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6,608 results for “code”
PARN and TOE1 constitute a 3′ end maturation module for nuclear non-coding RNAs
<p>HeLa cells were cultured in DMEM (Welgene) supplemented with 9% fetal bovine serum (Welgene). HeLa cells were transfected with 20 nM of siRNAs for four days using Lipofectamine 3000 (Thermo Fisher Scientific). Equal amounts of four different siRNAs were used for each knockdown. In the combinatorial knockdown, we mixed multiple siRNA pools to have a final concentration of 20 nM per siRNA pool. Total RNAs were extracted from siRNA-transfected HeLa cells using TRIzol reagent (Thermo Fisher Scientific) according to the manufacturer’s protocol and treated with DNase I (Takara). mTAIL-seq libraries were prepared as previously described (Lim et al., 2016). Amplified cDNA libraries were sequenced on an Illumina MiSeq platform with 50% of the PhiX control library (Illumina).</p> <p>The uploaded file includes both intensity and sequence information for spike-ins and libraries used in the mTAIL-seq analysis. These data can be processed with Tailseeker 3.1.7 (Chang, 2017) according to the standard workflow of the software. The source codes and container images are available from Zenodo (https://zenodo.org/record/887547; doi:10.5281/zenodo.887546).</p>
Mitigating Network Noise on Dragonfly Networks through Application-Aware Routing (code, data and scripts to reproduce paper results)
<p>This repository contains the data, code, and scripts required to reproduce the results of the paper "Mitigating Network Noise on Dragonfly Networks through Application-Aware Routing" by Daniele De Sensi, Salvatore Di Girolamo and Torsten Hoefler, presented at the 2019 International Conference for High Performance Computing, Networking, Storage, and Analysis. </p> <p>This repository does not contains the code of the library used to automatically tune the routing algorithm, which can be found at http://doi.org/10.5281/zenodo.3372785</p>
Data sets for "Magnetic helicity dissipation and production in an ideal MHD code"
<pre>The tar archive Helicity_in_IdealMHDCode.tar contains an index.html file with links to a directory with "Add-ons" to the FLASH code and the flash.par file. We also list the IDL directory with secondary data and plot routines for each figure used in the paper "Magnetic helicity dissipation and production in an ideal MHD code" by Axel Brandenburg (Nordita) and Evan Scannapiecoo (Arizona State University) with the URL https://arxiv.org/abs/1910.06074.</pre>
Data, Analytical Code, and Model Outputs From: Restoration Treatments Enhance Tree Growth and Alter Climatic Constraints During Extreme Drought
<p>This archive includes data (forest inventories, tree ring measurements, climate variables), statistical code, model outputs, and a preprint copy of Rodman et al. (2024). For more information on specific information, processing methods, and data formats, see "README.md" or "README.html" files associated with this archive</p>
qc3C manuscript simuated sweep configuration and source code
<p>This is the repository of configuration details and source code necessary to reproduce the simulated sweep for the manuscript : qc3C - reference-free quality control for Hi-C sequencing data.</p> <p>The repository also contains the qc3C analysis results used in the paper.</p> <p>This now includes QC reports over the simulated sweep generated by HiCExplorer.</p>
Publishing Reproducible Research Outputs - Thematic coding of interview findings
<p>The spreadsheet in the present dataset (CSV format) includes the anonymised thematic coding that has been applied to our interview findings. A list of interviewees and interview questions is available <a href="https://doi.org/10.5281/zenodo.5141665">here</a>.</p> <p>The thematic coding has been applied by using <a href="https://www.qsrinternational.com/nvivo-qualitative-data-analysis-software/home">NVivo</a>, a professional qualitative analysis software, and then exported in spreadsheet form for public sharing. The findings of this analysis have been used to inform our final report, which is available in our <a href="https://zenodo.org/communities/ke-prro/?page=1&size=20">Zenodo project Community</a>.</p>
AMBON diversity & community composition, data & code
<p>Data and R code for analyzing diversity and community composition of eight assemblages in the Northeast Chukchi Sea in 2015 and 2017. For details of the analysis, results and interpretation, see:</p> <p><em>Mueter, F.J., Iken, K., Cooper, L.W., Grebmeier, J.M., Kuletz, K.J., Hopcroft, R.R., Danielson, S.L., Collins, R.E., Cushing, D. Changes in diversity and species composition across multiple assemblages in the northeast Chukchi Sea during two contrasting years are consistent with borealization. Oceanography (In Press).</em></p>
Data and code related to the paper: "Integrated stretchable pneumatic strain gauges for electronics-free soft robots"
<p>This folder contains the raw data and Matlab scripts to reproduce the plots and supplementary movies for the paper:</p> <p>Anastasia Koivikko, Vilma Lampinen, Mika Pihlajamäki, Kyriacos Yiannacou, Vipul Sharma & Veikko Sariola, "Integrated Stretchable Pneumatic Strain Gauges for Electronics-Free Soft Robots", Communications Engineering, 1, 14 (2022).</p> <p><a href="https://doi.org/10.1038/s44172-022-00015-6">Link to the paper</a>.</p> <p>The scripts were tested on Matlab R2021a on Windows.</p> <p>Generally speaking, there is a folder containing the plotting scripts for each figure. In most cases, the folder contains scripts named <strong>plot<...>.m</strong> that recreate the actual plots. Some folders also have a scripts <strong>analyze<...>.m</strong> to analyze the data; these need to be run before the actual plotting.</p> <p>For more details, please see the paper.</p>
Data and code related to the paper: "Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation"
<p>This archive contains the raw data and Matlab scripts to reproduce the plots and supplementary movies for the paper:</p> <p>Kyriacos Yiannacou, Vipul Sharma and Veikko Sariola, "Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation", <em>Langmuir</em> 2022, 38, 38, 11557–11564.</p> <p><a href="https://doi.org/10.1021/acs.langmuir.2c01061">Link to the paper</a>.</p> <p>The scripts were tested on Matlab R2021a on Windows.</p> <p>The acoustofluidic controller software is the same as in our previous paper and is archived <a href="https://doi.org/10.5281/zenodo.4593021">here</a>.</p> <p>Generally speaking, there is a folder containing the plotting scripts for each figure(s) and/or movie(s). Within each folder, the raw data files are under the folder `data/`. Once ran, the scripts produce another folder called `output/`, to which they place the created plots and movies. Most folder contain a script name `plot_*.m` that makes the figure(s) and `video_*.m` that generates the video(s). You will need `ffmpeg` installed to convert the serial images into a video.<br> </p>
Image Databases for Computer Vision Coded for Subject Traceability
<p>This document consists of the corpus of image databases examined for traceability of dataset subjects as published in:</p> <p>Morgan Klaus Scheuerman, Katy Weathington, Tarun Mugunthan, Emily Denton, and Casey Fiesler. 2023. From Human to Data to Dataset: Mapping the Traceability of Human Subjects in Computer Vision Datasets. Proc. ACM Hum.-Comput. Interact. 7, CSCW1, Article 55 (April 2023), 33 pages. https://doi.org/10.1145/3579488</p>
Data and code for figures: Kinetic Inductive Electromechanical Transduction for Nanoscale Force Sensing
<p>This directory contains the datasets and code (if applicable) for generating the figures in the research article "Kinetic Inductive Electromechanical Transduction for Nanoscale Force Sensing", Physical Review Applied 20, 024022 (2023).</p>
Data and code from "Water availability is a stronger driver of soil microbial processing of organic nitrogen than tree species composition"
<p>#### Data description<br> Data from large scale, long-term tree diversity experiment in southwestern France (<a href="https://sites.google.com/view/orpheeexperiment/home">ORPHEE</a>), additionally manipulating water contraint. Variables presented are soil nitrogen cycling rates measured using isotope pool dilutions.</p> <p>Companion paper is found here:</p> <p>Maxwell TL, Augusto L, Tian Y, Wanek W & Fanin N (2023). Water availability is a stronger driver of soil microbial processing of organic nitrogen than tree species composition. <em>European Journal of Soil Science</em>. <a href="https://doi.org/10.1111/ejss.13350">https://doi.org/10.1111/ejss.13350</a></p> <p>#### Metadata<br> Soil sampling: July 2020<br> Maxwell_ShortComm_Data.csv data description</p> <p>ID: unique identifier per sample<br> Block: numbered 1-6. Blocks 1,3,6 are control (unirrigated), Blocks, 2,4,5 are irrigated<br> Plot: numbered plot according to the ORPHEE design. Plot 1 = BP, Plot 5 = PP, Plot 9 = BP_PP<br> Espece: species ID. BP = pure birch (<em>Betula pendula</em>), PP = pure pine (<em>Pinus pinaster</em>), BP_PP (50% mixed birch-pine)<br> Rep: sample replicate, 3 replicates per plot<br> Sample name: long unique identifier per sample. Concatenation of Block, Plot, and Espece<br> PD: gross protein depolymerization rates (micrograms nitrogen per grams dry soil per day = µg N g-1 d-1)<br> AAU: gross free amino acid uptake rates (µg N g-1 d-1)<br> Cmicrobial_ug_g: microbial biomass carbon (µg C g-1)<br> Nmicrobial_ug_g: microbial biomass nitrogen (µg N g-1)<br> MRT_FAA_hrs: mean residence time of free amino acids (hours)<br> FAA_ugN_g: free amino acids (µg N g-1)<br> Moisture_percent: soil moisture percent (%)<br> N_nonfumige_ug_g: nitrogen from non fumigated soils, i.e. extractable N (µg N g-1)<br> C_nonfumige_ug_g: nitrogen from non fumigated soils, i.e. extractableC (µg C g-1)</p>
Gaussian Process Model and Sensor Placement for Detroit Green Infrastructure: Datasets and Code
<ol> <li><strong>code.zip: </strong>Zip folder containing a folder titled "code" which holds: <ol> <li>csv file titled "MonitoredRainGardens.csv" containing the 14 monitored green infrastructure (GI) sites with their design and physiographic features;</li> <li>csv file titled "storm_constants.csv" which contain the computed decay constants for every storm in every GI during the measurement period;</li> <li>csv file titled "newGIsites_AllData.csv" which contain the other 130 GI sites in Detroit and their design and physiographic features;</li> <li>csv file titled "Detroit_Data_MeanDesignFeatures.csv" which contain the design and physiographic features for all of Detroit;</li> <li>Jupyter notebook titled "GI_GP_SensorPlacement.ipynb" which provides the code for training the GP models and displaying the sensor placement results;</li> <li>a folder titled "MATLAB" which contains the following: <ol> <li>folder titled "SFO" which contains the SFO toolbox for the sensor placement work</li> <li>file titled "sensor_placement.mlx" that contains the code for the sensor placement work</li> <li>several .mat files created in Python for importing into Matlab for the sensor placement work: "constants_sigma.mat", "constants_coords.mat", "GInew_sigma.mat", "GInew_coords.mat", and "R1_sensor.mat" through "R6_sensor.mat"</li> <li>several .mat files created in Matalb for importing into Python for visualizing the results: "MI_DETselectedGI.mat" and "DETselectedGI.mat"</li> </ol> </li> </ol> </li> </ol>
Netanyahu's and Abbas' speeches at the UNGA 2010-19, coded using a populism framework
<p>Databaset with speeches of Benjamin Netanyahu and Mahmoud Abbas before the United Nations General Assembly (UNGA) (2010-2019) coded using MAXQDA following populism multidimensional comparative framework by Olivas Osuna (2021).</p> <p>In this database syntactic units —sentences—are individually coded whenever they match the criteria corresponding to any of populism/anti-populism, re-bordering/de-bordering, religion and securitisation codes defined previously. See Olivas Osuna and Rama (2021) and Olivas Osuna (2022) for reference to the methodology and previous empirical applications.</p>
Dataset and supplemental codes for : "Referenceless characterisation of complex media using physics-informed neural networks"
<p>Dataset and associated supplemental codes for : "Referenceless characterisation of complex media using physics-informed neural networks".</p>
Nabro 3D velocity model produced by the FMTOMO code
<p>These files relate to "Seismic tomography of Nabro caldera, Eritrea: insights into the magmatic and hydrothermal systems of a recently erupted volcano" by Gauntlett et al., 2023. </p> <p>Original seismic waveforms are from the Nabro Urgency Array (Hammond et al., 2011; <a href="https://doi.org/10.7914/SN/4H_2011">https://doi.org/10.7914/SN/4H_2011</a>), which is publicly available through IRIS Data Services (<a href="http://service.iris.edu/fdsnws/dataselect/1/">http://service.iris.edu/fdsnws/dataselect/1/</a>). See Hammond et al. (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JB021910#jgrb55017-bib-0025">2011</a>) for further details on waveform data access and availability. Sources were originally located by Lapins et al. (<a href="https://doi.org/10.1029/2021JB021910">2021</a>), and the full catalogue is archived <a href="https://zenodo.org/record/7669717#.ZAXtfuymP0s">here</a> (Lapins, 2022). </p> <p>The FMTOMO package is freely available to download at<a href="http://rses.anu.edu.au/~nick/fmtomo.html"> http://rses.anu.edu.au/~nick/fmtomo.html</a>. </p> <p>This repository contains three 3D velocity models:</p> <ol> <li>vp_model.out</li> <li>vs_model.out</li> <li>vpvs_model.out</li> </ol> <p>These plain text files comprise the output of the FMTOMO tomography algorithm, which inverts for 3D P-wave velocity and S-wave velocity Vp and Vs) structure and Vp/Vs ratio for a grid centred around Nabro volcano. The velocity is given in km/s for Vp and Vs, and the values are dimensionless for Vp/Vs ratio.</p> <p>The grid is specified in the first four lines of the file:</p> <ul> <li>Line 1: for these data, this line will always hold the values `1' and `1'. </li> <li>Line 2: the number of grid nodes in radius (depth), latitude and longitude. </li> <li>Line 3: the radial (depth) node spacing in km, latitude spacing in radians, longitude spacing in radians.</li> <li>Line 4: the grid origin radius (km), latitude (radians) and longitude (radians).</li> </ul> <p>Each node of the grid has a P-wave, S-wave and Vp/Vs ratio associated with it, which is specified in lines 5 onward.</p> <ul> <li>Line 5 - 55229: Value of Vp, Vs or Vp/Vs (given by file name) at each node.</li> </ul> <p>The values loop over the grid, with longitude varying first, and radius last. The first node is at the grid origin. The second node is at the grid origin, plus the grid spacing in longitude. This continues for the `nlon` longitude nodes. The `nlon+1`the point is then at the grid origin, plus the latitude spacing; and so on.</p> <p>If there are `nr` radial nodes, `nlat` latitude nodes and `nlon` longitude nodes, then the following pseudocode shows how to read lines 5 forwards using the imaginary function `readline`, which reads a single real value from a plain text file and moves to the next line:</p> <p>```</p> <p># Comment: have already read the first four lines</p> <p>for ir in 1:nr:</p> <p>for ilat in 1:nlat:</p> <p>for ilon in 1:nlon:</p> <p>grid[ir,ilat,ilon] = readline(file_handle)</p> <p>```</p> <p>The repository also contains the event catalogue, with hypocenter locations after relocation by the FMTOMO code:</p> <p>4. event_catalogue.csv</p> <p>The columns are depth in km (negative values indicate depths below sea level, positive values indicate depths above sea level), latitude in degrees, longitude in degrees, depth error (km), latitude error (km), longitude error (km). </p>
Python code for "Evolutionary epidemiology consequences of trait-dependent control of heterogeneous parasites"
<p>The file contains the Python code used to run the agent-based simulation of the selection-mutation model presented in "Evolutionary epidemiology consequences of trait-dependent control of heterogeneous parasites"</p>
Detecting small changes in tropical forests from space... data and code for thesis chapter 4
<p>SAR and UAV-LiDAR data used in chapter 4 of my thesis <em>Detecting small changes in tropical forests from space: experiments using synthetic aperture radar. </em>This content has also been submitted for peer review in Frontiers in Remote Sensing.</p> <p>DEM_timeseries_3m contains phase height and coherence from TanDEM-X InSAR high-resolution spolight images, processed by Jose-Luis Bueso-Bello at DLR. NetCDF format, dimensions latitude, longitude, time.</p> <p>TDX_descending_intensity contains intensity from the same TanDEM-X time series, covering an area of the Madre de Dios region in Peru. These data were processed by Harry Carstairs using ESA's SNAP software.</p> <p>UAV_change_1m_mask is a raster showing the change in canopy height at the study site between June 2019 and July 2021, according to two UAV LiDAR campaigns, with 1m pixels, and with areas with low point density masked out.</p> <p>CODE.zip contains python scripts and notebooks used to collate the data, create change detection metrics, develop SAR models of canopy height, and produce the figures.</p> <p>Funded by European Research Council (ERC) grant to the Tropical Forest Degradation Experiment (FODEX).</p>
Datasets and codes for the peer review article "Human and natural impacts on the U.S. freshwater salinization and alkalinization: A machine learning approach"
<p>Ongoing salinization and alkalinization in U.S. rivers have been attributed to inputs of road salt and effects of human-accelerated weathering in previous studies. Salinization poses a severe threat to human and ecosystem health, while human derived alkalinization implies increasing uncertainty in the dynamics of terrestrial sequestration of atmospheric carbon dioxide. A mechanistic understanding of whether and how human activities accelerate weathering and contribute to the geochemical changes in U.S. rivers is lacking. To address this uncertainty, we compiled dissolved sodium (salinity proxy) and alkalinity values along with 32 watershed properties ranging from hydrology, climate, geomorphology, geology, soil chemistry, land use, and land cover for 226 river monitoring sites across the coterminous U.S. Using these data, we built two machine-learning models to predict monthly-aggregated sodium and alkalinity fluxes at these sites. The sodium-prediction model detected human activities (represented by population density and impervious surface area) as major contributors to the salinity of U.S. rivers. In contrast, the alkalinity-prediction model identified natural processes as predominantly contributing to variation in riverine alkalinity flux, including runoff, carbonate sediment or siliciclastic sediment, soil pH and soil moisture. Unlike prior studies, our analysis suggests that the alkalinization in U.S. rivers is largely governed by local climatic and hydrogeological conditions.</p>
Figure data and code used in Technical comment on "Fairness considerations in global mitigation investments"
<p>The package contains the data and code to create the figure in the associated technical comment in Science published at <a href="https://www.science.org/doi/10.1126/science.adg5893">https://www.science.org/doi/10.1126/science.adg5893</a></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.