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24 results for “World Of Code”
# Replication code and data for: Tracking green space along streets of world cities
<p># Replication code and data for: Tracking green space along streets of world cities<br>Falchetta, G., & Hammad, A. T. (2025). Tracking green space along streets of world cities. Environmental Research: Infrastructure and Sustainability. https://doi.org/10.1088/2634-4505/add9c4 </p> <p>The file "gvi_358cities_2016_2023_yearly_falchetta_hammad.csv" contains<strong> output data</strong>, reporting sampling-point level data on the yearly (2016-2023) values of the Green View Index for the 190 cities covered in the paper AND an additional number of world cities (for a total of 358 cities). The "README_gvi_358cities_2016_2023_yearly_falchetta_hammad.txt" file contains a dictionary of each column name and units. </p> <p>____<br><br></p> <p>To replicate the analysis, the results, and the figures of the paper:</p> <ul> <li>Download input data from this Zenodo repository and code from Github https://github.com/giacfalk/urban_green_space_mapping_and_tracking</li> <li><em>*Optional data extraction steps* </em>(processed output data are already available in the Zenodo repository):<br> <ul> <li>Adjust your working directory</li> <li>Run [lines 4-11] of workflow/sourcer.R</li> <li>Run the Javascript scripts written by the string_generator_training.R and string_generator_prediction.R files in Google Earth Engine (https://code.earthengine.google.com) and complete the export to Drive tasks to generate the output .csv files</li> </ul> </li> <li>Run workflow/sourcer.R [lines 15-46] to train the ML model and make predictions (including figures and tables replication)</li> </ul> <div> <div> <div> </div> <div> <div> <div> </div> <div> <p dir="auto"> </p> <p dir="auto"> </p> </div> </div> </div> </div> </div> <div> <div> <div> </div> <div> <div> <div> </div> <div> <p dir="auto"> </p> <p dir="auto"> </p> </div> </div> </div> </div> </div>
Gridded world country codes on longitude-latitude grid
<p>This netCDF file provides ISO country codes at 0.1 degrees spatial resolution. Oceans and undefined territories are identified with a fictional ISO code. The situation reported may not include more recent (post-2000) historical changes.</p> <p>Based on an elaboration of the `countries` software tool, available on github (see <a href="https://github.com/fserva/countries">countries fork</a>).</p> <p>The work is provided 'as is', without guarantees or conditions of any kind.</p>
Source data and code for: Existing fossil fuel extraction would warm the world beyond 1.5°C
<p>Source data and code for the study, "Existing fossil fuel extraction would warm the world beyond 1.5°C." Datasets 1-4 include mine-level data collected for China (Dataset 1), India (Dataset 2), and five other countries (Dataset 3) that are among the world's top nine coal producers - the United States, Indonesia, Australia, South Africa, and Poland. Dataset 4 includes global and country-level output data from the 1,000-run Monte Carlo simulation. <Committed_Reserves_Monte_Carlo_Input_Data.zip> includes data and code to replicate the Monte Carlo simulation.</p>
Data and analysis code for: Global protected areas seem insufficient to safeguard half of the world's mammals from human-induced extinction
<div> <p class="normal">Protected areas (PAs) are a cornerstone of global conservation and central to international plans to minimize global extinctions. During the coming century, global ecosystem destruction and fragmentation associated with increased human population and economic activity could make the <span class="PI"></span>long-term<span class="PI"></span> survival of most terrestrial vertebrates even more dependent on PAs. However, the capacity of the current global PA network to sustain species for the long term is unknown. Here, we explore this question for all <span class="PI"></span>nonvolant terrestrial mammals<span class="ins cts-1"> for which we found sufficient data</span>, ∼4,000 species. We first estimate the potential population size of each such mammal species in each PA and then use three different criteria to estimate if solely the current global network of PAs might be sufficient for their <span class="PI"></span>long-term<span class="PI"></span> survival. Our analyses suggest that current PAs may fail to provide robust protection for about half the species analyzed, including most species currently listed as threatened with extinction and a third of species not currently listed as threatened. Hundreds of mammal species appear to have no viable protected populations. Underprotected species were found across all body sizes, taxonomic groups, and geographic regions. <span class="PI"></span>Large-bodied<span class="PI"></span> mammals, endemic species, and those in <span class="PI"></span>high-biodiversity<span class="PI"></span> tropical regions were particularly poorly protected by existing PAs. As<span class="ins cts-1"> new</span> international biodiversity targets are formulated, our results suggest that the global network of PAs must be <span class="PI"></span>greatly expanded and most importantly that PAs must be located in diverse regions that encompass species not currently protected and must be large enough to ensure that protected species can persist for the long term.</p> </div> <p class="kwd-group"></p>
Results and code associated with «Predictability of ecological and evolutionary dynamics in a changing world»
<p>Below you'll find results and code associated with the following article (*):</p> <p>Bozzuto, C, Ives, AR (2024): Predictability of ecological and evolutionary dynamics in a changing world. <em>Proceedings of the Royal Society B</em>, <strong>291</strong>: 20240980. https://doi.org/10.1098/rspb.2024.0980</p> <p>ABSTRACT: Ecological and evolutionary predictions are being increasingly employed to inform decision-makers confronted with intensifying pressures on biodiversity. For these efforts to effectively guide conservation actions, knowing the limit of predictability is pivotal. In this study, we provide realistic expectations for the enterprise of predicting changes in ecological and evolutionary observations through time. We begin with an intuitive explanation of predictability (the extent to which predictions are possible) employing an easy-to-use metric, predictive power <em>PP</em>(<em>t</em>). To illustrate the challenge of forecasting, we then show that among insects, birds, fishes and mammals, (i) 50% of the populations are predictable at most 1 year in advance and (ii) the median 1-year-ahead predictive power corresponds to a prediction <em>R</em><sup>2</sup> of only 20%. Predictability is not an immutable property of ecological systems. For example, different harvesting strategies can impact the predictability of exploited populations to varying degrees. Moreover, incorporating explanatory variables, accounting for time trends and considering multivariate time series can enhance predictability. To effectively address the challenge of biodiversity loss, researchers and practitioners must be aware of the information within the available data that can be used for prediction and explore efficient ways to leverage this knowledge for environmental stewardship.</p> <p>(*) previously a preprint on <em>bioRxiv</em>: https://doi.org/10.1101/2023.11.01.565089</p>
Real-world Optimization Benchmark from Vehicle Dynamics - Data and Code
<p>Data and Code of five 2D single-objective optimization problems from vehicle dynamics design for benchmarking</p> <p>Conference Paper at ECTA Real-world Optimization Benchmark from Vehicle Dynamics: Specification of Problems in 2D and Methodology for Transferring (Meta-)Optimized Algorithm Parameters</p>
Data and analysis code for: Global protected areas seem insufficient to safeguard half of the world's mammals from human-induced extinction
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FEHM source code modifications and executables for use with ocean-world gravity
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Data and code for: Influence of atmospheric nitrogen deposition on soil greenhouse gas fluxes from forests in China and the world
<p><span>Since the industrial revolution, greenhouse gas emissions (particularly CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O) caused by human activities have accelerated global climate change. To avoid catastrophic transitions in the Earth system, many countries including China have set goals to achieve “net zero emission” (or “carbon neutrality”) by mid-21<sup>st</sup> century. Forestland-related practices are among the most preferred “natural climate solutions”. However, high uncertainties remain in the greenhouse gas fluxes from forest soils, because of the limited capability to observe soil dynamics at a large spatial scale. Meanwhile, forest soil greenhouse gas fluxes are influenced by multiple anthropogenic environmental changes including enhanced atmospheric nitrogen deposition, which further complicates the interactions between forest ecosystem and the atmosphere. During the past half century, simulated nitrogen deposition (or “nitrogen addition”) experiments have been conducted in various forest sites worldwide, founding a basis for quantifying the spatially-varying responses of soil greenhouse gas flux to nitrogen deposition. </span></p> <p><span>In this research, we systematically synthesized global nitrogen addition experiment data from published literature and public databases, using which we explored the responses of the three major greenhouse gases to N input. Derived sensitivity of soil N<sub>2</sub>O emission to N deposition allowed for determining the N saturation (or limitation) status of global forests. Using process-augmented data-driven approach and random forest regression models, we estimated soil greenhouse gas budgets on regional and global levels. On the basis, we quantified the varying effects of N deposition on soil greenhouse gas fluxes in N-limited and N-saturated forests across biomes. </span><span> </span></p> <p><span>The produced global map of N-saturated forests in this research could facilitate studies on carbon and nitrogen cycles and improve forest nitrogen management. The revealed response patterns and response factors of soil greenhouse gases to N input could help improve the structure and parameters of ecosystem models. Furthermore, the localized N<sub>2</sub>O emission factors for 145 countries could be used to reduce the uncertainties in their national greenhouse gas inventories. The “process-augmented data-driven” approach could potentially bridge the gap between site-level manipulative experiments and the demand for regional greenhouse gas budgets, allowing manipulative experiments to play a more important role in global change research. </span></p>
Code and figures in support of publication comparing transient and quasi-equilibrium warmer worlds
<p>This folder contains raw figure files for a publication comparing transient and quasi-equilibrium warmer worlds.</p>
Data and Code for replicating the manuscript submitted to World Development
<p>Data and Code for replicating the paper "A Data-Driven Approach Improves Food Insecurity Crisis Prediction"</p>
Data and code from: Fitness consequences of parasitism in a changing world: A case study with bird blow flies and sagebrush songbirds
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Replication Package for the Paper "OSS License Identification at Scale: A Comprehensive Dataset Using World of Code"
<div> <div># Replication Package for the Paper "OSS License Identification at Scale: A Comprehensive Dataset Using World of Code"<br><br>containing the dataset and scripts used to create it.</div> </div>
Code for manuscript "Multiple evolutionary pressures shape identical consonant avoidance in the world's languages"
<p>This repository contains code for the manuscript "Multiple evolutionary pressures shape identical consonant avoidance in the world’s languages". Unlike the github repository https://github.com/chundrac/idcc, it contains code output, including model fits.</p> <p>Models were run on the University of Zurich's Science Cluster using the Slurm workload management system and the Rstan anaconda environment (https://anaconda.org/conda-forge/r-rstan).</p> <p>The following shell script runs all data processing and model fitting scripts:</p> <p>./process_all_data.sh</p>
Data and code for : Madden Julian Oscillation moves faster as the meridional moisture gradient intensifies in a warming world
<p>Data and code related to the research article titled “Madden Julian Oscillation moves faster as the meridional moisture gradient intensifies in a warming world,” published in Geophysical Research Letters.</p>
Data and R code from: Fire-induced loss of the world's most biodiverse forests in Latin America
<p>Fire plays a dominant role in deforestation, particularly in the tropics, but the relative extent of transformations and influence of fire frequency on eventual forest loss remain unclear. Here we analyze the frequency of fire and its influence on post-fire forest trajectories between 2001-2018. We account for ~1.1% of Latin American forests burnt in 2002-2003 (8,465,850 ha). Although 40.1% of forests (3,393,250 ha) burned only once, by 2018~48% of the evergreen forests converted to other, primarily grass-dominated uses. While greater fire frequency yielded more transformation, our results reveal the staggering impact of even a single fire. Increasing fire frequency imposes greater risks of irreversible forest loss, transforming forests into ecosystems increasingly vulnerable to disturbance and degradation. Reversing this trend is indispensable to both mitigate and adapt to climate change globally. As climate change transforms fire regimes across the region, key actions are needed to conserve Latin American forests.</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>
Code and data for: Demographic resilience may sustain significant coral populations in a 2°C-warmer world.
<p>Data and Matlab code produced for the manuscript: Mason, R. A. B., Bozec, Y.-M., & Mumby, P. J. (2023). Demographic resilience may sustain significant coral populations in a 2°C-warmer world. <em>Global Change Biology</em>, 29(14): 4152-4160. <a href="https://doi.org/10.1111/gcb.16741">https://doi.org/10.1111/gcb.16741</a></p> <p>For questions about the contents of this repository, please contact robert.mason1@uqconnect.edu.au</p>
Data and R code from: Fire-induced loss of the world’s most biodiverse forests in Latin America
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Static Code Analysis Alarms Filtering Reloaded: an ML Approach and its Empirical Evaluation on a New Real-World Dataset
<p>This is a replication data package for a paper titled "<em>Static Code Analysis Alarms Filtering Reloaded: an ML Approach and its Empirical Evaluation on a New Real-World Dataset</em>".</p> <p>Paper abstract: Even though Static Code Analysis (SCA) tools are integrated into many modern software building and testing pipelines, their practical impact is still seriously hindered by the excessive number of false positive warnings they usually produce. To cope with this problem, researchers have proposed several post-processing methods that aim to filter out false hits (or equivalently identify ``actionable'' warnings) after the SCA tool produced its results. However, we found that most of these approaches are targeted (i.e., deal with only a few SCA warning types) and evaluated on synthetic benchmarks or small-scale manually collected data sets (i.e., with typical sample sizes of several hundred).</p> <p>In this paper, we present a code embedding-based approach for filtering false positive warnings produced by 160 different SonarQube rule checks, one of the most widely adopted SCA tools today. We evaluate the method on a dataset containing 224,484 real-world warning samples fixed (true positive samples) or ignored (false positive samples) by the developers, which we collected from 9,958 different open-source Java projects from GitHub using a data mining approach. It is the most extensive real-world study and public dataset we know of in this area. Our method works with an accuracy of 91% (best F1-score of 81.3% and AUC of 95.3%) for the classification of SonarQube warnings.</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.