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6,608 results for “coding”
R Code and Images for Developing a Spatial Concordance Coefficient at Harvard Forest 2010
Concordance correlation coefficients have been developed in a variety of different contexts. This problem has been widely addressed in a non-spatial context, but here we consider a coefficient that for a fixed spatial lag allows the comparison of two spatial sequences (e.g., images). We define a spatial concordance coefficient for second-order stationary processes.
Size scalability of Monte Carlo simulations applied to oxidized polypyrrole systems: Data and Codes
<p>This work generalizes our recently proposed coarse grained force field (CGFF) for halogen oxidized PPy in the condensed phases and introduces a novel implementation of the Nettropolis Monte Carlo (MMC) simulation based on the CGFF that enables simulations of polymer systems with more than<br>100000 particles. The MMC implementation utilizes a combination of CPU and GPUs and exploits a numerical approximation based on polynomial piecewise interpolation for the calculation of the CGFF pairwise additive terms. Our simulations evidence that the oxidized PPy thermodynamic and structural properties are consistent as the system size is scaled up. Predicted properties include density, enthalpy, potential energy, heat capacity, coefficient of thermal expansion, caloric curve, glass transition temperature range, compressibility, bulk modulus, radial distribution functions, and polymer chain characteristics.</p>
Data and Code in support of Caterpillar abundance in a northern hardwood forest: exogenous effects, endogenous feedbacks, and multidecadal trends.
In this study, we analyzed caterpillar abundance and biomass measured over 50 years (1970 - 2021) in the Hubbard Brook Experimental Forest, New Hampshire, USA. We tested mechanisms for determination of caterpillar abundance that included weather, host plant quality, and predator abundance. This dataset includes data, R code, and spatial files supporting this study. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Shared neural codes for visual and semantic information about familiar faces in a common representational space
Open the record for dataset details and reuse information.
Datafile for HERMES code
<p>This repository includes the data files for the code <a href="https://github.com/cosmicrays/hermes">HERMES</a>.</p> <p>The code is described in <a href="https://ui.adsabs.harvard.edu/abs/2021A%26A...653A..18D/abstract">Dundovic et al., 2021, A&A, 653, A18, arXiv:2105.13165</a> </p>
Data and Code Accompanying "Retrieving and Analyzing Taste Colexifications from Lexibank"
<p>Data and Code accompanying the study "Retrieving and analyzing taste colexifications from Lexibank" by Olena Shcherbakova and Johann-Mattis List (see <a href="https://calc.hypotheses.org/6398">https://calc.hypotheses.org/6398</a>).</p><p>Information on how to run the code can be found in the study itself.</p>
Dataset / Code: Targeted protein degradation in mycobacteria uncovers antibacterial effects and potentiates antibiotic efficacy
<p><strong>Targeted protein degradation in mycobacteria uncovers antibacterial effects and potentiates antibiotic efficacy</strong></p> <p><strong> </strong></p> <p>Harim I. Won<sup>1,#</sup>, Samuel Zinga<sup>1,#</sup>, Olga Kandror<sup>1</sup>, Tatos Akopian<sup>1</sup>, Ian D. Wolf<sup>1</sup>, Jessica T.P. Schweber<sup>1</sup>, Ernst W. Schmid<sup>2</sup>, Michael C. Chao<sup>1</sup>, Maya Waldor<sup>1</sup>, Eric J. Rubin<sup>1,*</sup>, Junhao Zhu<sup>1,3,*</sup></p> <p><strong> </strong></p> <p><sup>1</sup>Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA.</p> <p><sup>2</sup>Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Blavatnik Institute, Boston, Massachusetts 02115, USA.</p> <p><sup>3</sup>CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.</p> <p><sup>#</sup>These authors contributed equally to this work.</p> <p>*Corresponding authors: <a href="mailto:zhujh@im.ac.cn">zhujh@im.ac.cn</a> (J.Z.), <a href="mailto:erubin@hsph.harvard.edu">erubin@hsph.harvard.edu</a> (E. J. R.)</p> <p><strong> </strong></p> <p><strong>Abstract</strong></p> <p>Proteolysis-targeting chimeras (PROTACs) represent a new therapeutic modality involving selectively directing disease-causing proteins for degradation through proteolytic systems. Our ability to exploit targeted protein degradation (TPD) for antibiotic development remains nascent due to our limited understanding of which bacterial proteins are amenable to a TPD strategy. Here, we use a genetic system to model chemically-induced proximity and degradation to screen essential proteins in <em>Mycobacterium smegmatis </em>(<em>Msm</em>)<em>, </em>a model for the human pathogen <em>M. tuberculosis </em>(<em>Mtb</em>). By integrating experimental screening of 72 protein candidates and machine learning, we find that drug-induced proximity to the bacterial ClpC1P1P2 proteolytic complex leads to the degradation of many endogenous proteins, especially those with disordered termini. Additionally, TPD of essential <em>Msm </em>proteins inhibits bacterial growth and potentiates the effects of existing antimicrobial compounds. Together, our results provide biological principles to select and evaluate attractive targets for future <em>Mtb</em> PROTAC development, as both standalone antibiotics and potentiators of existing antibiotic efficacy.</p> <p> </p>
Raw data mzXML and MATLAB code for Variation in chemical composition of dissolved organic matter during the winter to spring transition in the northern Barents Sea
<p>MATLAB code and raw data mzXML for Variation in chemical composition of dissolved organic matter during the winter to spring transition in the northern Barents Sea.</p> <p>Seawater samples were collected during three distinct periods: early winter (December 2019), late winter (March 2021), and spring (May 2021). The sampling transect extended from the northern Barents Sea into the Nansen Basin (76°N – 83°N) as part of <em>The Nansen Legacy</em> project (Research Council of Norway, RCN #276730). The molecular composition of dissolved organic matter (DOM) was analyzed using an Orbitrap mass spectrometer.</p>
Data and code of the article: "Early Warning Signals of the Termination of the African Humid Period(s)"
<p>Data and MATLAB Code of the article Trauth, M.H., Asrat, A., Fischer, M.L., Hopcroft, P.O., Foerster, V., Kaboth-Bahr, S., Kindermann, K., Lamb, H.F., Marwan, N., Maslin, M.A., Schaebitz, F., Valdes, P.J. (2024) Early Warning Signals of the Termination of the African Humid Period(s), Nature Communications, https://doi.org/10.1038/s41467-024-47921-1. The individual directories contain the data and the MATLAB code used to generate Fig. 1 and 2 and Supplementary Fig. 1 to 7 published with the article.</p>
Data and code for: Rachel A Reeb, J Mason Heberling, & Sara E Kuebbing (2024). Cross-continental comparison of plant reproductive phenology shows high intraspecific variation in temperature sensitivity. AoB PLANTS, plae058
<p>Data and Analysis Code for: </p> <p>Rachel A Reeb, J Mason Heberling, Sara E Kuebbing (2024). Cross-continental comparison of plant reproductive phenology shows high intraspecific variation in temperature sensitivity. <em>AoB PLANTS</em>, plae058. <a href="https://doi.org/10.1093/aobpla/plae058">https://doi.org/10.1093/aobpla/plae058</a></p> <p>Includes two R markdown files ("climate_data_extraction_code.rmd" is the script for data extraction and cleaning and "Data_Analysis_V2.rmd" is the analysis script), the associated datasets (in .csv format), and the metadata file ("readme.txt").</p>
Code and data set for data analysis published as manuscript "Bacttle: a microbiology educational board game for lay public and schools"
<p>Code that processed raw data and plots the figures of the manuscript "Bacttle: a microbiology educational board game for lay public and schools"</p> <p>Below is a table with the original survey questions. The ID corresponds to the column displayed on the data set. When letters are followed by a number (1 or 2), it means that the question was answered before playing the game (1) and after playing the game (2).</p> <table> <tbody> <tr> <td> <p><em>ID<sup>1</sup></em></p> </td> <td> <p><em>Question text</em></p> </td> <td> <p><em>Possible answers<sup>2</sup></em></p> </td> </tr> <tr> <td> <p><em>A</em></p> </td> <td> <p>How old are you?</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>B</em></p> </td> <td> <p>Do you know what a bacterium is?</p> </td> <td> <p>y/n</p> </td> </tr> <tr> <td> <p><em>C</em></p> </td> <td> <p>Do you know what a bacterial capsule is?</p> </td> <td> <p>y/n</p> </td> </tr> <tr> <td> <p><em>D</em></p> </td> <td> <p>Do bacteria have tools to harm each other?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>E</em></p> </td> <td> <p>Do bacteria reproduce at the same pace?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>F</em></p> </td> <td> <p>What is sporulation?</p> </td> <td> <p>A resistant state that some bacteria can achieve under unfavorable conditions.</p> </td> </tr> <tr> <td> <p>The release of toxins by bacteria.</p> </td> </tr> <tr> <td> <p>idk</p> </td> </tr> <tr> <td> <p><em>G</em></p> </td> <td> <p>What are flagella used for?</p> </td> <td> <p>Sticking to surfaces.</p> </td> </tr> <tr> <td> <p>Motility in liquid environments.</p> </td> </tr> <tr> <td> <p>idk</p> </td> </tr> <tr> <td> <p><em>H</em></p> </td> <td> <p>What does it mean to be lithotrophic?</p> </td> <td> <p>A bacterium can get energy from minerals.</p> </td> </tr> <tr> <td> <p>A bacterium can get energy from the sunlight.</p> </td> </tr> <tr> <td> <p>idk</p> </td> </tr> <tr> <td> <p><em>I</em></p> </td> <td> <p>Can bacteria be infected by viruses?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>J</em></p> </td> <td> <p>Are all bacteria harmful for humans?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>K</em></p> </td> <td> <p>How many bacteria are in a coffee spoon of yoghurt?</p> </td> <td> <p>Millions</p> </td> </tr> <tr> <td> <p>Hundreds</p> </td> </tr> <tr> <td> <p>idk</p> </td> </tr> <tr> <td> <p><em>L</em></p> </td> <td> <p>How easy did you find the gameplay?</p> </td> <td> <p>VE/E/A/D/VD</p> </td> </tr> <tr> <td> <p><em>M</em></p> </td> <td> <p>Did you find the card content easy to understand?</p> </td> <td> <p>VE/E/A/D/VD</p> </td> </tr> <tr> <td> <p><em>N</em></p> </td> <td> <p>Did you like the setup of the game?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>O</em></p> </td> <td> <p>Would you like to play this game again?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>P</em></p> </td> <td> <p>What can we improve?</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> <p>1) Question A categorizes the player’s age; B and C assess the initial level of knowledge in microbiology (none -both questions are answered negatively-, basic -player knows what a bacterium is but not a bacterial capsule-, or advanced -both answers are positive-); questions D-I score knowledge acquisition; J and K are control questions; L-O evaluate the appreciation of the game; and P is an optional free text-entry answer for additional feedback. <br>2) y= yes, n=no, idk=I don’t know, VE=very easy, E=easy, A=adequate, D=difficult, VD=very difficult.</p>
Improving the Developer Experience with a Low-Code ProcessModelling Language: Companion site
<p>This companion site contains additional data to complement the paper:</p> <p><em><strong>Henriques, H., Lourenço, H., Amaral, V., and Goulão, M. (2018). Improving the developer experience with a low-code process </strong></em><em><strong>modelling</strong></em><em><strong> language. In ACM/IEEE 21st International Conference on Model Driven Engineering Languages and Systems (MODELS 2018), Copenhagen, Denmark. ACM. https://doi.org/10.1145/3239372.3239387</strong></em></p> <p><strong>Abstract</strong></p> <p><strong>Context</strong><strong>: </strong>The OutSystems Platform is a development environment composed of several DSLs, used to specify, quickly build and validate web and mobile applications. The DSLs allow users to model different perspectives such as interfaces and data models, define custom business logic and construct process models.</p> <p><strong>Problem</strong><strong>: </strong>TheDSL for process modelling (Business Process Technology (BPT)), has a low adoption rate and is perceived as having usability problems hampering its adoption. This is problematic given the language maintenance costs.</p> <p><strong>Method:</strong> We used a combination of interviews, a critical review of BPT using the “Physics of Notation” and empirical evaluations of BPT using the System Usability Scale (SUS)and the NASA Task Load indeX (TLX), to develop a new version ofBPT, taking these inputs and Outsystems’ engineers culture into account.</p> <p><strong>Results: </strong>Evaluations conducted with 25 professional soft-ware engineers showed an increase of the semantic transparency on the new version, from 31% to 69%, an increase in the correctness of responses, from 51% to 89%, an increase in the SUS score, from 42.25 to 64.78, and a decrease of the TLX score, from 36.50 to 20.78. These differences were statistically significant.</p> <p><strong>Conclusions:</strong> These results suggest the new version of BPT significantly improved the developer experience of the previous version. The end users background with OutSystems had a relevant impact on the final concrete syntax choices and achieved usability indicators.</p> <p> </p> <p><strong>Contents</strong></p> <p>This companion site provides a permanent link for additional data to the supported paper.</p> <p>This repository includes:</p> <ul> <li>Surveys and Questionnaires used in the evaluation reported in the paper <ul> <li>Survey on OutSystems BPT notations (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/survey.pdf">survey.pdf</a>)</li> <li>Prototype Symbol Set Questionnaire (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/PrototypeSymbolSetQuestionnaire.pdf">PrototypeSymbolSetQuestionnaire.pdf</a>)</li> <li>Original BPT Evaluation (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/languages.png">languages.png</a>)</li> <li>Usability Evaluation (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/sus.png">sus.png</a>)</li> <li>Cognitive Effort Evaluation (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/tlx.png">tlx.png</a>)</li> <li>Testing environment screenshot (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/Testing%20Environment%20Screenshot.png">Testing Environment Screenshot</a>)</li> </ul> </li> <li>Statistics <ul> <li>SUS and NASA TLX <ul> <li>Descriptive statistics (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/SUSTLXDescriptiveStats.pdf">SUSTLXDescriptiveStats.pdf</a>)</li> <li>Normality tests (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/SUSTLXNormalityTests.pdf">SUSTLXNormality.pdf</a>)</li> <li>Correlation test (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/SUSTLXCorrelation.pdf">SUSTLXCorrelation.pdf</a>)</li> <li>Scatterplot (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/SUSTLXScatterPlot.pdf">SUSTLXScatterplot.pdf</a>)</li> </ul> </li> </ul> </li> </ul> <p> </p>
Fine Fuel Moisture Code - ERA-Interim
<p>The Fine Fuel Moisture Code (FFMC) is a numeric rating of the moisture content of litter and other cured fine fuels. This code is an indicator of the relative ease of ignition and the flammability of fine fuel.</p> <p>This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019; Di Giuseppe et al., 2016). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately. </p> <p>Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md). </p> <p>This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018). </p> <p>Details: </p> <ul> <li> <p>File format: netcdf4 </p> </li> <li> <p>Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326). </p> </li> <li> <p>Longitude range: [-180, +180] </p> </li> <li> <p>Latitude range: [-90, +90] </p> </li> <li> <p>Temporal resolution: 1 day </p> </li> </ul> <ul> <li> <p>Spatial resolution: 0.7 degrees (~80 Km) </p> </li> <li> <p>Spatial coverage: Global </p> </li> <li> <p>Time span: from 1980-01-01 to 2018-12-31 </p> </li> </ul>
Duff Moisture Code - ERA-Interim
<p>The Duff Moisture Code (DMC) is a numeric rating of the average moisture content of loosely compacted organic layers of moderate depth. This code gives an indication of fuel consumption in moderate duff layers and medium-size woody material.</p> <p>This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019; Di Giuseppe et al., 2016). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately. </p> <p>Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md). </p> <p>This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018). </p> <p>Details: </p> <ul> <li> <p>File format: netcdf4 </p> </li> <li> <p>Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326). </p> </li> <li> <p>Longitude range: [-180, +180] </p> </li> <li> <p>Latitude range: [-90, +90] </p> </li> <li> <p>Temporal resolution: 1 day </p> </li> </ul> <ul> <li> <p>Spatial resolution: 0.7 degrees (~80 Km) </p> </li> <li> <p>Spatial coverage: Global </p> </li> <li> <p>Time span: from 1980-01-01 to 2018-12-31 </p> </li> </ul>
Drought Code - ERA-Interim
<p>The Drought Code (DC) is a numeric rating of the average moisture content of deep, compact organic layers. This code is a useful indicator of seasonal drought effects on forest fuels and the amount of smoldering in deep duff layers and large logs.</p> <p>This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019; Di Giuseppe et al., 2016). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately. </p> <p>Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md). </p> <p>This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018). </p> <p>Details: </p> <ul> <li> <p>File format: netcdf4 </p> </li> <li> <p>Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326). </p> </li> <li> <p>Longitude range: [-180, +180] </p> </li> <li> <p>Latitude range: [-90, +90] </p> </li> <li> <p>Temporal resolution: 1 day </p> </li> </ul> <ul> <li> <p>Spatial resolution: 0.7 degrees (~80 Km) </p> </li> <li> <p>Spatial coverage: Global </p> </li> <li> <p>Time span: from 1980-01-01 to 2018-12-31 </p> </li> </ul>
Incriminations in the inquisition register of Bologna (1291-1310): network data and code
<p>Cross-sectional (synchronic) projection of network data on incriminations (nominations of people in the criminal context of heresy trials) in the medieval inquisition register of Bologna, 1291–1310 in TSV format (tabulator-separated values), and R code for the article: Zbíral, David, Katia Riccardo, Tomáš Hampejs, and Zoltán Brys. ‘Gender, Kinship, and Other Social Predictors of Incrimination in the Inquisition Register of Bologna (1291–1310): Results from an Exponential Random Graph Model’. PLOS One 20, no. 2 (11 February 2025): e0315467. https://doi.org/10.1371/journal.pone.0315467.</p> <p>The data and analysis are described in the related article.</p>
Data and code for: Little directional change in the timing of Arctic spring phenology over the past twenty-five years
<p>Data and code accompanying the publication: Little directional change in the timing of Arctic spring phenology over the past twenty-five years.</p> <p>This resource contains 1. R-scripts to calculate yearly phenologies from raw temporally explicit flowerin, arthropod observation and bird nesting data from Zackenberg. The raw data is openly accessible through the Greenland Ecosystem Monitoring database (https://data.g-e-m.dk/), as well as an R-script to carry out most of the analyses presented in the publication. To facilitate the use of the analysis script, pre-produced annual phenologies of focal taxa are included as csv-tables.</p>
Photosynthetic quotients in aquatic ecosystems: data and code supporting Trentman et al. 2023 manuscript in L&O Letters
This study provides a summary of the mismatch between our current knowledge and the application of the photosynthetic quotient (PQ). We use data from the Upper Clark Fork River (UCFR) as a case study example of how the PQ may vary in space and time based on environmental conditions. Surface water sample measurements of dissolved oxygen (DO), temperature (T), nutrients (NO3-N, NH4-N, SRP), and several metabolism indicators are represented in this data product. Figures represent data from two sites on the mainstem of the Upper Clark Fork River (UCFR) over a roughly two-year period, from 2019 to 2021. Some measurements are derived from existing data products or manuscripts, including DOT (Valett, et al., 2023); nutrients (H. M. Valett, Dec. 2, 2022, pers. comm); air pressure (Deer Lodge Weather Station, 2023); underlying data for Trentman et al. (2023) Figure 2 and Figure 4e and 4f (via Burris, 1981); and SI-Figure2 USGS gage data (USGS, 2023). Products unique to this data product include metabolism data (Trentman, et al., 2023 (Figure 5)), chamber data supporting Trentman, et al., (2023) Figure 6, and code simulations/data. All analytes and variables are documented in the project data dictionary. For details on data collection methods, see the methods section, the manuscript, and/or referenced data products.
NEON soil inorganic nitrogen measurements 2017-2020, derived data and code for Earth's Future manuscript
Nitrogen (N) is a key limiting nutrient in terrestrial ecosystems, but there remain critical gaps in our ability to predict and model controls on soil N cycling. This may be in part due to lack of standardized sampling across broad spatial-temporal scales. In a paper submitted for publication in Earth's future, we introduce a continentally distributed, publicly available dataset collected by the National Ecological Observatory Network (NEON) that can help fill these gaps. To overcome methodological challenges and generate a standardized dataset, we produced a derived data version of soil inorganic N pools and net N transformation rate tables, which accounts for nitrite contamination in blanks. This derived dataset is then used to evaluate sources of variation within the NEON sampling design with mixed effects models, and we also compare measured net N mineralization to simulated fluxes from the Community Earth System Model 2 (CESM2).
Code for Random Forest models that predict pharmaceutical and water chemistry measurements in Baltimore Ecosystem Study streams
This file contains code to model the relationship between the water chemistry measurements and discharge measured as part of BES routine sampling and the pharmaceuticals measured in WY 2018. We use Random Forest models to predict 1) total (i.e., summed) concentration of the pharmaceuticals for which we screened, 2) total nutrient concentrations (TN & TP), 3) whether or not the antibiotic trimethoprim was detected in a given sample, and 4) whether or not nitrate and TP were above or below environmentally-relevant threshold concentrations. We also use RF models to predict N and P concentrations over a longer period, in order to compare models for nutrients to pharma. Code and analyses here rely on data processed in the file "BESPharma_WY2018.Rmd", published on EDI (doi:10.6073/pasta/610cb67fcbc8982c2af8ed946dce8ea5) and BES water chemistry data published on EDI (doi:10.6073/pasta/ce7f30e6013e003bfe28c5fd7d4aed23 )
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.