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8,375 results for “nationalism”
Human activities data in Wolong National Nature Reserve
<p>Wolong National Nature Reserve (hereafter Wolong) is an internationally renowned giant panda (<em>Ailuropoda melanoleuca</em>) reserve. Meanwhile, the reserve is also a popular tourist destination in Giant Panda National Park. It encompasses two major towns, Wolong and Gengda, home to approximately 5,000 residents. Agriculture, tourist economy, and livestock grazing remain important income sources for the residents. Here, we combine ongoing survey data on human activity areas in Wolong and make two layers of major human activities in Wolong. The two datasets are human pressure and livestock grazing. Among them, the human pressure layer combines roads and settlement data. We use this dataset to reflect the extent of human activities in Wolong and as an important indicator to assess its interference with wildlife.</p>
Fig 6A in Diversity and distribution of scleractinian corals from Mandapam group of Islands in Gulf of Mannar marine national park, South East coast of India
Fig 6A: D Acropora sp. E - G Montipora sp. H - Tubastreae coccinea
An Evaluation of the Nigerian National Information Infrastructure and E-Governance
<p>Nigeria has the fastest growing and most lucrative ICT market in Africa (which fellow emerging economies like South Africa, India, Malaysia and Singapore are making the most of); yet in spite of this significant progress is still being ranked low in e-government provision to its citizens. The need for national information infrastructure cum e-governance framework as a national imperative for a 21st Century Nigeria cannot be overemphasized. National information infrastructure and e-governance policy engineering is important in achieving Vision 2020 and the mission of re-branding and transforming Nigeria. Unless the significance of e-governance is recognized and made the engine room of nation-building, economic and social developments aims in our country may become unattainable. Soonest, intellectual capital, not mineral resources like oil, would be the most strategic and valuable global resource. This paper therefore evaluates and recognizes with great concern that the current national information infrastructure and e-governance readiness framework in Nigeria is grossly inadequate-especially in the areas of National Power Generation and Supply (as the government fixes June 1st as the take-off of new electricity tariff). It highlights the bureaucracies and politics that hamper speedy development of our national information infrastructure and e-governance in comparison to what is obtainable in other evolving economies. The study investigates the limitations and deficiencies of our national information infrastructure and e-governance. The study examines areas of paucities which have contributed to the low e-service delivery in the country despite the success recorded in the country’s ICT and telecommunications sector and finally suggests how this situation may be improved for an enviable national information infrastructure and e-governance as a national imperative for 21st century Nigeria of our dream.</p>
Scaling up video: from ePLANET region to national context
<p><span>The ePLANET scaling-up videos have been designed as a key material to highlight the added value of using ePLANET platform and governance strategies within the pilot territories, and so attract other local and regional authorities at national level on the adoption of ePLANET outcomes.</span></p> <p><span>In total, 3 scaling-up videos have been produced, each of them tailored to the specific states of pilot regions. In particular:</span></p> <ul> <li><span>Czech Republic (Zlín region pilot)</span></li> <li>Catalonia (Girona region pilot)</li> <li>Greece (Crete island pilot)</li> </ul> <p><span>The 3 videos have a common initial section, combining motion graphics together with a voice-over presentation (in the national language of each pilot region), followed by a tailored section with two short testimonials from the addressed pilot region.</span></p> <p><span>The common initial section has the format of a storytelling animated by motion-graphics. The storytelling highlights the added value of collaboration amongst local authorities to improve the capacities, decision-making and investment opportunities related to the deployment of Energy Action plans.</span></p> <p> </p>
Fig. 4 in The correlations between certain features of the journal Neotropical Ichthyology and its impact factor: a comparative analysis at the thematic and national levels
Fig. 4. Uncitedness rate of articles published in the Brazilian journals in Sample 2.
Fig. 1 in The correlations between certain features of the journal Neotropical Ichthyology and its impact factor: a comparative analysis at the thematic and national levels
Fig. 1. Brazilian journals and their corresponding self-cited rates between 2006 and 2011.
Fig. 2 in The correlations between certain features of the journal Neotropical Ichthyology and its impact factor: a comparative analysis at the thematic and national levels
Fig. 2. Brazilian journals and their corresponding self-citing rates between 2006 and 2011.
Burned areas dataset for the enclaves of grasslands and savannah of the Mapinguari National Park (Amazonas, Brazil)
<p><span>The present dataset includes annual mapping of fire scars for the enclaves of grasslands and savannah of the Mapinguari National Park (Amazonas, Brazil), at 30-meter spatial resolution, for the period 2000-2023. The enclave areas occupy a total of 241,000 hectares.</span></p> <p><span>The detection of burned areas was carried out using Burned Area Mapping (BAMS) algorithm (Bastarrika et al, 2014), followed by the performance of visual supervision processes and the use of active fire products to optimize the detection date of each fire event. The algorithm is applied to the Surface Reflectance series of Landsat (TM, ETM+, OLI and OLI-2) – Collection II, accessed using Google Earth Engine (Gorelick et al, 2017). Data from active fire products MCD14DL V006, VIIRS-NPP, VIIRS-NOA20 and GOES16, as well as burned area data from product MCD64A1 v006, were used to optimize the detection date of each fire scar.</span></p> <p><span>_________________________________________________________________________________________________________</span></p> <p><span>July 08, 2023 – The version 1.0 includes the period 2000-2023, with a total of 356,688.5 hectares of fire affected areas, distributed across 453 fire scars.</span></p> <p><span>_________________________________________________________________________________________________________</span></p> <p><span>The files available include: </span></p> <p><span>i) “fire_scars_dataset.rar”: annual burned areas vector files, in shapefile format (*.shp), projected at WGS84 UTM 20S. Each observation is an individual fire scar, with his attribute table indicating:</span></p> <p><span>- ID: Identification number for each;</span></p> <p><span>- area_ha - area of each fire scar, calculated in hectares.</span></p> <p><span>- date - detection date of the fire scar</span></p> <p><span>- date_preci - precision flag of the fire detection date:</span></p> <p><span>0 - fire date detected using only Landsat data</span></p> <p><span>1 - fire date optimized based on active fires dataset (MCD14DL V006; VIIRS-NPP; VIIRS-NOA20; or GOES16)</span></p> <p><span>ii) “enclaves_Mapinguari_National_Park.rar”: vector file of study area location, in shapefile format (*.shp), projected at WGS84 UTM 20S. Includes 8 enclaves of grasslands and savannah situated inside Mapinguari National Park.</span></p> <p><span>iii) “dataset_description.pdf”: description of the dataset.</span></p> <p><span>_________________________________________________________________________________________________________</span></p> <p><span>We thank the Conselho Nacional de Pesquisa e Desenvolvimento (CNPq) and the Instituto Chico Mendes de Conservação da Biodiversidade (ICMBIO) (process number 126772/2022-3) for the grant conceded to the second and third authors.</span></p> <p><span>_________________________________________________________________________________________________________</span></p> <p> </p> <p><span>References</span></p> <p> </p> <p><span>Bastarrika, Aitor, Maite Alvarado, Karmele Artano, Maria Pilar Martinez, Amaia Mesanza, Leyre Torre, Rubén Ramo, and Emilio Chuvieco. 2014. “BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data.” Remote Sensing 6: 12360–80. https://doi.org/10.3390/rs61212360.</span></p> <p><span>Gorelick, Noel, Matt Hancher, Mike Dixon, Simon Ilyushchenko, David Thau, and Rebecca Moore. 2017. “Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone.” Remote Sensing of Environment 202: 18–27. https://doi.org/10.1016/j.rse.2017.06.031.</span></p>
Microhabitat selection of the big-headed turtle Platysternon megacephalum in the Hainan Tropical Rainforest National Park, China
<p>Understanding species habitat requirements is vital for ensuring the success of targeted conservation and habitat restoration measures. The big-headed turtle (<em>Platysternon megacephalum</em>)<em> </em>is a freshwater species which is distributed across Southeast Asia. Due to the human threats posed by illegal pet trade and overharvesting for food and medicinal purposes, the species has undergone rapid decline. Furthermore, limited research has been conducted on this species, particularly regarding habitat preferences on Hainan Island, China. Therefore, this study examined the microhabitat selection of <em>P. megacephalum</em> using cage and sample plot methods in the Diaoluo Mountain area of the Hainan Tropical Rainforest National Park. Our results indicated that big-headed turtles select stream microhabitats at higher altitudes, in proximity to rocky substrates, several caves, and high diversity of food sources. Microhabitat utilization did not differ significantly between adults and juveniles. This suggests that protecting microhabitats and main food sources is important for the conservation of <em>P. megacephalum</em>. Our findings provide a reference for the protection of this species in Jianfeng, Yingge Ridge, and other areas in the Hainan Tropical Rainforest National Park.</p>
Lemonade Creek, Yellowstone National Park, USA - Microbial Community Analysis - Metabolomics Data
<p>Polar metabolomics data (targeted and untargeted) used for analysis of microbial community function over a diurnal cycle in Lemonade Creek, Yellowstone National Park, USA.</p> <p> </p> <p><code>GNPS_positive-2.xlsx</code> Comparison of GNPS data used for main metabolite analysis with targeted metabolite features. Done to support the accuracy of the GNPS results for metabolites identified outside the targeted set.</p> <p> </p> <p><code>NEG_506963_FinalEMA-HILIC_Identifications.xlsx</code> Negative ionization targeted metabolite identification quality and confidence results (prepared by the Joint Genome Institute, USA).</p> <p><code>NEG_msms_mirror_plots.tar.gz</code> Negative ionization targeted metabolite mirror plots.</p> <p><code>NEG_peak_height.tab</code> Negative ionization targeted metabolite peak height file (main results file used for abundance analysis).</p> <p><code>POS_506963_FinalEMA-HILIC_Identifications.xlsx</code> Positive ionization targeted metabolite identification quality and confidence results (prepared by the Joint Genome Institute, USA).</p> <p><code>POS_msms_mirror_plots.tar.gz</code> Positive ionization targeted metabolite mirror plots.</p> <p><code>POS_peak_height.tab</code> Positive ionization targeted metabolite peak height file (main results file used for abundance analysis).</p> <p> </p> <p><code>NEG_peak_height.csv</code> Negative ionization untargeted metabolite peak height file (main results file used for abundance analysis).</p> <p><code>POS_peak_height.csv</code> Positive ionization untargeted metabolite peak height file (main results file used for abundance analysis).</p>
Figure 5 in Seasonal distribution and habitat use preference of Barking deer (Muntiacus vaginalis) in Murree-Kotli Sattian-Kahuta National Park, Punjab Pakistan
Figure 5. Slopes of occupied locations of Barking deer habitat.
Figure 1 in Seasonal distribution and habitat use preference of Barking deer (Muntiacus vaginalis) in Murree-Kotli Sattian-Kahuta National Park, Punjab Pakistan
Figure 1. Distribution of Barking deer in study area.
Figure 7 in Seasonal distribution and habitat use preference of Barking deer (Muntiacus vaginalis) in Murree-Kotli Sattian-Kahuta National Park, Punjab Pakistan
Figure 7. Aspects of occupied locations of Barking Deer in study area.
Figure 3 in Seasonal distribution and habitat use preference of Barking deer (Muntiacus vaginalis) in Murree-Kotli Sattian-Kahuta National Park, Punjab Pakistan
Figure 3. Plant species recorded in summer from habitat of Barking deer.
Figure 6 in Seasonal distribution and habitat use preference of Barking deer (Muntiacus vaginalis) in Murree-Kotli Sattian-Kahuta National Park, Punjab Pakistan
Figure 6. Coarse topography / habitat characteristics at occupied locations of Barking deer.
Figure 4 in Seasonal distribution and habitat use preference of Barking deer (Muntiacus vaginalis) in Murree-Kotli Sattian-Kahuta National Park, Punjab Pakistan
Figure 4. Plant species recorded in winter from habitat of Barking deer.
Fig. 13 in Haliplidae, Noteridae, Dytiscidae (Coleoptera) du Gabon (12 partie). Parc National Moukalaba - Doudou (mission 2014) et la zone au nord en dehors du Parc
Fig. 13 - Rivière de la Mort (site 1).
Fig. 18 in Noteridae, Dytiscidae (Coleoptera) du Gabon (11ème partie). Parc National Monts Birougou (mission 2016)
Fig. 18 - Bord du lac de barrage. / Riva del lago di sbarramento. / Bank of barrage lac.
Fig. 16 - Site 4.3 in Noteridae, Dytiscidae (Coleoptera) du Gabon (11ème partie). Parc National Monts Birougou (mission 2016)
Fig. 16 - Site 4.3 rivière Lémianga. / Fiume Lémianga. / Small river Lémianga.
Fig. 17 in Noteridae, Dytiscidae (Coleoptera) du Gabon (11ème partie). Parc National Monts Birougou (mission 2016)
Fig. 17 - Lac de barrage. / Lago di sbarramento. / Barrage lac.
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.