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2,163 results for “sustainability”
Figure 8 in The historic background and potential of sustainable small-scale fisheries and aquaculture in small islands: the case of Saint-Pierre and Miquelon
Figure 8. – Evolution of the exports (in black) and imports (in grey) for Saint-Pierre and Miquelon since 2002. Data are from IEDOM reports (https://www.iedom.fr/saint-pierre-et-miquelon/).
Boosting the transition to biorefineries in compliance with sustainability and circularity criteria
<p>The file contains the tables included in the article "Boosting the transition to biorefineries in compliance with sustainability and circularity criteria"</p>
Fig. 1 in The red imported fire ant (Hymenoptera: Formicidae) in the West Indies: distribution of natural enemies and a possible test bed for release of self-sustaining biocontrol agents
Fig. 1. Distribution of 2 fire ant microsporidian pathogens (Kneallhazia solenopsae, Vairimorpha invictae) and 2 fire ant viruses (SINV-1, SiDNV) among collections of the red imported fire ant, Solenopsis invicta, from islands in the West Indies. The fire ant RNA viruses SINV-2 and SINV-3 were not detected in any of the collections. The number of collections from monogyne colonies is shown over the total number of collections for each island or island group (Tortola [1/5], St. John [0/1], and St. Thomas [3/4]).
Fig. 2 in Crop diversification for sustainable insect pest management in eggplant (Solanales: Solanaceae)
Fig. 2. Total ion current (TIC) mode chromatographic plot of marigold leaf volatiles sampled using the thermal desorption (TD) technique.
Fig. 3 in Crop diversification for sustainable insect pest management in eggplant (Solanales: Solanaceae)
Fig. 3. Total ion current (TIC) mode chromatographic plot of mint leaf volatiles sampled using the thermal desorption (TD) technique.
Fig. 11. A–L in Urban and Peri-urban small and medium-size Enterprise Development for sustainable Vegetable Production and Marketing Systems
Fig. 11. A–L. Mamatia retracta (Popov). A. Dorsal valve RM Br133828, exterior, × 40. B. Dorsal valve RM Br133829, interior, × 50. C. Ventral valve RM Br133830, exterior, × 32. D. Dorsal valve RM Br133831, interior, × 27. E, H, I, K. Ventral valve RM Br133832, exterior (E, × 75), oblique posterior view (H, × 40), oblique lateral view (I, × 75), detail of larval shell (K, × 162). F. Ventral valve RM Br133833, oblique lateral view, 62. G, J. Ventral valve RM Br133834, interior (G, × 45) and detail of apical process (J, × 195). L. Ventral valve RM Br133835, detail of larval shell, × 150. All specimens from the Tremadoc chalcedonites, Wysoczki.
Fig. 6. A–N in Urban and Peri-urban small and medium-size Enterprise Development for sustainable Vegetable Production and Marketing Systems
Fig. 6. A–N. Siphonotretella popovi sp. nov. A, N. Dorsal valve RM Br133791, exterior (A, × 26), detail of spines (N, × 100). B. Dorsal valve RM Br133792, exterior, × 32. C. Holotype, ventral valve RM Br133793, exterior, × 26. D, G, L. Dorsal valve RM Br133794, oblique posterior view (D, × 30), exterior (G, × 30), detail of larval shell (L, × 80). E, J. Dorsal valve RM Br133795, exterior (E, × 40) and detail of larval shell (J, × 120). F, H, I, K. Ventral valve RM Br133796, oblique lateral view (F, × 26), oblique posterior view (H, × 32), detail of larval shell and pedicle opening (I, × 80), detail of larval shell and pedicle opening (K, × 90). M. Dorsal valve RM Br133797, interior, × 40. O. Ventral valve RM Br133798, interior, × 23. All specimens from the Tremadoc chalcedonites, Wysoczki.
Fig. 8. A–Q. Semitreta maior Biernat. A in Urban and Peri-urban small and medium-size Enterprise Development for sustainable Vegetable Production and Marketing Systems
Fig. 8. A–Q. Semitreta maior Biernat. A. Dorsal valve RM Br133807, × 30. B. Dorsal valve RM Br133808, interior, × 40. C, G. Ventral valve RM Br133809, exterior (C, × 13) and oblique lateral view (G, × 13). D, L. Dorsal valve RM Br133812, oblique lateral view (D, × 50), detail of larval shell (L, × 195). E. Dorsal valve RM Br133810, exterior, × 30. F, Q. Ventral valve RM Br133811, oblique lateral view (F, × 75), detail of larval shell (Q, × 195). H. Dorsal valve RM Br133814, oblique lateral view, × 40. I. Dorsal valve RM Br133813, oblique lateral view, × 50). J, K, P, O. Dorsal valve RM Br133815, dorsal interior (J, × 25), oblique lateral view (K, × 50), detail of pseudointerarea (P, 100), detail of pseudointerarea (O, × 60). M, N. Ventral valve RM Br133816, oblique lateral view (M, 32), oblique posterior view (N, × 45). All specimens from the Tremadoc chalcedonites, Wysoczki.
Fig. 4. A–L in Urban and Peri-urban small and medium-size Enterprise Development for sustainable Vegetable Production and Marketing Systems
Fig. 4. A–L. Siphonobolus uralensis (Lermontova). A, G. Dorsal valve RM Br133779, interior (A, × 15) and oblique lateral view (G, × 23). B. Ventral valve RM Br133780, exterior, × 19. C, D, L. Ventral valve RM Br133781, oblique lateral view of exterior (C, × 33), posterior view (D, × 36) and detail of pedicle opening (L, × 80). E, H, J. Ventral valve RM Br133782, oblique lateral view of interior (E, × 28), detail of posterior margin (H, × 100) and detail of pedicle tube (J, × 70). F. Dorsal valve RM Br133783, oblique lateral view of exterior, × 26. I. Dorsal valve RM Br133784, oblique lateral view of interior, × 37. K. Ventral valve RM Br133785, detail of pedicle tube, × 55. All specimens from the Tremadoc chalcedonites, Wysoczki.
Fig. 2. A–K in Urban and Peri-urban small and medium-size Enterprise Development for sustainable Vegetable Production and Marketing Systems
Fig. 2. A–K. Elliptoglossa polonica sp. nov. A, H. Dorsal valve RM Br133767, exterior (A, × 45) and oblique lateral view (H, × 45). B, G. Dorsal valve RM Br133768, interior (B, × 32) and oblique lateral view (G, × 40). C. Holotype, ventral valve RM Br133769, exterior, × 45. D. Ventral valve RM Br133770, interior, × 45. E, F, I. Ventral valve RM Br133771, exterior (E, × 38), oblique lateral view (F, × 40) and detail of larval shell (I, × 135). J. Ventral valve RM Br133772, detail of pseudointerarea, × 400. K. Dorsal valve RM Br133773, oblique lateral view of umbonal section of interior, × 100. All specimens from the Tremadoc chalcedonites, Wysoczki.
Fig. 9. A–F in Urban and Peri-urban small and medium-size Enterprise Development for sustainable Vegetable Production and Marketing Systems
Fig. 9. A–F.?Ditreta dividua Biernat. A, D. Dorsal valve RM Br133817, interior (A, × 36), detail of pseudointerarea (D, × 80). B, C, E, F. Ventral valve RM Br133818, oblique lateral view (B, × 32), exterior (C, × 30), oblique posterior view (E, × 30), detail of larval shell (F, × 165). All specimens from the Tremadoc chalcedonites, Wysoczki.
Ecosystem-Level Determinants of Sustained Activity in Open-Source Projects: A Case Study of the PyPI Ecosystem
<pre><em>Replication pack, FSE2018 submission #164: </em><em>------------------------------------------ </em></pre> <pre><strong>**</strong>Working title:<strong>** </strong>Ecosystem-Level Factors Affecting the Survival of Open-Source Projects: A Case Study of the PyPI Ecosystem <strong>**</strong>Note:<strong>** </strong>link to data artifacts is already included in the paper. Link to the code will be included in the Camera Ready version as well. <em>Content description </em><em>=================== </em> <strong>- **</strong>ghd-0.1.0.zip<strong>** </strong>- the code archive. This code produces the dataset files described below <strong>- **</strong>settings.py<strong>** </strong>- settings template for the code archive. <strong>- **</strong>dataset_minimal_Jan_2018.zip<strong>** </strong>- the minimally sufficient version of the dataset. This dataset only includes stats aggregated by the ecosystem (PyPI) <strong>- **</strong>dataset_full_Jan_2018.tgz<strong>** </strong>- full version of the dataset, including project-level statistics. It is ~34Gb unpacked. This dataset still doesn't include PyPI packages themselves, which take around 2TB. <strong>- **</strong>build_model.r, helpers.r<strong>** </strong>- R files to process the survival data (`survival_data.csv` in <strong>**</strong>dataset_minimal_Jan_2018.zip<strong>**</strong>, `common.cache/survival_data.pypi_2008_2017-12_6.csv` in <strong>**</strong>dataset_full_Jan_2018.tgz<strong>**</strong>) <strong>- **</strong>Interview protocol.pdf<strong>** </strong>- approximate protocol used for semistructured interviews. <strong>- </strong>LICENSE - text of GPL v3, under which this dataset is published <strong>- </strong>INSTALL.md - replication guide (~2 pages)</pre> <pre><em>Replication guide </em><em>================= </em> <em>Step 0 - prerequisites </em><em>---------------------- </em> <strong>- </strong>Unix-compatible OS (Linux or OS X) <strong>- </strong>Python interpreter (2.7 was used; Python 3 compatibility is highly likely) <strong>- </strong>R 3.4 or higher (3.4.4 was used, 3.2 is known to be incompatible) Depending on detalization level (see Step 2 for more details): <strong>- </strong>up to 2Tb of disk space (see Step 2 detalization levels) <strong>- </strong>at least 16Gb of RAM (64 preferable) <strong>- </strong>few hours to few month of processing time <em>Step 1 - software </em><em>---------------- </em> <strong>- </strong>unpack <strong>**</strong>ghd-0.1.0.zip<strong>**</strong>, or clone from gitlab: git clone https://gitlab.com/user2589/ghd.git git checkout 0.1.0 `cd` into the extracted folder. All commands below assume it as a current directory. <strong>- </strong>copy `settings.py` into the extracted folder. Edit the file: <strong> * </strong>set `DATASET_PATH` to some newly created folder path <strong> * </strong>add at least one GitHub API token to `SCRAPER_GITHUB_API_TOKENS` <strong>- </strong>install docker. For Ubuntu Linux, the command is `sudo apt-get install docker-compose` <strong>- </strong>install libarchive and headers: `sudo apt-get install libarchive-dev` <strong>- </strong>(optional) to replicate on NPM, install yajl: `sudo apt-get install yajl-tools` Without this dependency, you might get an error on the next step, but it's safe to ignore. <strong>- </strong>install Python libraries: `pip install --user -r requirements.txt` . <strong>- </strong>disable all APIs except GitHub (Bitbucket and Gitlab support were not yet implemented when this study was in progress): edit `scraper/init.py`, comment out everything except GitHub support in `PROVIDERS`. <em>Step 2 - obtaining the dataset </em><em>----------------------------- </em> The ultimate goal of this step is to get output of the Python function `common.utils.survival_data()` and save it into a CSV file: # copy and paste into a Python console from common import utils survival_data = utils.survival_data('pypi', '2008', smoothing=6) survival_data.to_csv('survival_data.csv') Since full replication will take several months, here are some ways to speedup the process: <em>####Option 2.a, difficulty level: easiest </em> Just use the precomputed data. Step 1 is not necessary under this scenario. <strong>- </strong>extract <strong>**</strong>dataset_minimal_Jan_2018.zip<strong>** </strong><strong>- </strong>get `survival_data.csv`, go to the next step <em>####Option 2.b, difficulty level: easy </em> Use precomputed longitudinal feature values to build the final table. The whole process will take 15..30 minutes. <strong>- </strong>create a folder `<DATASET_PATH>/common.cache`, where `<DATASET_PATH>` is the value of the variable `DATASET_PATH` in `settings.py` <strong>- </strong>extract <strong>**</strong>dataset_minimal_Jan_2018<strong>** </strong>to the newly created folder <strong>- </strong>rename files: mv backporting.csv monthly_data.pypi_backporting.csv mv cc_degree.csv monthly_data.pypi_cc_degree.csv mv commercial.csv monthly_data.pypi_commercial.csv mv commits.csv monthly_data.pypi_commits.csv mv contributors.csv monthly_data.pypi_contributors.csv mv dc_katz.csv monthly_data.pypi_dc_katz.csv mv downstreams.csv monthly_data.pypi_downstreams.csv mv d_upstreams.csv monthly_data.pypi_d_upstreams.csv mv github_user_info.csv user_info.pypi.csv mv issues.csv monthly_data.pypi_issues.csv mv non_dev_issues.csv monthly_data.pypi_non_dev_issues.csv mv non_dev_submitters.csv monthly_data.pypi_non_dev_submitters mv package_urls.csv package_urls.pypi.csv mv q90.csv monthly_data.pypi_q90.csv # raw_dependencies.csv is not required # raw_packages_info.csv is not required # Feel free to read README.md for more details about the data mv submitters.csv monthly_data.pypi_submitters.csv # In this scenario we'll generate a new survival_data.csv mv university.csv monthly_data.pypi_university.csv mv upstreams.csv monthly_data.pypi_upstreams.csv <strong>- </strong>edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` Then, use the Python code above to obtain `survival_data.csv`. <em>####Option 2.c, difficulty level: medium </em> Use pre-downloaded raw data to build longitudinal feature values, and then the dataset. Despite most of the data is cached, some functions will pull up updates which might take anywhere from days to couple weeks to run. <strong>- </strong>Download <strong>**</strong>dataset_full_Jan_2018.tgz<strong>** </strong>(5.4Gb compressed, 34Gb unpacked). <strong>- </strong>edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` <strong>- </strong>extract the content of this archive into `<DATASET_PATH>`. <strong>- </strong>clean up `<DATASET_PATH>/common.cache` (otherwise you'll get Step 2.a. You can reproduce Step 2.b by deleting only `survival_data.pypi_2008_2017-12_6.csv`) Run the Python code above to obtain `survival_data.csv`. <em>####Option 2.d, difficulty level: hard </em> Build the dataset from scratch. Although most of the processing is parallelized, it will take at least couple months on a reasonably powerful server (32 cores, 512G of RAM, 2Tb+ of HDD space in our setup). <strong>- </strong>ensure the `<DATASET_PATH>` is empty <strong>- </strong>add more GitHub tokens (borrow from your coworkers) to `settings.py`. Run the Python code above to obtain `survival_data.csv`. <em>Step 3 - run the regression </em><em>--------------------------- </em> install R libraries: install.packages(c("htmlTable", "OIsurv", "survival", "car", "survminer", "ggplot2", "sqldf", "pscl", "texreg", "xtable")) Use `build_model.r` (e.g. in RStudio) and produced `survival_data.csv` to build the regressions used in the paper. This process takes at least 16Gb of RAM and takes few hours to run due to the gigantic size of the dataset. </pre>
Assessing the sustainability performance of sustainability management software - replication package
<p>This is the replication package for the article "Assessing the sustainability performance of sustainability management software", published in Technologies – SI „Advanced Green Information and Communication Technology“</p> <p><strong>Contents</strong><br> The repository contains the following files:</p> <ul> <li><em>Data_-_Scenario_Hardware_Measurement.csv:</em><br> measurement data acquired during the measurement of the standard usage scenario</li> <li><em>Data_-_Scenario_Log.txt:</em><br> timestamp log file for the measurements of the standard usage scenario</li> <li><em>Data_-_Baseline_Hardware_Measurement.csv:</em><br> baseline meaurements</li> <li><em>Data_-_Baseline_Log.txt:</em><br> baseline timestamp log file</li> <li><em>R_image.Rdata:</em><br> data is also available in an R image dump</li> <li><em>R_Analysis_Script.R:</em><br> analysis script, written in R</li> <li><em>Results_SCSS_WeSustain_ESM.pdf:</em><br> results of the analysis</li> <li><em>Results_and_calculation_for_Indicators_1.2.b_and_1.2.c.ods:</em><br> calculation for indicators 1.1.4.d) 1.2.b) and 1.2.c)</li> <li><em>Usage-scenario_WeSustain_ESM.pdf:</em><br> description of the standard usage scenario</li> </ul> <p><strong>Usage</strong><br> To recreate the analysis, run the R script and, if necessary, modify <em>lines 103 to 115</em> to fit the filenames where to find the data.<br> <em>Lines 193 to 217</em> need to be executed manually, to generate the desired plots and calculations.</p> <p>It is also possible to load the <em>R_image.RData</em> data dump into an R session, import the library `psych` (<em>line 2</em> in the script) and manually execute <em>lines 193 to 217</em> to generate the desired plots and calculations.</p>
A comprehensive collection of sustainability criteria and indicators included in current sustainability certification schemes
<p>This dataset is part of the results of STAR-ProBio WP1. In this WP, we have analysed existing sustainability certification schemes in the EU Bioeconomy. This dataset includes the sustainability principles, criteria and indicators from an in-depth assessment of ~ 50 EU bioeconomy sustainability certification schemes.</p>
What makes research software sustainable? Anonymized interview transcripts
<p>Anonymized transcripts of a series of interviews with the developers of research software.</p> <p>We also include the Participant Information Sheet provided to all participants before the interview commences.</p>
Replication package: assessing the sustainability of software products - a method comparison
<p><strong>Assessing the Sustainability of Software Products - A Method Comparison - Replication Package</strong></p> <p>This is a replication package for the paper entitled "Assessing the Sustainability of Software Products - A Method Comparison". The paper was submitted to the 33. EnviroInfo conference "Environmental Informatics – Computational sustainability: ICT methods to achieve the UN Sustainable Development Goals", 23th – 26th September 2019 at the University of Kassel, Germany.</p> <p>For further information, please refer to the <a href="https://zenodo.org/record/3257517/files/README.md?download=1">README.md</a></p> <p>This replication package is licensed under <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons CC BY-NC 4.0</a>.</p>
Link to dataset related to article "Sustained activation of detoxification pathways promotes liver carcinogenesis in response to chronic bile acid-mediated damage"
<p>This record contains link to raw data related to article "Sustained activation of detoxification pathways promotes liver carcinogenesis in response to chronic bile acid-mediated damage"</p> <p>Chronic inflammation promotes oncogenic transformation and tumor progression. Many inflammatory agents also generate a toxic microenvironment, implying that adaptive mechanisms must be deployed for cells to survive and undergo transformation in such unfavorable contexts. A paradigmatic case is represented by cancers occurring in pediatric patients with genetic defects of hepatocyte phosphatidylcholine transporters and in the corresponding mouse model (Mdr2-/- mice), in which impaired bile salt emulsification leads to chronic hepatocyte damage and inflammation, eventually resulting in oncogenic transformation. By combining genomics and metabolomics, we found that the transition from inflammation to cancer in Mdr2-/- mice was linked to the sustained transcriptional activation of metabolic detoxification systems and transporters by the Constitutive Androstane Receptor (CAR), a hepatocyte-specific nuclear receptor. Activation of CAR-dependent gene expression programs coincided with reduced content of toxic bile acids in cancer nodules relative to inflamed livers. Treatment of Mdr2-/- mice with a CAR inhibitor blocked cancer progression and caused a partial regression of existing tumors. These results indicate that the acquisition of resistance to endo- or xeno-biotic toxicity is critical for cancers that develop in toxic microenvironments.</p> <p>https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE80777 under the accession GSE80777, which comprises ChIP-seq data (GSE80775) and expression data (GSE80776).</p>
nmatthews323/SustAssessR: An integrated codebase for sustainability assessment using R
<p>An integrated codebase for sustainability assessment using R</p> <p>This codebase was used for and is made available alongside the following publication: Matthews, N. E., Cizauskas, C. A., Layton, D. S., Stamford, L., & Shapira, P. (2019). Collaborating constructively for sustainable biotechnology. Scientific Reports. <a href="https://doi.org/10.1038/s41598-019-54331-7">https://doi.org/10.1038/s41598-019-54331-7</a></p> <p><strong>A note on input data</strong></p> <p>This repository is designed to demonstrate and make available the code used for the above publication. Due to the proprietary nature of some of the process flow modelling the data in the directory "Data/ProcessedFlows" has been averaged and rounded to three significant figures. Therefore, while this codebase will authentically replicate the data processing carried out in the published analysis, the outputs will not be identical due to the changes made to the input data. All output data from the original publication has been made available through the supplementary information of the original publication.</p> <p> </p>
Fig. 3 in Sustainability of capture of fish bycatch in the prawn trawling in northeastern Brazil
Fig. 3. Correspondence analysis showing the correlation among species and criteria used to evaluate the susceptibility and resilience. Legend: Pom cor - Pomadasys corvinaeformis, Cat spi - Cathrops spixii, Ste ras - Stellifer rastrifer, Pel har - Pellona harroweri, Ste ste - Stellifer stellifer, Chi ble - Chirocentrodon bleekerianus, Lar bre - Larimus breviceps, Men ame - Menticirrhus americanus, Con nob - Conodon nobilis, Pol vir - Polidactylus virginicus, Anc spi - Anchoa spinifer, Sel set - Selene setapinnis, Iso par - Isopisthus parvipinnis, Cet ede - Cetengraulius edentulus, Anc tri - Anchoa tricolor, Sel vom - Selene vomer, Bag mar - Bagre marinus, Rep - reproductive.
Fig. 2 in Sustainability of capture of fish bycatch in the prawn trawling in northeastern Brazil
Fig. 2. Classification of bycatch species according to criteria indicating susceptibility to capture and resilience. The combination of these criteria provides a graphic that indicates the species with the most sustainable catch. Legend: Pom cor - Pomadasys corvinaeformis, Cat spi - Cathrops spixii, Ste ras - Stellifer rastrifer, Pel har - Pellona harroweri, Ste ste - Stellifer stellifer, Chi ble - Chirocentrodon bleekerianus, Lar bre - Larimus breviceps, Men ame - Menticirrhus americanus, Con nob - Conodon nobilis, Pol vir - Polidactylus virginicus, Anc spi - Anchoa spinifer, Sel set - Selene setapinnis, Iso par - Isopisthus parvipinnis, Cet ede - Cetengraulius edentulus, Anc tri - Anchoa tricolor, Sel vom - Selene vomer, Bag mar - Bagre marinus.
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.