 5) flies_2_He_2_gstream1_1.mp4 - video showing the experiment with flushing with He (second repetition) 6) flies_2_He_3_gstream1_1.mp4 - video showing the experiment with flushing with He (third repetition)

Data for "Anthropogenic linear features exhibit greater mammal activity relative to surrounding game trails in a woody savanna"

Code and data investigating mammal use of anthropogenic linear features relative to game trails in South Africa.  Article is titled "Anthropogenic linear features exhibit greater mammal activity relative to surrounding game trails in a woody savanna".

Active Monitoring Mechanism (AMM)

AMM is a solution for detecting model deviation in control-based self-adaptive systems with vague observation. Given runtime measurements of a control-based self-adaptive system, AMM generates an active control signal if needed and gives an alarm when model deviation is detected.

Code Generation for Predicting the Radium Activity Equivalent

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Raw dataset for "Multi-Objective Bayesian Active Learning for MeV-ultrafast electron diffraction"

this dataset contains raw data collected at the SLAC MeV-UED facility, the data was saved in .npy format. The name of each file starts with a number referring to the time stamp when it was recorded. “xxxxxxxxxx_Andor1.npy” contains the beam images recorded at the diffraction detector plane associated with the q-resolution “xxxxxxxxxx_qm.npy” contains the beam images recorded at the sample plane associated with the spot size “xxxxxxxxxx_scalars.npy” contains the machine settings and readouts from the EPICs system, scalar names are listed in “scalars.txt” “xxxxxxxxxx_vcc.npy” contains the images recorded at a virtual cathode camera “xxxxxxxxxx_THzon_img.npy” contains the THz streaked beam images associated with the temporal length “xxxxxxxxxx_THzoff_img.npy” contains the unstreaked beam images for subtracting intrinsic broadening without THz pulses

openMay 2024View details →

CoolWalks: Assessing the potential of shaded routing for active mobility in urban street networks - Dataset

This contains the raw and processed data for the paper "CoolWalks: Assessing the potential of shaded routing for active mobility in urban street networks" by H. Wolf, M. Szell and A. R. Vierø.

Experimental data: "Dynamical clustering and wetting phenomena in inertial active matter".

In each zip folder, we report experimental data for shaker frequency f=90 Hz, 120 Hz, 150 Hz at different packing fraction, corresponding to different number of particles N=30, 60, 90, 120, 150, 180, 210, 240

Potential Interactions of the 20 Arabidopsis thaliana Mitogen-Activated Protein Kinases (MPKs)

A comprehensive dataset detailing potential interactions of the 20 Arabidopsis thaliana MPKs.

Protocol for Calcium Imaging and Analysis of Hippocampal CA1 Activity Evoked by Non-Spatial Stimuli

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Quantum-Accurate Machine Learning Potentials for Metal-Organic Frameworks using Temperature Driven Active Learning

It contains reference training and test set configurations (and corresponding energy, forces, and virial stress values) for ZIF-8 and MOF-5.

Healthcare Professionals Interpersonal variability and determinants of medical decision thresholds for active management of extremely preterm infants in a level 3 perinatal center in France

Raw data and R script

ARID1A suppresses R-loop mediated STING-Type I Interferon pathway activation of anti-tumor immunity

Clinical trials have identified ARID1A mutations as enriched among patients who respond favorably to Immune Checkpoint Blockade (ICB) in several solid tumor types independent of microsatellite instability. We show that ARID1A loss in murine models is sufficient to induce anti-tumor immune phenotypes observed in ARID1A mutant human cancers, including increased CD8+ T cell infiltration and cytolytic activity. ARID1A deficient cancers upregulated an interferon (IFN) gene expression signature, the ARID1A-IFN signature, associated with increased R-loops and cytosolic single stranded DNA (ssDNA). Overexpression of the R-loop resolving enzyme, RNASEH2B, or cytosolic DNase, TREX1, in ARID1A deficient cells prevented cytosolic ssDNA accumulation and ARID1A-IFN gene upregulation. Further, the ARID1A-IFN signature and anti-tumor immunity were driven by STING dependent Type I IFN signaling, which was required for improved responsiveness of ARID1A mutant tumors to ICB treatment. These findings define a molecular mechanism underlying anti-tumor immunity in ARID1A mutant cancers.

Machine Learning-based Analysis of Electronic Properties as Predictors of Anticholinesterase Activity in Chalcone Derivatives

In this study, we investigated the correlation between the electronic properties of anticholinesterase compounds and their biological activity. While the methodology of such correlation is well-established and has been effectively utilized in previous studies, we employed a more sophisticated approach: machine learning. Initially, we focused on a set of 22 molecules sharing a common chalcone skeleton and categorized them into two groups based on their IC50 indices: high activity and low activity. Utilizing the open-source software Orca, we conducted calculations to determine the geometries and electronic structures of these molecules. Over a hundred parameters were collected from these calculations, serving as the foundation for the features used in machine learning. These parameters included the Mulliken and Lowdin electronic populations of each atom within the skeleton, molecular orbital energies, and Mayer's free valences. Through our analysis, we developed numerous models and identified several successful candidates for effectively distinguishing between the two groups. Notably, the most informative descriptor for this separation relied solely on electronic populations and orbital energies. By understanding which computationally calculated properties are most relevant to specific biological activities, we can significantly enhance the efficiency of drug development processes, saving both time and resources.

Datasets for Digital tools and gamification to improve the consumers' waste recycling activity in smart cities

In the data description you can find following: 1 Methods of choosing participants 2 Translations of survey questions 2.1 Survey A 2.2 Survey B 2.3 Survey C 3 Demographic analysis 3.1 Respondents' age 3.2 Household sizes and dwelling types

opencc-by-nc-4.0May 2024View details →

Activating 2D MoS2 by loading 2D Cu-S nanoplatelets for improved visible light photocatalytic hydrogen evolution, drug degradation, and CO2 reduction

Please see ref:   Temerov, F.; Greco, R.; Celis, J.; Eslava, S.; Wang, W.; Yamamoto, T.; Cao, W. Results Mater. 2024, 22, 100569

Data for "Impacts of an active Pacific Meridional Overturning Circulation on the Pliocene climate and hydrological cycle"

Post-Processing Scripts and Data for the Paper Title: Impacts of an Active Pacific Meridional Overturning Circulation on the Pliocene Climate and Hydrological Cycle Authors: Minmin Fu, Alexey Fedorov Publication Year: 2024 Contact: minmin.fu@yale.edu Overview This repository contains the post-processing scripts and data used in the paper. It includes Jupyter notebooks for data analysis and plot generation, as well as the GCM output and utilities for adding land-sea masks and proxy sites. Repository Structure notebooks/: Jupyter notebooks for analyzing data and creating plots. data/: GCM output files for reproducing all figures in the paper. utilities/: Functions for adding the land-sea mask and proxy sites. PlioMIP2/: PlioMIP2 boundary conditions. Data The data directory contains the GCM output files necessary for reproducing all figures. Ensure you have the necessary storage space as these files may be large. Utilities The utilities directory contains utility functions, including: Land-Sea Mask Functions: Scripts to apply land-sea masks to the data. Proxy Site Functions: Scripts to integrate proxy site data into the analysis. The PlioMIP2 directory contains the boundary condition files used for the PlioMIP2 experiments.

Experimental and Computational Study Towards Identifying Active Sites of Supported SnOx Nanoparticles for Electrochemical CO2 Reduction Using Machine-Learned Interatomic Potentials

SnOx has received great attention as an electrocatalyst for CO2 reduction reaction (CO2RR), however, it still suffers from low activity. Moreover, the atomic-level SnOx structure and the nature of the active sites are still ambiguous due to the dynamism of surface structure and difficulty in structure characterization under electrochemical conditions. Herein, we first enhance its CO2RR performance by supporting SnO2 nanoparticles on two common supports, Vulcan Carbon and TiO2 . Then, electrolysis of CO2 at various temperatures in a neutral electrolyte reveals that the application window for this catalyst is between 12 and 30 °C. Furthermore, our study introduces a machine learning interatomic potential method for the atomistic simulation to investigate SnO 2 reduction and establish a correlation between SnO x structures and their CO 2 RR performance. In addition, selectivity is analyzed computationally with density functional theory simulations to identify the key differences between the binding energies of *H and *CO2−, where both are correlated with the presence of oxygen on the nanoparticle surface. This study offers in-depth insights into the rational design and application of SnOx -based electrocatalysts for CO2RR.

Premature Activation of the HIV-1 Protease is influenced by Polymorphisms in the Hinge Region

Abstract: HIV-1 protease inhibitors are an essential component of antiretroviral therapy. However, drug resistance is a pervasive issue motivating the persistent search for novel therapies. Recent reports found that when protease activates within the host cell cytosol, it facilitates the pyroptotic killing of infected cells. This has led to speculation that promoting protease activation, rather than inhibiting it, could help to eradicate infected cells and potentially cure HIV-1 infection. Here, we used a nanoscale flow cytometry-based assay to characterize protease resistance mutations and polymorphisms. We quantified protease activity, viral concentration, and premature protease activation and confirmed previous findings that major resistance mutations generally destabilize the protease structure. Intriguingly, we found evidence that common polymorphisms in the hinge domain of protease can influence its susceptibility to premature activation. This suggests that viral heterogeneity could pose a considerable challenge for therapeutic strategies aimed at inducing premature protease activation in the future.   Attached is the raw .fcs flow virometry data files and western blot images. In addition, the MIFlowCyt-EV information is attached.