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1,751 results for “Future”
Data from: Hydrology induces intraspecific variation in freshwater fish morphology under contemporary and future climate scenarios
<p>Datasets for manuscript "Andres, K. J., Chien, H., and Knouft, J. H. Hydrology induces intraspecific variation in freshwater fish morphology under contemporary and future climate scenarios. Science of the Total Environment. <a href="https://doi.org/10.1016/j.scitotenv.2019.03.292">https://doi.org/10.1016/j.scitotenv.2019.03.292</a>"</p> <p>landmarks.zip: landmarks digitized on images of 1081 specimens using TpsDig2 software.</p> <p>streamflow_estimates.csv: Contemporary (1980-2009) and future (2070-2099) streamflow estimates [avg: average annual streamflow discharge (m3 s-1); cv: coefficient of variation of annual discharge] in sub-basins containing populations of 6 minnow species in IL, USA</p>
Supporting data for "Arctic sea ice response to flooding of the snow layer in future warming scenarios"
<p>Supporting data for "Arctic sea ice response to flooding of the snow layer in future warming scenarios" submitted to Earth's Future in April 2021</p> <p>Contains model output from both the Icepack and CCSM4 experiments from the paper. File descriptions for the Icepack and CCSM4 data are contained in the files README_icepack and README_CCSM respectively.</p>
Data set for the PLOS ONE paper Expecto transitio: Exploring non-experts' techno-economic expectations of the energy future
<p>Raw data file (SPSS and .cv versions) consisting of all data used for the Plos One publication.</p> <p> </p>
ESR-thermochronometry of the Hida range of the Japanese Alps: Validation and future potential
<p><strong>Supplementary data for the above titled paper published in <em>Geochronology</em>. </strong></p> <p><strong>Contents:</strong></p> <p>Raw OSL data, Raw ESR data, DRAC dose rate input spreadsheet.</p> <p><strong>OSL-thermochronometry Raw Data.</strong></p> <p>KRG16-05</p> <p>KRG16-06</p> <p>KRG16-101</p> <p>KRG16-104</p> <p>KRG16-111</p> <p>KRG16-112<br> </p> <p><strong>ESR-thermochronometry Raw Data.</strong></p> <p>KRG16-05 Al & Ti-centres</p> <p>KRG16-06 Al & Ti-centres</p> <p>KRG16-101 Al & Ti-centres</p> <p>KRG16-104 Al & Ti-centres (4.3 kGy isothermal holding experiment)</p> <p>KRG16-104 Al & Ti-centres (2.15 kGy isothermal holding experiment)</p> <p>KRG16-111 Al & Ti-centres</p> <p>KRG16-112 Al & Ti-centres</p> <p><strong>Dosimetry data.</strong></p>
Fig. 2 in Broad tapeworms (Diphyllobothriidae), parasites of wildlife and humans: Recent progress and future challenges
Fig. 2. Microphotographs of permanent slides of diphyllobothriid tapeworms. A – Dibothriocephalus alasensis from Canis familiaris, Hooper Bay, Alaska, March 18, 1958; fixed after relaxation by R. Rausch (MSBP 17029). B – Dibothriocephalus latus from Homo sapiens, Chile, 19 November 2012; contracted clinical sample fixed with 'cold' fixative by T. Weitzel. C – Dibothriocephalus dalliae from C. familiaris, Alaska, 5 November 1970; fixed after relaxation by R. Rausch (MSBP 26232). D – Diphyllobothrium lanceolatum from Erignathus barbatus, Greenland, 7 October 1987; collected by P. Baagoe (SNM). E – Dibothriocephalus cf. nihonkaiensis from Homo sapiens, Newtok, Alaska, 26 March 1967; fixed after relaxation by R. Rausch (MSBP 26244). F – Diphyllobothrium stemmacephalum from Lagenorhynchus acutus, Wellfleet Bay, Cape Cod, Massachusetts, 1998; collected by J.N. Caira. G – Diphyllobothrium mobile from Ommatophoca rossii, Antarctica, 11 August 1901, Deutsche Südpolar- Expedition; collected by E. Dagobert von Drygalski (ZNB 5188). H – Dibothriocephalus ursi from Ursus arctos middendorfi, Karluk Lake, Kodiak Island, 9 October 1952; fixed after relaxation by R. Rausch; paratype (MSBP 3269). I – Schistocephalus sp. from C. familiaris, Newtok, Alaska, 4 April 1958; fixed after relaxation by R. Rausch (MSBP 17939). Acronyms of museum collections: MSBP – Museum of Southwestern Biology, Division of Parasitology, University of New Mexico, Albuquerque, New Mexico, U.S.A.; SNM – Swedish Museum of Natural History, Stockholm, Sweden; ZNB – Zoologische Museum Berlin, Berlin, Germany.
Text-fig. 1 Associate Professor RNDr. Václav Ziegler, CSc. is speaking and teaching during palaeontological excursion with students from Faculty of Education, Charles University - future teachers in Kutná Hora area. (photo: Marek, J.: 2006) in Václav Ziegler Septagenarian
Text-fig. 1 Associate Professor RNDr. Václav Ziegler, CSc. is speaking and teaching during palaeontological excursion with students from Faculty of Education, Charles University - future teachers in Kutná Hora area. (photo: Marek, J.: 2006)
Figure 4 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method
Figure 4. Mean suitability for Arborophila crudigularis under baseline climate conditions and future climate scenarios. cccma and csiro represent two general circulation models; RCP2.6, and RCP8.5 represent two greenhouse gas emission scenarios; EN is entire suitable habitat; PR is presence records.
Figure 2 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method
Figure 2. Performance of each model for predicting the suitable habitat for Arborophila crudigularis. GLM: Generalized linear model; GBM: generalized boosting model; GAM: generalized additive model; CTA: classification tree analysis; ANN: artificial neural network; FDA: flexible discriminant analysis; MARS: multiple adaptive regression splines; RF: random forest; MAXENT: maximum entropy model.
Figure 6 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method
Figure 6. Changes in suitable habitat for Arborophila crudigularis under the RCP8.5 emission scenario. cccma and csiro represent two general circulation models.
Figure 5 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method
Figure 5. Changes in suitable habitat for Arborophila crudigularis under the RCP2.6 emission scenario. cccma and csiro represent two general circulation models.
Past, present and future rainfall erosivity in Northwestern Europe
<p>Past, present and future rainfall erosivity in Northwestern Europe calculated from convection-permitting climate simulations in CNRM-AROME (Lucas-Picher et al., 2023; <a href="https://doi.org/10.1007/s00382-022-06637-y">https://doi.org/10.1007/s00382-022-06637-y</a>) using emission scenario RCP 8.5. A description of the methodology is given in the article "Past, present and future rainfall erosivity in central Europe based on convection-permitting climate simulations" by Magdalena Uber et al. (2024) in Hydrology and Earth System Sciences (<a href="https://doi.org/10.5194/hess-28-87-2024">https://doi.org/10.5194/hess-28-87-2024</a>). Please see the README-file for further information.</p> <p>This work was funded by the German Federal Ministry for Digital and Transport in the framework of the DAS-Basisdienst.</p>
Figure 1 in Positioning entomopathogenic nematodes for the future viticulture: exploring their use against biotic threats and as bioindicators of soil health
Figure 1. Example of the progression of authorized phytosanitary product usage in Spain against the most important diseases and pests of vineyards during the last decade. The size of each circle is proportional to the total number of phytosanitary authorized against each biotic threat.1
Fig. 3 in Does prior feeding behavior by previous generations of the maize weevil (Coleoptera: Curculionidae) determine future descendants feeding preference and ovipositional suitability?
Fig. 3. Mean (SE) number of S. zeamais (n = 8,000) attracted to corn, barley, brown rice, and white rice, with a 200 µL Eppendorf tube containing the pheromone lure placed in barley. Means with the same letter are not significantly different.
Fig. 6 in Does prior feeding behavior by previous generations of the maize weevil (Coleoptera: Curculionidae) determine future descendants feeding preference and ovipositional suitability?
Fig. 6. Number (SE) of S. zeamais male and female (n = 200) emergence when reared on the individual host grains: corn, barley, brown rice, and white rice. Means with the same letter are not significantly different.
Fig. 5 in Does prior feeding behavior by previous generations of the maize weevil (Coleoptera: Curculionidae) determine future descendants feeding preference and ovipositional suitability?
Fig. 5. Mean (SE) number of S. zeamais (n = 8,000) attracted to corn, barley, brown rice, and white rice, with a 200 µL Eppendorf tube containing the pheromone lure placed in brown rice. Means with the same letter are not significantly different.
Fig. 2 in Does prior feeding behavior by previous generations of the maize weevil (Coleoptera: Curculionidae) determine future descendants feeding preference and ovipositional suitability?
Fig. 2. Mean (SE) number of S. zeamais (n = 8,000) attracted to corn, barley, brown rice, and white rice, with a 200 µL Eppendorf tube containing the pheromone lure placed in corn. Weevils were reared on corn, barley, brown rice, and white rice, then presented with a choice of 4 host grains. Means with the same letter are not significantly different.
Fig. 1 in Does prior feeding behavior by previous generations of the maize weevil (Coleoptera: Curculionidae) determine future descendants feeding preference and ovipositional suitability?
Fig. 1. Mean (SE) number of S. zeamais (n = 8,000) attracted to corn, barley, brown rice, and white rice. Weevils were reared on corn, barley, brown rice, and white rice, then presented with a choice of 4 host grains. Means with the same letter are not significantly different.
Fig. 4 in Does prior feeding behavior by previous generations of the maize weevil (Coleoptera: Curculionidae) determine future descendants feeding preference and ovipositional suitability?
Fig. 4. Mean (SE) number of S. zeamais (n = 8,000) attracted to corn, barley, brown rice, and white rice, with a 200 µL Eppendorf tube containing the pheromone lure placed in white rice. Means with the same letter are not significantly different.
Fig. 3 in Advertisement calls of six glassfrog species in the Colombian Andes, and comments on priorities for future research and conservation
Fig. 3. Counts of glassfrog species for those with and without the call described, and categorized by: (A) IUCN conservation status categories; (B) Endemism or distribution range (see details in Materials and Methods); and (C) Occurrence in National Protected Areas.
Fig. 2 in Advertisement calls of six glassfrog species in the Colombian Andes, and comments on priorities for future research and conservation
Fig. 2. Spectrograms (top) and oscillograms (bottom) of the described glassfrogs advertisement calls. All calls are displayed at Blackman window (length = 512) and 80% overlap, at the same frequency range, and all but S. punctulata and C. huilensis are displayed at the same temporal scale.
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.