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675 results for “Opportunism”
Data from paper: "Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates"
<p>Data from the paper:</p> <p>Dalagnol, R. <em>et al.</em> Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates. <em>Sci Rep</em> <strong>11, </strong>1388 (2021). https://doi.org/10.1038/s41598-020-80809-w</p> <p>Link: https://www.nature.com/articles/s41598-020-80809-w</p> <p> </p> <p>This repository contains:</p> <p>1) Data frame with data from static and dynamic gaps used in Figure 2 (Dalagnol_2020_Data_Multitemporal_gaps.csv). Each row is the aggregated measurement at 5-km resolution. The site component referes to the five site studied with multitemporal data. Site order from 1 to 5 is DUC, TAP, FN1, BON and TAL.</p> <p>2) Data frame with data from static gaps and environmental factors used in Table 1, Figure 3, 4, 5 (Dalagnol_2020_Data_Singledate_gaps_Modeling.csv). Each row is the aggregated measurement of one site observed by airborne lidar data.</p> <p>3) Raster file at 5-km resolution with dynamic gap fraction estimates presented in Figure 5 (dynamic_gap_fraction_amazon.tif).</p> <p> </p> <p>If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).</p>
[Supplementary Information] Can LCA be FAIR? – Assessing the status quo and opportunities for FAIR data sharing
<p>This is the supplementary information related to a the manuscript - 'Can LCA be FAIR?' - Assessing the status quo and opportunities for FAIR data sharing. The purpose of this study is to assess the status quo of data sharing in LCA in relation to the FAIR data principles (Findability, Accessibility, Interoperability and Re-use).</p><p>The supplementary information consists of three files:</p><p><strong>SI 1</strong> - How the life cycle inventory is shared in relation to the FAIR data principles in 25 peer reviewed LCA journal articles between 2018 -2022.</p><p><strong>SI 2</strong> - Review of ten data management plans of EU Horizon Europe projects in relation to LCA to assess the recommendations on the implementation of FAIR principles.</p>
Explosive networking: the role of adaptive host radiations and ecological opportunity in a species-rich host-parasite assembly
<p>Dataset for Cruz-Laufer et al. (2021) Explosive networking: the role of adaptive host radiations and ecological opportunity in a species-rich host-parasite assembly.</p> <p><strong>Abstract: </strong>Many species-rich ecological communities emerge from adaptive radiation events. The effects of this explosive speciation on community assembly remain poorly understood. Here, we explore the well-documented radiations of African cichlid fishes and their interactions with the flatworm gill parasites <em>Cichlidogyrus </em>spp., including 10529 reported infections and 477 different host-parasite combinations collected through a survey of peer-reviewed literature. We assess how evolutionary, ecological, and morphological parameters determine host-parasite meta-communities affected by adaptive radiation events through network metrics, host repertoire measures, and network link prediction. The hosts’ evolutionary history mostly determined host repertoires of the parasites. Ecological and evolutionary parameters determined host-parasite interactions. Generally, ecological opportunity and fitting have shaped cichlid-<em>Cichlidogyrus</em> meta-communities suggesting an invasive potential for hosts used in aquaculture. Meta-communities affected by adaptive radiations are increasingly specialised with higher environmental stability. These trends should be verified across other systems to infer generalities in the evolution of species-rich host-parasite networks.</p>
Dataset for: Evaluating phylogenetic methods for quantifying risks and opportunities presented by forks in open source software (master dissertation).
<p>This is the data for my master dissertation [1]. If you wish to get a copy, download it from Zenodo and open docs/master.pdf.</p> <p>Data acquisition and encoding techniques are described in paragraph 3.1.1 (table 3.1).</p> <p>The data is described in more detail in paragraph 4.1 (table 4.2).</p> <p>* fork1_all.csv: MySQL server / MariaDB server<br> * fork2_all.csv: Linux kernel / Android kernel<br> * fork3_all.csv: Apache OpenOffice / LibreOffice</p> <p>==Cite==<br> [1] A. Ortiz-Troncoso. Evaluating phylogenetic methods for quantifying risks and opportunities presented<br> by forks in open source software (master dissertation). Zenodo, 2018. doi: http://doi.org/10.5281/zenodo.1158292</p>
Accompanying material to the Inventory of opportunities and bottlenecks in policy to facilitate the adoption of soil-improving techniques
<p>Inventory of policies at EU and country level for the inventory and analysis of bottlenecks and opportunities in sectoral and environmental policies to facilitate the adoption of Soil-Improving Cropping Systems (SICS).</p>
Global restoration opportunities in tropical rainforest landscapes - Supplementary Materials - Spatial Data Layers
<p><strong>Global restoration opportunities in tropical rainforest landscapes</strong></p> <p><strong>Sci Adv 5 (7), eaav3223</strong></p> <p><strong>DOI: 10.1126/sciadv.aav3223</strong></p> <p><strong><a href="https://advances.sciencemag.org/content/5/7/eaav3223">https://advances.sciencemag.org/content/5/7/eaav3223</a></strong></p> <p><strong>Supplementary Materials</strong></p> <p><strong><a href="https://advances.sciencemag.org/content/suppl/2019/07/01/5.7.eaav3223.DC1">https://advances.sciencemag.org/content/suppl/2019/07/01/5.7.eaav3223.DC1</a></strong></p> <p><strong>Spatial Data layers:</strong></p> <p><strong><a href="https://doi.org/10.5281/zenodo.3233495">https://doi.org/10.5281/zenodo.3233495</a></strong></p> <p><strong>_OutR10:</strong></p> <p><strong>r_10.img → Global restoration opportunity score (ROS)</strong></p> <p><strong>r_10_sc.img → Global restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_nt_sc.img → Neo Tropic restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_aa_sc.img → Australiasia restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_at_sc.img → Afro Tropic restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_im_sc.img → Indo Malay restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_nt_sc.img → Neo Tropic restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p> </p> <p><strong>_OutBasics:</strong></p> <p><strong>r_1.img → Study Area</strong></p> <p><strong>r_2.img → Restorable Area</strong></p> <p><strong>r_3.img → Restoration Benefits</strong></p> <p><strong>r_4.img → Restoration feasibility</strong></p> <p><br> <strong>_OutCountry:</strong></p> <p><strong>r_10_XXX_sc.tif → restoration opportunity score (ROS) for country XXX – rescaled 0-1</strong></p> <p><br> <strong>_OutHotspots:</strong></p> <p><strong>r_10_hotspot_XXX_hotspot_area_sc.tif → restoration opportunity score (ROS) for conservation hotspot area XXX – rescaled 0-1</strong></p> <p><strong>r_10_hotspots_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in conservation hotspots</strong></p> <p><br> <strong>_OutKBA:</strong></p> <p><strong>r_10_XXX_sc.tif → restoration opportunity score (ROS) for Key Biodiversity Area XXX – rescaled 0-1</strong></p> <p><strong>r_10_kba_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in Key Biodiversity Areas</strong></p> <p><br> <strong>_OutAichi:</strong></p> <p><strong>r_10_aichi_XXX.tif → Top 15% area of with highest restoration opportunity score (ROS) in country XXX</strong></p> <p><strong>r_10_aichi.img → Top 15% area of with highest restoration opportunity score (ROS) global</strong></p> <p><br> <strong>_OutBonn:</strong></p> <p><strong>r_10_XXX_Bonn.img → Area with highest restoration opportunity score (ROS) in country XXX according to their Bonn Challenge commitments</strong></p> <p> </p> <p><strong>_OutParis:</strong></p> <p><strong>r_10_at_paris.img → Area with highest restoration opportunity score (ROS) in Afro Tropic Nationally Determined Contributions to the Paris Climate Agreement</strong></p> <p><strong>r_10_im_paris.img → Area with highest restoration opportunity score (ROS) in Indo Malay Nationally Determined Contributions to the Paris Climate Agreement</strong></p> <p><strong>r_10_nt_paris.img → Area with highest restoration opportunity score (ROS) in Neo Tropic Nationally Determined Contributions to the Paris Climate Agreement</strong></p> <p><br> <strong>_OutTEOW:</strong></p> <p><strong>r_10_ECOREGION_XXX_sc.tif → restoration opportunity score (ROS) for Ecoregion XXX – rescaled 0-1</strong></p> <p><strong>r_10_ECOREGION_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in Ecoregions</strong></p> <p> </p> <p><strong>_OutAll</strong></p> <p><strong>alltargets.img → Area with highest restoration opportunity score (ROS) according to all targets (excluded from the paper)</strong></p> <p> </p>
Open Science: New Challenges and Opportunities for the PV sector
<p>Presentation given at the European PV Solar Energy Conference, Marseille, 2019 about the development of Open Science in the context of photovoltaics</p>
Data from: "Mapping opportunities and barriers for coexistence between people and pumas in the Argentine Dry Chaco"
<p>Data from: "Mapping opportunities and barriers for coexistence between people and pumas in the Argentine Dry Chaco"</p><p>====================================================</p><p>Datasets include: </p><p>1. .csv file with puma occurrence data in 149 sites and analyzed covariates (pumadata.csv)</p><p>2. .txt file with puma occupancy modelling scripts (pumascript.txt).</p><p>3. .txt. file with interview data (intdataf.txt).</p><p>4. .txt file with scripts for conflict risk modelling and mapping (confscript.txt).</p><p>5. three zip files with .tiff of the three covariates used for conflict risk mapping (goat.zip, disturbedpr.zip,</p><p>pue1km.zip).</p><p>6. .csv file with protected area data: number of pixels for each landscape type within all protected areas,</p><p>strict protected areas and multiple-use protected areas (PAchaco.csv).<br>7. Read me file with more information on datasets (README.txt)</p><p> </p>
Opportunities and barriers for promoting biodiversity in Danish beef production
<p>Code and data used in the paper "Opportunities and barriers for promoting biodiversity in Danish beef production" published in iScience.</p> <p>Pre-proof available at: <span>https://doi.org/10.1016/j.isci.2024.111422</span></p>
Dataset for the article "Barriers and Opportunities for Sustainable Farming Practices and Crop Diversification Strategies in Mediterranean Cereal-Based Systems"
<p>Datasets from the surveys applied for the article "Barriers and Opportunities for Sustainable Farming Practices and Crop Diversification Strategies in Mediterranean Cereal-Based Systems" <a href="https://doi.org/10.3389/fenvs.2022.861225">https://doi.org/10.3389/fenvs.2022.861225</a></p>
Dataset for "Facsimiles as Epigraphic Evidence: Pitfalls and Opportunities"
<p>This dataset is used in the article "Facsimiles as Epigraphic Evidence: Pitfalls and Opportunities" (Journal of Epigraphic Studies 7, 2024, pp. 9-27). </p> <p>The material was collected through the itemisation of the epigraphic archive collection of Olof August Danielsson (Uppsala University Library). Types of facsimiles, date of creation, creator and references to CIE, Etruskische Texte and CIL were collected during itemisation. Information on type of object and material of object is collected from Etruskische Texte, with additional information from CIE. Method of inscription is collected from CIE. </p>
Supplementary data to Challenges and Opportunities for the Recovery of Critical Raw Materials from Electronic Waste
<p>Content of the excell file:</p> <p>Table S1: Relationship between UNU-keys and MINCOTUR codes</p> <p>Table S2: UNU-Key composition and alpha and beta values for Weibull distributions</p> <p>Table S3: Metal prices used in this study.</p>
Mapping of Collective Action Opportunities for Water Stewardship
<p>With this global mapping exercise, we attempted to identify catchments with stronger need and potential of Collective Action for Water Stewardship. On the “need” side we considered a selection of water and biodiversity risk layers from the <a href="https://riskfilter.org/">WWF Risk Filter Suite</a>. On the “potential” side we considered economic factors such as value of crop production, density of business facilities (assets), and potential for cross-industry collaboration. The result is a global map (or a shapefile) of collective action opportunities, highlighting 350 catchments, across 100 river basins and 7 regions of the world, where multiple NGOs and the private sector shall work together to accelerate collective action for water stewardship. See the interactive map <a href="https://panda.maps.arcgis.com/apps/mapviewer/index.html?webmap=8d5c4bf6c6ff46c1a5c00e0b62e0b821">here</a>.</p> <p>This repository hosts all outputs as well as the input data of the mapping exercise. For transparency, reproducibility, as well as for future enhancements, the code is publicly available at <a href="https://github.com/rafaexx/collective_action_opportunities">https://github.com/rafaexx/collective_action_opportunities</a></p> <p>Final outputs are basically the shapefiles in <strong>output/summarise_geo/shp/*</strong> as well as the list of basins in <strong>output/summarise_geo/3_prioritization/hybas6_list_basins_collective_action.xlsx</strong></p> <p>For more details see part III of the report <a href="https://wwfint.awsassets.panda.org/downloads/unpacking-collective-action-in-water-stewardship.pdf">Unpacking Collective Action in Water Stewardship: Shared Solutions for Shared Water Challenges</a>.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Joint Research Agenda and Research Opportunities
<p>This dataset includes the opinions of different sectoral researchers regarding the importance of the assembled 144 research topics in the open data domain. The survey was conducted in March 2022.</p>
Replication package of "Revisiting Test Smells in Automatically Generated Tests: Limitations, Pitfalls, and Opportunities"
<p><strong>Abstract:</strong><br> Test smells attempt to capture design issues in test code that reduce their maintainability. Previous work found such smells to be highly common in automatically generated test-cases, but based this result on specific static detection rules; although these are based on the original definition of “test smells”, a recent empirical study showed that developers perceive these as overly strict and non-representative of the maintainability and quality of test suites. This leads us to investigate how effective such test smell detection tools are on automatically generated test suites. In this paper, we build a dataset of 2,340 test cases automatically generated by EVOSUITE for 100 Java classes. We performed a multi-stage, cross-validated manual analysis to identify six types of test smells and label their instances. We benchmark the performance of two test smell detection tools: one widely used in prior work, and one recently introduced with the express goal to match developer perceptions of test smells. Our results show that these test smell detection strategies poorly characterized the issues in automatically generated test suites; the older tool’s detection strategies, especially, misclassified over 70% of test smells, both missing real instances (false negatives) and marking many smell-free tests as smelly (false positives). We identify common patterns in these tests that can be used to improve the tools, refine and update the definition of certain test smells, and highlight as of yet uncharacterized issues. Our findings suggest the need for (i) more appropriate metrics to match development practice; and (ii) more accurate detection strategies, to be evaluated primarily in industrial contexts.</p>
Data and code for the paper Atmospheric Observations with E-band Microwave Links – Challenges and Opportunities
<p>Raw and preprocessed data for the paper Atmospheric Observations with E-band Microwave Links – Challenges and Opportunities accepted for publication in the journal Atmospheric Measurement Techniques.</p> <p>The dataset includes total losses (transmitted - received power levels) of commercial microwave links and observations of rainfall, air temperature, and air relative humidity. In addition, theoretical gaseous attenuation calculated from air temperature and relative humidity is provided.</p> <p>Data are stored in semicolon-delimited csv files. Time stamps are in UTC time in the format yyyy-mm-dd HH:MM:SS. Raw data contain not regular time series, preprocessed data contain regular time series at 1-min and 5-min temporal resolution. Metadata are stored in textfiles.</p> <p>Dataset contains also R code for processing and analyzing the data as presented in the paper Atmospheric Observations with E-band Microwave Links – Challenges and Opportunities. The code is in the form of R Markdown files and interactive html notebooks.</p>
Dataset Challenges and Opportunities for Academic Parents during COVID-19
<p>Anonimized dataset of the survey on the impact of COVID-19 on academic parents. Participants who did not give consent were filtered out as well.</p>
Supporting Data from Ecological Opportunities and Constraints in the Evolution of Diatoms
<p>2016-02-01<br /> Teofil Nakov</p> <p>Data and code for Nakov, Ruck, Alverson: Ecological opportunities and constraints in the evolution of diatom</p> <p>This packet contains the following files:</p> <p>1. Sequence alignment for 540 diatoms and 2 Bolidomonas: Nakov-etal-542-taxon-alignment-clean.fasta<br /> 2. The constraint topology used in tree searches: Nakov-etal-constraint-small-clades.phylip<br /> 3. Alignment partition file: Nakov-etal-tiger-partitions-rax<br /> 4. Character data for 540 diatoms and 2 Bolidomonas: Nakov-etal-542-taxon-discrete-characters.csv<br /> 5. The best tree from 420 RAxML optimizations: Nakov-etal-RAxML_bestTree.opt_89<br /> 6. 99 additional trees used in the analyses: Nakov-etal-99-randomly-sampled-unique-trees.newick<br /> 7. All 100 trees converted into chronograms: Nakov-etal-100-chronograms-smooth-1000.newick<br /> 8. Script with random seeds for RAxML optimizations: Nakov-etal-RAxML-calls-for-420-optimizations<br /> 9. R scripts with random seeds: Nakov-etal-R-scripts.zip (three scripts: equal-rates, independent, and coordinated + paint regimes models)<br /> 10. TreePL control file: Nakov-etal-TreePL-control-file.txt</p>
Replication Package for "Compatibility Issues in Deep Learning Systems: Problems and Opportunities"
<p>This dataset contains scripts and data used to generate relevant results for this paper. Detailed information and procedure to reproduce our results are described in README.md. </p> <p>code</p> <p>This folder contains two Python scripts: soextractor.py is used to extract 3,072 high-quality StackOverflow (SO) posts and soextractor_tags.py is used to extract the number of posts for the tags on SO. For detailed data collection criteria, please refer to Section 3.1 of our paper.</p> <p>DL compatibility issues.xlsx</p> <p>This file provides all the collected 3,072 issues, in which each line indicates whether the issue is a DL compatibility issue. Among them, 352 are DL compatibility issues. We also provide information on the library, stage, symptom, type, solution, root cause, and exception type for the DL compatibility issues. For the type CORE-TPL, we also provide backward-incompatible or forward-incompatible as well as API evolution patterns. For detailed manual classification of DL compatibility issues, please refer to Section 3.2 of our paper.</p> <p>Tool Survey.xlsx </p> <p>This file includes all the papers collected from the three top SE conferences (i.e., ICSE, FSE, and ASE) in recent five years (18-22). Each line of each sheet provides the following information: (a) Title, (b) Year, (c) Conference, and (d) Type. For the detailed paper collection procedure, please refer to Section 5 of our paper.</p>
A distinct neurogenomic response to a tradeoff between social challenge and opportunity in male sticklebacks (Gasterosteus aculeatus)
<p>Animals frequently make adaptive decisions about what to prioritize when they are faced with multiple, conflicting demands at the same time. However, the proximate mechanisms of decision-making in the face of competing demands are not well understood. We explored this question using brain transcriptomics in a classic model system: threespined sticklebacks, where males face conflict between courtship and territorial defense. The outcome of decisions made by breeding males over whether to court mates or defend against territory intrusion are profoundly important to fitness. We characterized the behavior and brain gene expression profiles of males confronted by a tradeoff between courtship and territorial defense by comparing them to males not confronted by this tradeoff. When faced with the tradeoff, males behaviorally prioritized defense over courtship, and this decision was reflected in their brain gene expression profiles. A distinct set of genes and biological processes was recruited in the brain when males faced a tradeoff and these responses were largely non-overlapping across two brain regions. Combined, these results raise new questions about the interplay between the neural and molecular mechanisms involved in decision-making.</p>
ScienceDex guides
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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.