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Replication package for "Real Effects of Financial Distress: The Role of Heterogeneity"
<p>This package contains synthetic data and code that generates the figures and tables in the paper and the online appendix. Data files in .csv and stata format have been provided. A readme has been included that provides detailed guidelines how to run the codes.</p>
Data used in real world-inspired experiments for submission titled "Causal Contextual Bandits with Targeted Interventions"
<p>Data used in real world-inspired experiments for submission titled "Causal Contextual Bandits with Targeted Interventions".</p>
An Integrated Framework for Real-time Intelligent Traffic Management of Smart Highways
<p>Raw data for the paper "An Integrated Framework for Real-time Intelligent Traffic Management of Smart Highways".</p>
Raw dataset for "Rehybridization dynamics into the pericyclic minimum of an electrocyclic reaction imaged in real-time"
<p>The dataset contains raw diffraction images in .tiff format. Each image file name contains three numbers, separated by "_". The first number refers to the order in which the images were taking in laboratory time. The second number refers to the absolute translation stage position in millimeters in the optical beam path of the pump beam. The stage position can be converted into a pump-probe delay time (taking into account the speed of light and a factor of 2 for the beam path, since the pulses move back and forth on the stage). Larger stage position values correspond to the optical pump pulse arriving later with respect to the probe pulse. Time zero was determined to be at 156.26 mm using a solid reference sample.</p>
Detection of the Fire Drill anti-pattern: 15 real-world projects with ground truth, issue-tracking data, source code density, models and code
<p>This package contains artifacts for <strong>15</strong> real-world software projects. The data is supposed to aid the detection of the presence of the Fire Drill anti-pattern. We include original data, ground truth, code (experimental setups and models), and notebooks. The data supports two distinct methods of detecting the AP: a) through issue-tracking data, and b) through the underlying source code. This version of the dataset corresponds to <strong>v8</strong> of the <a href="https://arxiv.org/abs/2104.15090v8">technical report</a> and the <a href="https://github.com/MrShoenel/anti-pattern-models/releases/tag/arxiv-v8">GitHub repository</a>. The package includes the following:</p> <p>Original data:</p> <ul> <li>For each project, its <strong>original</strong> artifacts (e.g., wikis, meeting minutes, mentor's notes, etc.)</li> <li>Evaluation of raters' notes by the assessor</li> </ul> <p>Fire Drill in issue-tracking data:</p> <ul> <li><strong>Ground truth</strong> for whether and how strong each project exhibits the Fire Drill AP, on a scale from [0,10]. This was determined by two individual raters, who also reached a consensus.</li> <li>Coefficients for indicators for the first method, per project.</li> <li>Detailed issue-tracing data for each project: what occurred and when.</li> <li>Time logs for each project.</li> </ul> <p>Fire Drill in source-code data:</p> <ul> <li><strong>Four</strong> technical reports that document the developed method of how to translate a description into a detectable pattern, and to use the pattern to detect the presence and to score it (similar to the rating). Also includes a report for how activities were assigned to individual commits.</li> <li>Source code density data (metrics) for each commit in each of the nine projects as a separate dataset.</li> <li>Code: a snapshot of the repository that holds all code, models, notebooks, and pre-computed results, for utmost reproducibility (the code is written in R).</li> </ul>
AALTO - Analysis of Drone Propagation With Ray Tracing From Sub-6 GHz Upto TeraHertz Frequencies in a Real World Urban Environment - DATA
<p>The data set includes simulation results from radio propagation modelling of TERAWAY links (at W, D and THz bands) in realistic 3D propagation environments. The modelling is performed using a Ray Tracing Tool developed in MATLAB environment at Aalto University. Ray tracing technique used in this tool is based on Image Theory (IT) algorithm. Unlike a quasi three-dimensional environment, it supports ray tracing in full three dimension. </p> <p>This data set contains propagation modelling results of the TERAWAY link. Output data includes (but is not limited to): Multipath component IDs, Path Distance (meter), Angle of Arrival AoA (degree), Angle of Departure AoD (degree), Direction of Arrival DoA (degree), Direction of Departure DoD (degree), E-Field (Volt/meter), H-Field (Ampere/meter), Phase (Radians), Power (Watts), Number of reflections a path experienced, Number of diffractions a path experienced, information that is it ground reflected path or not, Receiver location (x and y coordinates), information that is it rooftop path or not.</p>
Immune Checkpoint Inhibitor, Nivolumab, Combined with Chemotherapy Improved the Survival of Unresectable Ad-vanced and Metastatic Esophageal Squamous Cell Carcinoma: a real world experience
<p><strong>Figure S1 the detail of treatments.</strong> Blue arrow means this patient was still alive at the latest date of follow up. Hollow circle means this patient die. The others were lose follow up at the latest date of follow up.</p> <p><strong>Figure S2 progression free survival (PFS) of patients received immunotherapy, including 5 nivolumab and chemotherapy and 1 dual immune check point inhibitor, on different PD-L1 tumor cells (TC) expression.</strong> (A) divided by PD-L1 TC <1% or ≧1%. (B)divided by PD-L1 TC <10% or ≧10%.</p>
Real world networks for network classification method evaluation
<p>This research presents a set of network databases based on real-world data, aimed at evaluating the effectiveness of network classification methods. The first database, called the "Social database," consists of networks from the Stanford Network Analysis Project (SNAP) platform, including two classes, namely, Google+ and Twitter, we have made some preprocessment and also reduced the original dataset from the SNAP platform such that each class contains 50 networks. The other databases presented in this study are collectively known as the "Metabolic database," as they are constructed using the substrate-product network model and are based on biochemical reactions of organisms obtained from the Kyoto Encyclopedia of Genes and Genomes database (KEGG). The networks were generated using a model that considers metabolites as vertices and chemical reactions as edges. The Metabolic database comprises six classification schemes, which include the "kingdom-database" with 160 network samples, where each of the four classes contains 40 networks representing animal, plant, fungi, and protist kingdoms. The remaining databases in the Metabolic database are the "Animal-database," "Fungi-database," "Plant-database," "Firmicutes-Bacilis-database," and "Actinobacteria-database," each containing a varying number of network samples.<br> <br> To ensure consistency and comparability of our results, we implemented a standardization procedure whereby all networks were converted to adjacency list format. This allowed us to more efficiently process and analyze the data.</p>
Ab initio multiple spawning simulations for "Rehybridization dynamics into the pericyclic minimum of an electrocyclic reaction imaged in real-time"
<p>60 ICs a(0.82)-2SA-cas(6,4)-SCF/6-31G* using AIMS/uPBE0-D3 for 1st ps of alpha-terpinene photochemistry. 20 for each<br> conformer upon rotation around the isopropyl group. Each initial condition (IC) is saved in a<br> separate folder labeled after conformers m, p, t, and with the index number of the initial condition.<br> The folder contains the positions and amplitudes of all trajectory basis functions (TBF) arising from<br> the initial condition as well as information about their coupling. The TBF index 1 always refers to<br> the TBF launched in the Franck-Condon region of the excited state based on an initial condition<br> sampled from a ground state Wigner distribution. </p> <p>Description of the folders and subsequent files in each IC folder:</p> <p>Simulation_data <br> X-YYYY : X is the isomer and YYYY is the initial condition number<br> "Positions.x.xyz" : Files containing the geometries of each TBF in cartesian coordinates in Angstroms<br> at each time step. The "x" in the filename corresponds to the index of the TBF.<br> "Amp.x" : Files containing the TBF amplitudes for each timestep.<br> "Spawn.log" : File containing timing information about the spawning events.<br> "S.dat" : File containing coupling matrices between the TBFs for every time step.<br> "ext_x" : Folders containing extensions of TBFs on DFT level. Each folder contains a file<br> "coors.xyz" with cartesian coordinates in Angstroms at each time step. The<br> timestep size of the DFT trajectories is uniformly 0.5 femtoseconds. Only TBFs in<br> the groundstate are extended on DFT level. Therefore, there is no folder "ext_1".</p> <p>Simulation_Parameters : Parameters for FMS and TeraChem nonadiabatic dynamics <br> X-rotamer : x is isomer containing parameter files for all X rotamers<br> "c0.casscf" : Binary file with the alpha(0.82)-SA2-CAS(6,4)-SCF/6-31G* orbitals<br> "Geometry.dat" : Initial condition (position and momentum) to start the FMS/TeraChem nonadiabatic dynamics<br> "Control.dat" : Parameter file for running FMS<br> "misc_options" : Parameter file for running TeraChem <br> "DFT-tc.in" : TeraChem adiabatic dynamics on ground electronic state (ext_x)<br> </p>
Data and code to create figures of "Rehybridization dynamics into the pericyclic minimum of an electrocyclic reaction imaged in real-time"
<p>Instruction of making Fig 4 in the main text:<br> 04/07/2023, SLAC, CA<br> Yusong (Liu), on behalf of Thomas (Wolf)</p> <p>1. Making Figure 1:<br> Figure 1 is fully vectorized with file 'Figure-1.svg', also saved in a png version, 'Figure-1.png'</p> <p>2. Making Figure 2:<br> The data plots in panels a and b are produced from a Matlab script: 'MakingFig2MainText.m'<br> This script loads data saved in 'TerpNatCommFig2.mat' and produce several files if using the saving condition:<br> 'PDFStaticTwoDelaysExpSim_22.fig'<br> 'PDFStaticTwoDelaysExpSim_22.png'<br> 'PDFStaticTwoDelaysExpSim_22.svg'<br> The final Figure 2, 'Figure-2.svg', then was assembled using 'PDFStaticTwoDelaysExpSim_22.svg' and 'Molskeleton.svg'.</p> <p>3. Making Figure 3:<br> The data plots in panels a, b, and c are produced from a Matlab script: 'MakingFig3MainText.m'<br> This script loads data saved in 'TerpNatCommFig3.mat' and produce several files if using the saving condition:<br> 'Fig3PDFFalseCMapLineOutExpSim_V10.fig'<br> 'Fig3PDFFalseCMapLineOutExpSim_V10.png'<br> 'Fig3PDFFalseCMapLineOutExpSim_V10.svg'<br> The final Figure 3, 'Figure-3.svg', then was assembled using 'Fig3PDFFalseCMapLineOutExpSim_V10.svg' and 'MolCartoonsFig3.svg'.</p> <p>4. Making Figure 4:<br> (1). Drawing the signal in Fig. 4 panel a<br> Fig. 4a was generated from a py script: 'MakingFig4aMainText.ipynb'<br> Run this script and it will load the data set 'MainFig4aData.npy'generate a figure showing Fig. 4a.<br> Change the figure saving condition to decide whether or not save the figure to both .svg and .png files as below:<br> 'Figure-4a.png'<br> 'Figure-4a.svg'<br> (2). Drawing the signals in Fig. 4 panels b and c<br> These two panels, the signals are drew from a Matlab script: 'MakingFig4bcMainText.m'<br> Run this script and it will load data set 'TerpNatCommFig4.mat' and plot panels b and c<br> Change the figure saving conditions to decide whether or not save to both .svg and .png files as below:<br> 'Fig4DataPanelsbc_v11.fig'<br> 'Fig4DataPanelsbc_v11.png'<br> 'Fig4DataPanelsbc_v11.svg'<br> (3). Fig 4 is then assembled with the 3 data plot panels, molecular cartoons, and equations:<br> Data plot panels:<br> 'Figure-4a.svg'<br> 'Fig4DataPanelsbc_v11.svg'<br> Molecular cartoons:<br> 'Fig4aCartoon.svg' 'Fig4bCartoon.svg' 'Fig4cCartoon.svg'<br> And equations:<br> 'Fig4bEquPhi.svg' 'Fig4cEquPsi.svg'<br> Fig 4 is fully vectorized with file: 'Figure-4.svg' and also saved as a .png version 'Figure-4.png'</p> <p>5. Making Figure-5:<br> The whole Fig. 5 is produced from a single .py script: 'MakingFig5MainText.ipynb'<br> Run the script and it will plot Figure-5. If choosing the save conditions, then it will save Figure-5 to 'Figure-5.svg' and 'Figure-5.png'.</p>
Data used for global TEC forecasting for space weather application based on deep learning techniques: a comparative study and considerations for real-time implementation
<p>This dataset contains the measurements of TEC from Global Ionospheric Maps (GIMs), provided by the International GNSS Service (IGS), as the target parameter and the global geomagnetic Kp index as the external input. This data set is composed of samples from 2005 to 2017. The datasets have been curated to obtain the same resolution (2 hs) of the two parameters.</p>
MAESTRO Real - Multi-Annotator Estimated Strong Labels; Evaluation dataset
<p>Evaluation dataset for the DCASE challenge 2023 task 4 B - Sound Event Detection with Soft Labels.</p> <p>The evaluation dataset contains 26 real-life audio files from 5 different acoustic scenes.</p>
Data for Real-K 18SIB02 Task 1.2 Report "CHARACTERISATION OF FURNACE THERMAL EFFECTS ON THE REPRODUCIBILITY OF HIGH-TEMPERATURE FIXED-POINT CELLS"
<p>Report "CHARACTERISATION OF FURNACE THERMAL EFFECTS ON THE REPRODUCIBILITY OF HIGH-TEMPERATURE FIXED-POINT CELLS" was written as part of WP1 of Real-K 18SIB02 and can be found at 10.5281/zenodo.7896128</p> <p>A summary was presented at the 10th International Temperature Symposium DOI10.6028/NIST.SP.2100-05 and a paper to be submitted to the proceedings </p> <p>Data files contain individual melt curves provided by participants, together with the results from fitting 3-rd order polynomial curves as described in the report. The output from each melting curve has:</p> <p>The point-of-inflection, with type-1 uncertainty<br> An extrapolated upper limit to the liquidus, with type-1 uncertainty<br> The melting range<br> <br> The liquidus and its uncertainty are derived from the above</p>
Real and Psuedosynthetic timeseries used in "Characterizing High Rate GNSS Velocity Noise for Synthesizing a GNSS Strong Motion Learning Catalog"
<p><strong>5Hz GNSS Velocity Data for the submitted work: </strong>"Characterizing High Rate GNSS Velocity Noise for Synthesizing a GNSS Strong Motion Learning Catalog" Dittmann et al (202?)</p> <p><strong>Datasets included:</strong></p> <ol> <li>Pseudosynthetic timeseries, ambient timeseries and training featuresets generated for Dittmann, et al (202?) </li> <li>Real GNSS 5Hz validation featuresets from <a href="https://doi.org/10.1029/2022JB024854">Dittmann, et al. (2022) </a></li> </ol> <p>Timeseries and Featuresets are stored in <a href="https://parquet.apache.org/">Apache Parquet</a> format.</p> <p><strong>Getting Started:</strong><br> Notebook demos for reading using <a href="https://docs.conda.io/en/latest/">conda</a>+ jupyterlab (easiest).<br> In a terminal:</p> <ol> <li>Unzip untar (mac/linux tar -xvf psuedo_synth_gnssvel.tar.gz)</li> <li> <pre><code>conda env create -f environment.yml conda activate pgv23_zenodo jupyter lab</code></pre> <p> </p> </li> <li>Open the notebook “reading_data.ipynb”</li> </ol> <p> </p> <p><strong>Data References:</strong></p> <p><a href="https://ngawest2.berkeley.edu/">NGA-West 2 (NGAW2) Ground Motion Database </a></p> <p><a href="https://github.com/crowellbw/SNIVEL">SNIVEL</a> GNSS Velocity Processing</p> <p> </p>
Real-time Welding Sparks Detection on Construction Sites us-ing Contour Detection and Deep Learning
<p>One of the primary causes of fires at construction sites is welding sparks. Fire detection systems utilizing computer vision technology offer a unique opportunity to monitor fires in construction sites. However, little effort has been made to date in regards to real-time tracking of small sparks that can lead to major fires at construction sites. In this study, a novel method is proposed to detect welding sparks in real time contour detection with deep learning parameter tuning. An automatic parameter tuning algorithm employing a convolutional neural network was developed to identify the optimum hue-saturation-value. Additional filtering methods regarding non-welding zone and contour area-based filter, were also newly developed to enhance prediction accuracy of welding sparks. The method was evaluated using 230 welding sparks images and 104 videos. The results obtained from the welding images indicate that the suggested model for detecting welding sparks achieves a precision of 74.45% and a recall of 63.50% when noise images, such as flashing and reflection light, were removed from the dataset. Furthermore, our findings demonstrate that the proposed model is effective in capturing the number of welding sparks in the video dataset, with a 95.2% accuracy in detecting the moment when the number of welding sparks reaches its peak. These results highlight the potential of automated welding sparks detection for enhancing fire surveillance at construction sites.</p>
On the Determinants of Real Estate Returns
<p>Data on GDP growth, inflation, and remittances are collected from the Pakistan Economic Survey.<a href="#_ftn1">[1]</a> The growth rate of the consumer price index (CPI) has been used to measure inflation. The database of the State Bank of Pakistan has been used to collect data on interest rates. We used commercial banks’ lending rates to proxy the interest rate in the economy. While population density at the Pakistan level was extracted from the Pakistan Economic Survey. However, data on the city-wise population density was not available, therefore, we computed it by extrapolating the city-wise density figure for 2017 on the basis of city-wise population growth rates.</p> <p> </p> <p> </p>
FIGURE 7 in Are the recently described fossil Mole Crickets of Myanmar amber real gryllotalpids? (Orthoptera: Gryllotalpidae & Gryllidae)
FIGURE 7. Photograph of apical spurs on the fore tibia in Mole Crickets and related groups. A, Gryllotalpa sp. Mole cricket (Gryllotalpidae: Gryllotalpinae). B, Ammopelmatus sp. Jerusalem cricket (Stenopelmatidae: Stenopelmatinae). C, Mellogryllus mutus Cadena-Castañeda et al. 2022 (Gryllidae: Gryllinae). D, Xya sp. Pigmy mole crickets (Tridactylidae: Tridactylinae). E, †Pherodactylus rectanguli n. comb. (Gryllidae: Gryllinae).
FIGURE 1 in Are the recently described fossil Mole Crickets of Myanmar amber real gryllotalpids? (Orthoptera: Gryllotalpidae & Gryllidae)
FIGURE 1. Habitus of †Tresdigitus rectanguli n. comb., specimen ECNU-AM-0019. A, photograph, ventral view. B, photograph, dorsal view. Scale bars = 1 mm.
FIGURE 3 in Are the recently described fossil Mole Crickets of Myanmar amber real gryllotalpids? (Orthoptera: Gryllotalpidae & Gryllidae)
FIGURE 3. Habitus of †Pherodactylus rectanguli n. comb., specimen ECNU-AM-0010, adult male. A, photograph, dorsal view. B, photograph, ventral view. Scale bars = 1 mm.
FIGURE 5 in Are the recently described fossil Mole Crickets of Myanmar amber real gryllotalpids? (Orthoptera: Gryllotalpidae & Gryllidae)
FIGURE 5. Habitus of †Pherodactylus rectanguli n. comb., specimen ECNU-AM-0024, adult female. A, photograph of right side. B, photograph of left side. Scale bars = 1 mm.
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.