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2,163 results for “sustainability”
Empirical data, qualitative codes, analysis: Schuur J.S. et al. Identifying levers of urban neighbourhood transformation. npj Urban Sustainability (2023)
<p>Please refer to the stand-alone "2023_SchuurJS_UrbanSustainabilityfinal.html" file where the analysis and results corresponding to the article titled: "Identifying levers of urban neighbourhood transformation using serious games" is presented. The underlying data sets and Rmarkdown script used for the analysis can be used to re-run the analysis. Ensure to read the "0_README.txt" file to build the appropriate folder structure to do so.</p>
Weekly county-level pollution data for China from Zhang, Carleton, Lin, and Zhou (accepted, Nature Sustainability), "Estimating the role of air quality improvements in the decline of suicide rates in China"
<p>This dataset contains weekly, county-level air pollution data for 2,839 counties from 2013 to early 2018. These data are used and described in Zhang, Carleton, Lin, and Zhou (accepted, <em>Nature Sustainability</em>), "Estimating the role of air quality improvements in the decline of suicide rates in China". When the paper is published a link to the manuscript will be added here. </p> <p>The manuscript Methods section details data construction. In summary, these county-level observations are obtained from monitoring stations maintained by the China National Environmental Monitoring Center (CNEMC), which is affiliated with the Ministry of Ecology and Environment of China. CNEMC began publishing hourly air pollution data in 2013, including the Air Quality Index, PM2.5, PM10, ozone, sulfur dioxide, nitrogen dioxide, and carbon monoxide. We average hourly data to the station-day level and use inverse-distance weighting with a radius of 200km to convert data from station to the county level. We average across days to generate county-level weekly values. Any missing station-hour observations in the raw data are omitted in this spatial and temporal aggregation. Our main analysis relies on PM2.5, but all pollutants are released here.</p>
A floating 3D printed formulation for the coadministration and sustained release of antihypertensive drugs - Underlying CT data
<p>Underlying CT data of <strong>"A floating 3D printed formulation for the coadministration and sustained release of antihypertensive drugs"</strong></p> <p>Paola Zgouro1, Orestis L. Katsamenis3,4, Thomas Moschakis5, Georgios K. Eleftheriadis6, Athanasios S. Kyriakidis6, Konstantina Chachlioutaki1,2, Paraskevi Kyriaki Monou1,2, Marianna Ntorkou7, Constantinos K. Zacharis7, Nikolaos Bouropoulos8,9, Dimitrios G. Fatouros1,2, Christina Karavasili1, Christos I. Gioumouxouzis1</p> <p><em>1 Laboratory of Pharmaceutical Technology, Department of Pharmaceutical Sciences, Aristotle University of Thessaloniki, GR-54124, Thessaloniki, Greece</em><br><em>2 Center for Interdisciplinary Research and Innovation (CIRI-AUTH), 57001 Thessaloniki, Greece</em><br><em>3 μ-VIS X-Ray Imaging Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK</em><br><em>4 Institute for Life Sciences, University of Southampton, University Rd, Highfield, Southampton, SO17 1BJ, UK</em><br><em>5 Department of Food Science and Technology, School of Agriculture, Aristotle University of Thessaloniki, GR-541 24 Thessaloniki, Greece</em><br><em>6 Pharmacare Premium Limited, R&D Department, HHF003 Hal Far Industrial Estate, Birzebbugia BBG3000, Malta</em><br><em>7 Laboratory of Pharmaceutical Analysis, Department of Pharmacy, Aristotle University of Thessaloniki, GR-54124, Greece</em><br><em>8 Department of Materials Science, University of Patras, 26504 Rio, Patras, Greece</em><br><em>9 Foundation for Research and Technology Hellas, Institute of Chemical Engineering and High Temperature Chemical Processes, Patras, Greece</em></p> <p><strong>Microfocus Computed Tomography (μCT)</strong></p> <p>X-ray microfocus computed tomography (μCT) was employed for the characterization of the microstructure of the printed object, assessing the overall volume, porosity, local thickness and other printing defects. The imaging took place at the University of Southampton’s μ-VIS X-ray Imaging Centre (<a title="&mu;-VIS X-ray Imaging Centre at the University of Southampton" href="https://www.muvis.org" target="_blank" rel="noopener">www.muvis.org</a>) / 3D X-ray Histology facility using a customized μCT scanner optimized for 3D X-ray histology (<a title="3D X-ray Histology facility at University of Southampton" href="https://www.xrayhistology.org" target="_blank" rel="noopener">www.xrayhistology.org</a>) (<a title="A high-throughput 3D X-ray histology facility for biomedical research and preclinical applications" href="https://doi.org/10.12688/wellcomeopenres.19666.2" target="_blank" rel="noopener">Katsamenis et al., 2023</a>) based on Nikon’s XTH225ST system (Nikon Metrology, Castle Donington, UK). The scanner was operated at 110 kVp / 90 μA (9.9 W), with the X-ray beam prefiltered using 0.04 mm of aluminum. The source-to-object and source-to-detector distances were 28.4 mm and 1136.7 mm, respectively, resulting in a magnification factor of 40x. Acquisition parameters included 2201 projections, averaging 4 frames per projection, with an exposure time of 177 ms per projection. The 2850 x 2850 dexels detector was binned 2x (virtual detector: 1425 × 1425 dexels), resulting in an isotropic voxel edge of 7.5 μm. The reconstructed data underwent visualization and analysis using Dragonfly software (Comet Technologies Canada Inc.; software available at http://www.theobjects.com/dragonfly).</p>
Dataset and Input Files for the "Sustainability and Resilience Through Connection: The Economic Metacommunites of the Western USA" Manuscript
<p>Datasets and input files used for the Ecology and Society manusript "Sustainability and Resilience Through Connection: The Economic Metacommunites of the Western USA". </p>
Data for The Disparities and Development Trajectories of Nations in Achieving the Sustainable Development Goals
<p>This dataset provides the source data for Tables and Figures in the main text and the supplementary information, and the code for the main figure of the article.</p>
Systematic review data on the role of urban planning in the context of sustainability transformations and human-nature connections
<p>This data publication belongs to the following research paper:<br>Harms, P., Hofer, M. & Artmann, M. Planning cities with nature for sustainability transformations — a systematic review. Urban Transform 6, 9 (2024). <br>https://doi.org/10.1186/s42854-024-00066-2 </p> <p>We conducted a systematic literature review according to the PRISMA Statement 2020 (Page et al. 2021). The list shows the steps performed and the names of the corresponding datasets available here:</p> <p>Step A - Identification of Records<br>A_01_PRISMA-protocoll.pdf<br>A_02_searchstring.txt<br>A_03_recordsidentified.ris</p> <p>Step B - Screening of Records<br>B_01_recordsscreened-title-keywords.ris<br>B_02_recordsscreened-abstract.ris<br>B_03_recordsscreened-fulltext.ris<br>B_04_studiesincluded.ris<br>B_05_screeningdecisions-overview.xlsx</p> <p>Step C - Qualitative Analysis<br>C_01_codingscheme.xlsx</p> <p> </p>
"Non facciamo la guerra, combattiamo i rifiuti!"_FishArt.Participatory Art for environmental sustainability and aesthetic transformation of Anzio Fishermen's Harbour
<p>FishArt is a 10-month international project funded by the New European Bahahus Cross-KIC Connect 2024 written by and granted to Chiara Certoma’ (UniTo – now at UniRm1) and led by Laura Corazza (UniTo), in collaboration with Raw-News, Platoon and the fishermen cooperatives of Anzio (Rome, Italy). FishArt promotes a radically participatory art and education process supporting the requalification of the Fishermen’s Harbour in the coastal city of Anzio, Italy.As part of the FishArt project agenda, transect walks and educational training initiatives have been combined in a major event, strongly requested by the local community. The synergy between local institutions and international research aims to stimulate civic participation on the problem of marine pollution and the care of the coastal territory.The event “Let’s not make war, let’s fight waste!” is a civic initiative promoted by the Extraordinary Commission supported by the Environment Area of the Municipality of Anzio which aims to involve all the components of civil society for a collaborative cleaning of the beaches, transforming the celebrations of the Anzio/Nettuno landing into a moment of sharing and to raise awareness against marine pollution.</p>
Transforming towards what? A review of futures thinking applied in the quest for navigating sustainability transformations
<p>This is the dataset used for the review article "Transforming towards what? A review of futures thinking applied in the quest for navigating sustainability transformations". The spreadsheet contains the bibliographic records and the data used and organized for its analysis. </p>
Supplementary Table 1 and data from the workshop on Digital Building Logbooks and Permit Processes for Sustainability in Sustainable Places 24.9.2024 in Luxembourg
<p>This repository contains the supplementary Table 1 and data collected during a workshop on Digital Building Logbooks and Permit Processes for Sustainability. The workshop was held in Sustainable Places on the 24th of September 2024 in Luxembourg. </p>
EU Taxonomy on Sustainable Activities (Tidy)
<p>In order to meet the EU’s climate and energy targets for 2030 and reach the objectives of the European green deal, it is vital that we direct investments towards sustainable projects and activities. To achieve this, a common language and a clear definition of what is ‘sustainable’ is needed. This is why the action plan on financing sustainable growth called for the creation of a common classification system for sustainable economic activities, or an EU taxonomy.</p> <p>The EU taxonomy is a classification system, establishing a list of environmentally sustainable economic activities in the areas of Climate mitigation, Climate adaptation, Biodiversity, Circular economy, Water, Pollution prevention. It could play an important role helping the EU scale up sustainable investment and implement the European green deal. The EU taxonomy would provide companies, investors and policymakers with appropriate definitions for which economic activities can be considered environmentally sustainable. It was defined by the Regulation (EU) 2020/852</p> <p>The European Commmission created an EU Taxonomy Compass provides a visual representation of the contents of the EU Taxonomy, starting with the Delegated Act on the climate objectives, as adopted on 4 June 2021. Whilst you can download the EU Taxonomy in xlsx or json format, they are not tidy datasets, and they are not particularly well-suited for calculations or filtering.</p> <p>Reprex created a tidy version of the EU Taxonomy for developing better sustainability indicators into the Green Deal Data Observatory. This tidy version has subjective weights given to broader NACE sections and divisions. We plan to add better, more objective weights for NACE sections or divisions which are only partially matching the EU Taxonomy. For example, H49.32 is part of the sustainable taxonomy, but the entire H49 division is not. Therefore, we use weight=0.5 for this division. The A2 division is fully part of the taxonomy, and we use a weight=1 to refer to this fact. A better weighting would consider the weight of the H492.32 activity within the H49 division in the European economy. What would make such a weighting tricy is that this weight is different for each European country and the EU as a whole.</p>
Data for 'Stakeholder Perspectives on Nature, People, and Sustainability at Mount Kilimanjaro'
<p>Title: Data for ‘Stakeholder Perspectives on Nature, People, and Sustainability at Mount Kilimanjaro’</p> <p>Recommended Citation: Masao CA, Prescott GW, Snethlage MA, Urbach D, Torre-Marin Rando A, Molina-Venegas R, Mollel NP, Hemp C, Hemp A, Fischer M (2022). People and Nature.</p> <p>Principal Investigator:<br> - Markus Fischer (markus.fischer@ips.unibe.ch)</p> <p>Authors:<br> * joint first-author<br> - Catherine A. Masao (ndeutz@yahoo.com, ORCID: 0000-0002-1242-9117) *<br> - Graham W. Prescott (graham.prescott.research@gmail.com, ORCID: 0000-0001-5123-514X) *<br> - Mark A. Snethlage (mark.snethlage@ips.unibe.ch, ORCID: 0000-0002-1398-8869) *<br> - Davnah Urbach (davnah.payne@ips.unibe.ch, ORCID: 0000-0001-9170-7834) *<br> - Amor Torre-Marin Rando (amor.torre@ips.unibe.ch)<br> - Rafael Molina Venegas (rafmolven@gmail.com, ORCID 0000-0001-5801-0736)<br> - Neduvoto P. Mollel (neduvotomollel@yahoo.com, ORCID: 0000-0002-4402-4667)<br> - Claudia Hemp (claudiahemp@yahoo.com, ORCID: 0000-0002-5369-2122)<br> - Andreas Hemp (andreas.hemp@uni-bayreuth.de, ORCID: 0000-0001-9170-7113)<br> - Markus Fischer (markus.fischer@ips.unibe.ch, ORCID: 0000-0002-5589-5900)</p> <p>Date of data collection: 2018-09<br> Location of data collection: Moshi, Kilimanjaro Region, Tanzania<br> Date of final file release: 2022-01-13</p> <p>Data Overview:</p> <p>We conducted a three-day stakeholder workshop in Moshi, Tanzania, in September 2018. The workshop was attended by 73 participants (16 women and 57 men), whom we invited to represent various sectors and local communities. We established the list of invitees through an extensive online search validated and complemented by key local informants. We divided registered participants into five groups based on their sectoral affiliation: 16 residents of local communities, including farmers (herein ‘Community’), 14 researchers and scientists (‘Research’), 16 professionals in conservation and management (‘Conservation’), 17 professionals in forestry, agriculture, and water management and governance (‘Resources’), and 10 other professionals mainly drawn from the tourism sector (‘Other’).</p> <p>We used two questionnaires—herein ‘habitat’ and ‘ecosystem services’— with open and closed questions. Closed questions were scored using a Likert-type scale.</p> <p>File overview:</p> <p>1. kilimanjaro_ipbes_workshop_habitat_questionnaire.csv</p> <p>Data from the ‘habitat’ questionnaire, entered by Catherine A. Masao and Mark A. Snethlage (finalised 2020-09-22). Individual perceptions about the state of and trends in habitats and species diversity and about the direct and indirect factors driving these trends. We invited participants to fill out separate questionnaires for each habitat of importance to their sector or for which they had knowledge, starting with the most important one.</p> <p>2. kilimanjaro_ipbes_workshop_ecosystem_services_questionnaire.csv</p> <p>Data from the ‘ecosystem services’ questionnaire, entered by Catherine A. Masao and Mark A. Snethlage (finalised 2020-01-09). The ‘ecosystem services’ questionnaire collected individual perceptions about the state of, trends in, and importance of NCP (Nature's Contributions to People), as well as about the factors driving observed changes in access and provision. With reference to the preliminary group discussion on NCP, we invited participants to fill out separate forms for each NCP they deemed important to their sector or had knowledge about and to indicate which habitat(s) provide(s) each of them.</p> <p>3. kilimanjaro_ipbes_workshop_ecosystem_services_access_change_codes.csv</p> <p>Adapted from the ecosytem services questionnaire data (kilimanjaro_ipbes_workshop_ecosystem_services_questionnaire.csv), coding the reasons for change in access to NCP.</p> <p>4. kilimanjaro_ipbes_workshop_spatial_scales_recommended_measures.csv</p> <p>Tally of recommended measures towards recorded from the carousel session, grouped by spatial scale and Conservation Measures Partnership (CMP) categories. See Table S7 for details.</p> <p><br> Code used for analysis:<br> R code used for the statistical analysis and to create the figures available from: https://github.com/grahamprescott/kilimanjaro.ipbes.workshop.paper</p> <p>File details:</p> <p>1. kilimanjaro_ipbes_workshop_habitat_questionnaire.csv</p> <p>143 observations of 73 variables</p> <p>Key Variables:<br> - Group<br> (categorical - stakeholder group to which participants were assigned. Blue = Community, Green = Research, Orange = Conservation, Red = Other, Yellow = Resources)<br> - Biome2<br> (categorical - standardised habitat categories used in the analysis, coded by Mark A. Snethlage)<br> - Habitat.area<br> (categorical - trends in habitat area over past 10 years (2008-2018); Decreased, Not Changed, Increased, No Answer)<br> - Habitat.condition<br> (categorical - trends in habitat condition over past 10 years (2008-2018); Deteriorated, Not Changed, Improved, No Answer)<br> - Habitat.area.will<br> (categorical - prediction for trend in habitat condition over next 10 years (2018-2028); Decrease Not Change, Increase, No Answer)<br> - Habitat.condition.will<br> (categorical - trends in habitat condition over past 10 years (2018-2028); Decrease, Not Change, Increase, No Answer)<br> Variables beginning with ES., DIR., IND., ACT. refer to ecosystem services (i.e. NCP), direct drivers, indirect drivers, and recommended actions associated with each habitat form. They are numerical and scored as 1 if that variable is mentioned (present) or 0 if not mentioned (absent). In a few cases where different ecosystem services listed by the participant are coded to the same variable the number is the number of times that ecosystem service is mentioned.</p> <p>Codes for ecosystem services (ES.): HAB (Habitat Creation and Maintenance), POL (Pollination and dispersal of seeds and other propagules), AIR (Regulation of Air Quality), CLI (Regulation of Climate), OCE (Regulation of Ocean Acidification), WQN (Regulation of Freshwater Quantity, Location, and Timing), WQL (Regulation of Freshwater and Coastal Water Quality), SOL (Formation, Protection, and Decontamination of Soils and Sediments), HAZ (Regulation of Hazards and Extreme Events), PST (Regulation of Organisms Detrimental to Humans), NRG (Energy), FOD (Food and Feed), MAT (Materials and Assistance), MED (Medicinal, Biochemical, and Genetic Resources), LRN (Learning and Inspiration), EXP (Physical and Psychological Experiences), IDE (Supporting Identities), OPT (Maintenance of Options), WEB (Human Wellbeing), LIV (Livelihoods). Note: WEB and LIV are not traditionally included in NCP categories, but we created them as additional categories to capture responses that could not strictly be placed into the traditional 18 categories. </p> <p>Codes for direct drivers (DIR.): ACT = ‘Human Activities’, CC = Climate Change, IAS = Invasive Alien Species, LUC = Land-Use Change, OVR = Overexploitation, POL = Pollution.</p> <p>Codes for indirect drivers (IND.): CLT = Cultural, DEM = Demographic, ECO = Economic, GOV = Governance, S.T = Science and Technology.</p> <p>Codes for recommended actions (ACT.): AWR = Awareness Raising, ECO = Livelihood, Economic & Moral Incentives, EDU = Education & Training, ENF = Law Enforcement & Prosecution, INS = Institutional Development, LAN = Land / Water Management, LAW = Legal & Policy Frameworks, PRT = Conservation Designation & Planning, RSR = Research & Monitoring, SPC = Species Management.</p> <p>2. kilimanjaro_ipbes_workshop_ecosystem_services_questionnaire.csv</p> <p>144 observations of 38 variables</p> <p>Key variables:</p> <p>- Group<br> (categorical - stakeholder group to which participants were assigned. Blue = Community, Green = Research, Orange = Conservation, Red = Other, Yellow = Resources)<br> - Service.original (free text response to which ecosystem service the participant was filling out the form)<br> - ESCODE<br> (categorical - NCP category to which we assigned the free text response. Abbreviations: HAB (Habitat Creation and Maintenance), POL (Pollination and dispersal of seeds and other propagules), AIR (Regulation of Air Quality), CLI (Regulation of Climate), OCE (Regulation of Ocean Acidification), WQN (Regulation of Freshwater Quantity, Location, and Timing), WQL (Regulation of Freshwater and Coastal Water Quality), SOL (Formation, Protection, and Decontamination of Soils and Sediments), HAZ (Regulation of Hazards and Extreme Events), PST (Regulation of Organisms Detrimental to Humans), NRG (Energy), FOD (Food and Feed), MAT (Materials and Assistance), MED (Medicinal, Biochemical, and Genetic Resources), LRN (Learning and Inspiration), EXP (Physical and Psychological Experiences), IDE (Supporting Identities), OPT (Maintenance of Options), WEB (Human Wellbeing), LIV (Livelihoods). Note: WEB and LIV are not traditionally included in NCP categories, but we created them as additional categories to capture responses that could not strictly be placed into the traditional 18 categories.)<br> - Biome<br> (categorical - which habitat provided the ecosystem service)<br> - Why.changed.provision<br> (free text response for why Provision changed)<br> - Why.changed.access<br> (free text response for why Access changed) [Note: although we theoretically expected a distinction between provision and access of each ecosystem service, we observed that this distinction was not strictly followed in practice and deemed the responses about access to be most accurate]<br> - Access<br> (categorical - changes in access to the ecosystem service over the last 10 years (2008-2018); Decreased, No Change, Increased, No Answer)<br> - Access.will<br> (categorical - predicted changes in access to the ecosystem service over the next 10 years (2018-2028); Deteriorate, Not Change, No Answer, Improve (note: no one responded ‘Improve’)) </p> <p><br> 3. kilimanjaro_ipbes_workshop_ecosystem_services_access_change_codes.csv</p> <p>144 observations of 7 variables</p> <p>We took the following variables from the ecosystem services questionnaire:<br> - ESCODE<br> (categorical - NCP category to which we assigned the free text response)<br> - Access<br> (whether access to this NCP increased or decreased between 2008-2018)<br> - Why.changed.access<br> (free text response for why Access changed)<br> And created a new variable to synthesise the drivers of change in NCP access:<br> - Why.changed.access.code</p> <p>Note: a challenge with the ‘Why.changed.access’ variable is that many drivers are listed in the same response. To process this, we duplicated the rows with multiple drivers so that there would be one row per driver. We did this using Microsoft Excel for Mac. We did this so that each link from a driver to an increase or decrease in a given NCP could be visualised. The individual links are not standardised by individual respondent or response. They represent every instance of a reported link between a driver of change and a change in access to a given NCP. Responses or respondents who listed multiple instances of NCP access change and/or multiple drivers have therefore contributed more to the Sankey figure (Figure 4). We chose this approach because the aim in this case was to document the complex web of drivers leading to changes in NCP access, drawing upon the collective expertise of the respondents, not to test for individual differences between groups or respondents. Graham W. Prescott and Mark A. Snethlage independently coded each of the drivers and reached a consensus on any disagreements. Graham W. Prescott edited the final file.</p> <p>4. kilimanjaro_ipbes_workshop_spatial_scales_recommended_measures.csv</p> <p>11 observations of 6 variables<br> <br> We also conducted a carousel session in which participants could suggest actions and actors that could contribute towards achieving a sustainable future for people and nature at Mt. Kilimanjaro. This file contains the tally of recommended measures arising from this carousel session, grouped by spatial scale and Conservation Measures Partnership (CMP) categories. For full list of measures, see Table S7.</p> <p> </p> <p> </p>
Dataset for "Mass loss of the Greenland ice sheet until the year 3000 under a sustained late-21st-century climate"
<p>Dataset for the paper "Mass loss of the Greenland ice sheet until the year 3000 under a sustained late-21st-century climate" (Journal of Glaciology, <a href="https://doi.org/10.1017/jog.2022.9">https://doi.org/10.1017/jog.2022.9</a>).</p> <p>Please see the README for details.</p> <p>V1.2: Now providing compressed netCDF files with reduced size.<br>V1.1: Scalar flux variable 'dlimdt' (total ice mass change) added. README updated.<br>V1: Initial upload.</p> <p>* * * * * * *</p> <p>Users should cite the original publication when using all or parts of these data.</p>
Data for "Paris Agreement requires substantial, broad, and sustained policy efforts beyond COVID-19 recovery packages"
<p>This dataset contains the underlying data for the following publication: Tanaka, K., C. Azar, O. Boucher, P. Ciais, Y. Gaucher, D. J. A. Johansson (2022) Paris Agreement requires substantial, broad, and sustained policy efforts beyond COVID-19 public stimulus packages. <em>Climatic Change</em> <strong>172, </strong>1 (2022). https://doi.org/10.1007/s10584-022-03355-6</p> <p>Earlier manuscripts were published as a preprint. https://arxiv.org/abs/2104.08342</p>
Dataset for "Mass loss of the Antarctic ice sheet until the year 3000 under a sustained late-21st-century climate"
<p>Dataset for the paper "Mass loss of the Antarctic ice sheet until the year 3000 under a sustained late-21st-century climate" (Journal of Glaciology, <a href="https://doi.org/10.1017/jog.2021.124">https://doi.org/10.1017/jog.2021.124</a>).</p> <p>Please see the README for details.</p> <p>V2: New version with ISMIP6-type variables and compressed netCDF files.<br> V1: Initial upload.</p> <p>* * * * * * *</p> <p>The following script may be used to download the entire content of the archive on a Unix/Linux system:</p> <p>#!/bin/bash<br> # --- download_all.sh ---<br> wget https://zenodo.org/record/6215117/files/_README.pdf<br> wget https://zenodo.org/record/6215117/files/run_specs_headers.zip<br> for aexp in hist ctrl_proj_long \<br> exp05_long exp06_long exp07_long exp08_long exp09_long exp10_long \<br> exp12_long exp13_long \<br> expA5_long expA6_long expA7_long expA8_long \<br> expB6_long expB7_long expB8_long expB9_long expB10_long; do<br> wget https://zenodo.org/record/6215117/files/${aexp}.zip<br> done<br> for aexp in abuc_long abuciso_long \<br> abuk_long abukiso_long \<br> abum_long abumiso_long; do<br> wget https://zenodo.org/record/6215117/files/${aexp}.zip<br> done</p> <p>* * * * * * *</p> <p>Users should cite the original publication when using all or parts of these data.<br> </p>
Evaluating the Sustainability Impacts of Intelligent Carpooling Systems for SOV Commuters in the Atlanta Region
<p>This data repository provides the data used in the following research project supported by an NCST (UTC) grant: Evaluating the Sustainability Impacts of Intelligent Carpooling Systems for SOV Commuters in the Atlanta Region.</p> <p>Please refer to Data Format and Content in the research report for meta-data information</p> <p>1. ABM Trip Table (Data Set 1):<br> CarpoolSim_input.csv<br> 2. Traffic Analysis Zones (TAZ) Shapefiles (Data Set 2):<br> TAZ+ECs_TAZ.zip<br> 3. Employment Center Shapefiles (Data Set 3):<br> TAZ+ECs_TAZ.zip<br> 4. Shortest Paths (Data Set 4):<br> path_retention.zip<br> 5. Analytical Outputs (Data Set 5):<br> summ_stats_ind.csv<br> summ_stats.csv</p>
Maps of the Sustainable Development Goal (SDG) indicator 15.3.1 with its sub-indicators for the entire Amazon River Basin
<p>Maps of the SDG indicator 15.3.1 adopted by the United Nations Convention to Combat Desertification (UNCCD) together with its sub-indicators for the Amazon River Basin for the period 2001-2020. The sub-indicators are trajectory (or trend), state, and performance. The SDG indicator 15.3.1 was calculated using the procedures described in the second version of the Good Practice Guidance for SDG Indicator 15.3.1. The annual LCLU maps from the MapBiomas project at 30 m spatial resolution and the 16-day MOD13Q1 NDVI and SoilGrids dataset were used as inputs. In addition, annualized maps of drought severity derived from SPI12, SPEI12, and scPDSI are added. </p> <p>A total of seven GeoTIFF files in Geographic Tagged Image File Format (GeoTIFF) format are provided at 250 m spatial resolution.</p> <p>Coding for the SDG indicator 15.3.1, trajectory, state, and performance.</p> <p>-32768 is ‘No data’</p> <p>-1 is ‘Degraded’</p> <p>0 is ‘Stable’’</p> <p>1 is ‘Improvement’</p> <p>Coding for the drought severity.</p> <p>From 0 (minimum drought severity) to 1 (maximum drought severity).</p>
Corpus and list of keywords from Improving sustainable crop protection using population genetics concepts
<p>Corpus extracted in April 2021 from the ISI Web of Science portal (https://www.webofscience.com) with the following request: ‘Plant AND Resistan* AND Durab*’. A first corpus of 2522 articles was built considering all publication years for this extraction. This collection was then refined by categories to remove articles outwith the scope of our search (e.g. related to durable resistant materials for constructions). We also kept only articles cited at least once. The final corpus was composed of 1783 articles from 1979 to 2021:</p> <ul> <li>CORPUS_plant_resistance_durability.zip</li> </ul> <p>List of keywords used for the network presented in the article:</p> <ul> <li>keywords_list.csv</li> </ul>
Projects and Organizations in Open Sustainable Technology
<p><strong>A curated database of open technology projects and organisations working for a stable climate, energy supply and natural resources. The dataset was created by the <a href="https://opensustain.tech/">Open Sustainable Technology Initiative</a></strong></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 of the paper "Vermicomposting as a sustainable option for the management of the biomass of the invasive tree Acacia dealbata Link."
<p>Data generated during an experiment of vermicomposting of <em>Acacia dealbata</em> fresh biomass. Four files are included: "<strong>vermicompost_and_earthworm_data.csv</strong>" and "<strong>readme.csv</strong>" are the raw data of different parameters measured in vermicompost samples during the vermicomposting of <em>Acacia dealbata</em> by the earthworm <em>Eisenia andrei</em> and an explanation of each parameter and the unit in which the parameter is expressed. </p> <p>"<strong>germination test.csv</strong>" and "<strong>radicle_length.csv</strong>" are the results of an ecotoxicological test on the effect of <em>A. dealbata</em> biomass and vermicompost on the germination and radicle elongation in <em>Lepidium sativum</em>.</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.