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100 results for “knowledge management”
Knowledge base for NBS for water treatment and stormwater management
<p>Five tables containing:</p> <ol> <li>nbs_catalog.csv: A catalogue of nature-based solutions for wastewater treatment and stormwater management. For each solution there is information on its performance, types of water, cobenefits, barriers and cost.</li> <li>sci_publications.csv: A list of scientific publications focused on one or several technologies of the above catalogue.</li> <li>sci_publications_treatment_details: For solutions for water treatment, a second table containing data about treatment performance extracted from previous scientific publications.</li> <li>description_nbs_catalog.csv: Descriptors for the catalogue.</li> <li>description_sci_publications_treatment_details.csv: Descriptors for the treatment performance data.</li> </ol> <p>The most updated version of each table can be queried from https://snappapi-v2.icradev.cat/</p>
Database of permacultural adoption responses in Mexicali, BC, Mexico. based on Circular Economy, Knowledge Management, and Sustainability policies
<p>Database documenting the perspectives of citizens in Mexicali, Baja California, Mexico, regarding the adoption of permaculture practices. The study is analyzed through the lenses of Knowledge Management, Circular Economy, and Sustainability Policies. The data was collected during the summer of 2024. </p>
Figure 2: Direct and indirect paths of knowledge transfer to New Zealand to manage sand drifting in the nineteenth and twentieth centuries.
<p>Figure 2 of article: Managing Coastal Sand Drift in the Anthropocene: A Case Study of the Manawatū-Whanganui Dune Field, New Zealand, 1800s–2020s</p> <p>DOI zenodo: 10.5281/zenodo.5075980</p>
Knowledge gaps on trade-offs of soil carbon sequestration related to soil management strategies
<p>The database contains 87 unique literature items (29 reviews, 42 meta-analyses, 16 original papers) describing the effect of a soil management strategy (tillage management, cropping systems, water management, cover crops, crop residues, livestock manure, slurry, compost, biochar, liming) on the trade-offs between soil carbon sequestration or SOC change and N2O emission, CH4 emission and nitrogen leaching. Since some literature items describe effects of several SMS categories, the database_summary tab comprises a total of 112 unique inputs. For each input it is indicated in the Database_summary tab if it was used as input for the "Soil management effect assessment" in Maenhout et al. (2024) [Maenhout, P., Di Bene, C., Cayuela, M. L., Diaz-Pines, E., Govednik, A., Keuper, F., Mavsar, S., Mihelic, R., O'Toole, A., Schwarzmann, A., Suhadolc, M., Syp, A., & Valkama, E. (2024). Trade-offs and synergies of soil carbon sequestration: Addressing knowledge gaps related to soil management strategies. European Journal of Soil Science, 75(3), e13515. https://doi.org/10.1111/ejss.13515] and/or to define knowledge gaps ("Knowledge gap in tab"-column). Knowledge gaps and research recommendations are gouped per soil management strategy in different tabs in this database. Per soil management strategy, knowledge gaps are clustered per theme in groups. These themes include: the specific soil management strategy, pedoclimatic conditions, establishment of experiments, other soil management strategies, meta-analysis, modelling and other</p>
Figure 4 in The Middle Eastern Biodiversity Network: Generating and sharing knowledge for ecosystem management and conservation
Figure 4. Joint field research in Yemen: Project participants sample fish in Socotra Island for studies of connectivity among populations. Th e results are important for fisheries management (photo U. Zajonz).
Figure 3 in The Middle Eastern Biodiversity Network: Generating and sharing knowledge for ecosystem management and conservation
Figure 3. Collection management and museum curatorship training workshop: Participants sampling biological specimens aboard the RV "Senckenberg" (photo N. Manasfi).
Figure 2 in The Middle Eastern Biodiversity Network: Generating and sharing knowledge for ecosystem management and conservation
Figure 2. Red Sea coral reef: Th e Red Sea is the enclosed sea with the highest biodiversity on Earth (photo F. Krupp).
Figure 1 in The Middle Eastern Biodiversity Network: Generating and sharing knowledge for ecosystem management and conservation
Figure 1. Terrestrial biodiversity in the Middle East is strongly influenced by seasonality: Desert area in northern Saudi Arabia after the winter rainfall (photo F. Krupp).
Figure 3 in Biology of jungle perch, Kuhlia rupestris, identification of threats and knowledge gaps to improve local and global management
Figure 3. – Life cycle of K. rupestris and conservation issues. Within the cycle (blue area), the solid black line corresponds to the freshwater life phase. The lower and upper grey discontinuous lines correspond to the larval marine and estuarine phases of the species respectively. Outside the cycle, in orange, the known and suspected threats on the different life phases of the species (Gelineau et al., modified, 2015).
Figure 1 in Biology of jungle perch, Kuhlia rupestris, identification of threats and knowledge gaps to improve local and global management
Figure 1. – Distribution of K. rupestris in the Indo-Pacific zone (modified from Feutry, 2012a). In dotted line, the presumed natural range of the species and in solid lines, localities with a high likelihood of occurrence or known occurrence.
Fig. 7 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 7. Ordination plot of the correspondence analysis (first two axes) based on fishermen's answers about fishing season of the nine studied fish species in the southeastern Brazilian coast: Absa = Abudefduf saxatilis; Boru = Bodianus rufus; Cala = Caranx latus; Epma = Epinephelus marginatus; Haau = Haemulon aurolineatum; Heba = Hemiramphus balao; Kysp = Kyphosus spp.; Mifu = Micropogonias furnieri; Sesp = Seriola spp.
Fig. 6 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 6. Ordination plot of the correspondence analysis (first two axes) based on fishermen's answers about migratory routes of the nine studied fish species in the southeastern Brazilian coast: Absa = Abudefduf saxatilis; Boru = Bodianus rufus; Cala = Caranx latus; Epma = Epinephelus marginatus; Haau = Haemulon aurolineatum; Heba = Hemiramphus balao; Kysp = Kyphosus spp.; Mifu = Micropogonias furnieri; Sesp = Seriola spp.
Fig. 4 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 4. Trophic chain based on those food items and predators most cited by fishermen in the southeastern Brazilian coast for a) reef fishes and b) pelagic fishes. Numbers are percent of interviewed fishermen who mentioned each feeding interaction. Fish sizes are not in scale. Those feeding interactions that agree with reported feeding habits of these fishes in the biological literature are marked *(Randall, 1967; Berkeley & Houde, 1978; Menezes & Figueiredo, 1980; Sazima, 1986; Pipitone & Andaloro, 1995; Barreiros & Santos, 1998; Vasconcellos & Gasalla, 2001; Silvano, 2001; Silvano & Güth, 2006; Figueiredo & Vieira, 2005; Gibran, 2007).
Fig. 3 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 3. Main habitats of fishes according to fishermen in the southeastern Brazilian coast: percentages of fishermen who mentioned each habitat category are in Appendix 1. Double-headed arrows indicate that fishes occur in both habitats in horizontal space (e.g. open ocean and reefs), up and down arrows indicate that fishes occur in both habitats in vertical space (e.g., near the bottom and at the surface). Fish sizes are not in scale.
Fig. 1 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 1. Ordination plot of the correspondence analysis (first two axes) based on fishermen answers about uses of the nine studied fish species in the southeastern Brazilian coast: Absa = Abudefduf saxatilis; Boru = Bodianus rufus; Cala = Caranx latus; Epma = Epinephelus marginatus; Haau = Haemulon aurolineatum; Heba = Hemiramphus balao; Kysp = Kyphosus spp.; Mifu = Micropogonias furnieri; Sesp = Seriola spp.
Fig. 2 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 2. Ordination plots of the correspondence analysis (first two axes) based on fishermen answers about fishing methods and baits of the nine studied fish species in the southeastern Brazilian coast: Absa = Abudefduf saxatilis; Boru = Bodianus rufus; Cala = Caranx latus; Epma = Epinephelus marginatus; Haau = Haemulon aurolineatum; Heba = Hemiramphus balao; Kysp = Kyphosus spp.; Mifu = Micropogonias furnieri; Sesp = Seriola spp.
Fig. 8 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 8. Ordination plot of the correspondence analysis (first two axes) based on fishermen's answers about reproductive (spawning) season of the nine studied fish species in the southeastern Brazilian coast: Absa = Abudefduf saxatilis; Boru = Bodianus rufus; Cala = Caranx latus; Epma = Epinephelus marginatus; Haau = Haemulon aurolineatum; Heba = Hemiramphus balao; Kysp = Kyphosus spp.; Mifu = Micropogonias furnieri; Sesp = Seriola spp.
Fig. 5 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 5. Ordination plot of the correspondence analysis (first two axes) based on fishermen's answers about migratory behavior of the nine studied fish species in the southeastern Brazilian coast: Absa = Abudefduf saxatilis; Boru = Bodianus rufus; Cala = Caranx latus; Epma = Epinephelus marginatus; Haau = Haemulon aurolineatum; Heba = Hemiramphus balao; Kysp = Kyphosus spp.; Mifu = Micropogonias furnieri; Sesp = Seriola spp.
SEAKNOT - SEvere Accident Research and KNOwledge ManagemenT for LWRs
<p>Video presented at the <a href="https://snetp.eu/2023/04/14/read-the-coordinators-hub-day-summary/">SNETP Coordinators’ hub day</a>. This initiative took place in Brussels on March 14th, 2023 as part of the SNETPFORWARD project. The event was co-organized by SNETP. </p>
Figure 1 in Current knowledge of New Caledonian marine and freshwater ichthyofauna, SW Pacific Ocean: diversity, exploitation, threats and management actions
Figure 1. – Location of New Caledonia in the southwest Pacific. Dotted lines: limits of the New Caledonian EEZ, triangles: main seamounts, blue zones: UNESCO world heritage areas, and red hatched zones: fully protected marine areas. Modified from New Caledonian Government, Global seamounts database, The Pew charitable trust.
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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.