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Fig. 3 in The Implementation Of Nature Management Plans For Specially Protected Nature Territories In Latvia: Stakeholder Awareness, Applying Gis Tools, Indicators For Management
Fig. 3. Map of the landscape terrain at Nature park "Dridža ezers" ("Lake Dridža")
Fig. 1 in The Implementation Of Nature Management Plans For Specially Protected Nature Territories In Latvia: Stakeholder Awareness, Applying Gis Tools, Indicators For Management
Fig. 1. Interactive map of recreation sites at Nature park "Dridža ezers" ("Lake Dridža")
Figure 3. Cluster Dendrogram indicating 11 in Composition and structure of plant communities in the Moist Temperate Forest Ecosystem of the Hindukush Mountains, Pakistan
Figure 3. Cluster Dendrogram indicating 11 plant association types in the Lalkoo Valley.
Vegetation greenesss data for the Aït Benhaddou Catchment, Morocco. Includes: 1984-2019 NDVI time series, breakpoint analysis results, and resillience indicator results, among others.
<p>This dataset is comprised of two main parts, both originating from different but related works. </p> <p>The NDVI timeseries and breakpoint analysis were originally developed by Vermeer (2021) for the MSc thesis: Vermeer, A. L. (2021). <em>Ecological stability in the face of climatic disturbances: a case study of a dryland ecosystem in the Moroccan High Atlas Mountains</em>. These data include a harmonized timeseries of Normalized Difference Vegetation Index from different Landsat missions at 30x30 meter resolution for the Aït Benhaddou catchment in Morocco. It also includes the output of a breakpoint analysis that was conducted using this dataset, which showcases different statistical breakpoints in NDVI after a severe drought that occured between 1998 and 2002. Shapefiles, a DEM and masks of irrigiated areas for the catchment are also included. For more information about these data, consult Vermeer (2021).</p> <p>The secondary part of this dataset was produced by Grootoonk (2024) for the MSc thesis: Grootoonk, W. (2024). <em>Relations between temporal resilience indicators and trend breakpoints in a dryland high-mountain catchment, </em>drawing upon the original dataset from Vermeer (2021). These data include Kendall's tau values for the resillience indicators variance and lag-one autocorrelation, computed using a rolling window for each pixel. Results for differerent window sizes (WS) for both indicators are included. </p> <p>Beyond these main results, a number of additional data sources are provided. These are Kendall's tau for precipitation variance in the area, produced using CHIRPS data (https://www.chc.ucsb.edu/data/chirps) and a NSI soil salinity map produced from Landsat imagery. See Grootoonk (2024) for more information. </p>
Genotyping measures and population genetic indices for assesing reproductive modes of polyploid Ludwigia grandiflora subsp. hexapetala in western Europe
<p>Raw data used to assess reproductive modes in 53 sampled populations in western Europe (France and northern Spain).</p> <p><em>Ludwigia grandiflora </em>subsp.<em> hexapetala</em> (<em>Lgh</em>) is a hermaphrodite, polyploid, partially clonal and heteromorphic plant that recently colonized multiple countries worldwide. Individuals in this species are either self-incompatible caused by a late-acting self-incompatible (LSI) system developing long-styled flowers, or self-compatible (SC) developing short-styled flowers. We used a SNP approach allowing confident allele dosage to genotype 53 LSI and SC populations of <em>Lgh</em> in France and northern Spain. We measured their genetic diversity and assessed their reproductive modes using methods adapted to autopolyploid species. </p>
Indicative distribution map for Ecosystem Functional Group T1.3 Tropical/Subtropical montane rainforests
<p>This archive contains indicative distribution maps and profiles for <strong>T1.3 Tropical/Subtropical montane rainforests</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Figure 2 in Newly registered tracks of Raccoon dogs (Nyctereutes procyonoides) indicate the presence of resident population in the region of Bolata dere (NE Bulgaria)
Figure 2. Tracks of a Raccoon dog at the shore of Bolata dere; Scale bar – 10 cm.
Рис. 1. Станции отбора проб зоопΛанктона в Варваринском воΔохраниΛище, 28.07.2022. in Zooplankton as an indicator of water quality in the Varvara Reservoir
Рис. 1. Станции отбора проб зоопΛанктона в Варваринском воΔохраниΛище, 28.07.2022.
PhD Career Choice Indicator
<p>This poster was presented at the ESOF2018 meeting in Toulouse, as part of the researcher career programme. It describes the "PhD Career Choice Indicator", an adapted version of the Holland's Strengths Model, and how to use it to identify potential careers of interest. It is aimed at bioscience doctoral and postdoctoral researchers, but can be adapted to suit all disciplines. </p>
Figure 6 in New host detection of the parasitic mite, Erythraeus pistacicus (Trombidiformes: Erythraeidae) from Iran and indication of possible infection with bacterial symbionts
Figure 6. Haplotype network of Cardinium endosymbionts based on 16S rDNA sequences.
Figure 4 in New host detection of the parasitic mite, Erythraeus pistacicus (Trombidiformes: Erythraeidae) from Iran and indication of possible infection with bacterial symbionts
Figure 4. Haplotype network of Wolbachia endosymbionts based on wsp gene.
Forest quality and land use intensity indicators
<b>Description: </b><p>Trends in Biophysical Vegetation Traits of Tropical Forests under Logging and Fragmentation</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/81"><b>Trends in Biophysical Vegetation Traits of Tropical Forests under Logging and Fragmentation</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=22">here</a></p><p><b>Data worksheets: </b>There are 4 data worksheets in this dataset:</p><ol><li><p><b>Canopy-based forest quality metrics</b> (Worksheet Canopy)</p><p>Dimensions: 213 rows by 6 columns</p><p>Description: Fractional canopy cover and leaf area index</p><p>Fields: </p><ul><li><b>Plot</b>: SAFE project plot number (Field type: Location)</li><li><b>Date</b>: Date images were collected (Field type: Date)</li><li><b>LAI</b>: Leaf area index, corrected for clumping of leaves at plot level (25 m x 25 m) (Field type: Numeric)</li><li><b>fcover</b>: Fractional canopy cover (mean) (Field type: Numeric)</li><li><b>sdfcov</b>: Fractional canopy cover (standard deviation) (Field type: Numeric)</li></ul><br></li><li><p><b>Above-ground biomass</b> (Worksheet AGB)</p><p>Dimensions: 203 rows by 11 columns</p><p>Description: Was derived from DBH and Height of trees for individuals >= 10 cm DBH using five different algorithms on the raw data. We additionally binned heights of trees to account for uncertainties in tree height measurements and used multiple published equations combined with wood density estimates drawn from a distribution of wood density values that differs for unogged, logged and severely logged forest stands. We used oil palm specific equations for biomass estimations in oil palm plots. They will be identical estimates across the five algorithms used. Oil palms have a fundamentally different physical structure to forest trees, so we estimated AGB in oil palm plantations separately using the equation 〖AGB〗_palm= (0.3747*height*100+3.6334)/1000 (Thenkabail et al. 2004). See Pfeifer M, Lefebvre V, Turner E, Cusack J, Khoo M, Chey VK, Peni M, Ewers RMet al. 2015, Deadwood biomass: an underestimated carbon stock in degraded tropical forests?, ENVIRONMENTAL RESEARCH LETTERS, Vol: 10:044019. </p><p>Fields: </p><ul><li><b>Plot</b>: SAFE project plot number (Field type: Location)</li><li><b>Date</b>: Date field plot data were collected (Field type: Date)</li><li><b>AGB_Saner</b>: AGB estimates developed for mixed-species forest stands in East Kalimantan, Indonesia (Field type: Numeric)</li><li><b>AGB_Chave_wet</b>: AGB estimates developed for wet forest (Field type: Numeric)</li><li><b>AGB_Chave_moist</b>: AGB estimates developed for moist forest (Field type: Numeric)</li><li><b>AGB_K09</b>: AGB estimates developed for logged over old growth forest in Malaysian Sabah (Field type: Numeric)</li><li><b>AGB_N10</b>: AGB estimate developed for old growth forest in Malaysia for forests 110 km south-east of Kuala Lumpur (Field type: Numeric)</li><li><b>AGB_Chave14</b>: AGB estimates developed for pantropical forest assuming a wood density of 0.64 (Field type: Numeric)</li><li><b>AGB_Chave14_simulWD</b>: AGB estimates developed for pantropical forest and reflecting disturbance-induced changes to wood density (Field type: Numeric)</li><li><b>AGB_Chave14_Bin5_simulWD</b>: AGB estimates developed for pantropical forest and reflecting disturbance-induced changes to wood density (Field type: Numeric)</li></ul><br></li><li><p><b>SAFE project forest quality scores</b> (Worksheet Quality)</p><p>Dimensions: 203 rows by 4 columns</p><p>Description: Visual assessment of forest disturbance</p><p>Fields: </p><ul><li><b>Plot</b>: SAFE project plot number (Field type: Location)</li><li><b>Date</b>: Date of assessment (Field type: Date)</li><li><b>ForestQuality</b>: SAFE Project forest quality scores (Field type: Ordered Categorical)</li></ul><br></li><li><p><b>Caneye software analyses carried out using Caneye v6.3.8 in August/September 2013</b> (Worksheet LAI_Caneye)</p><p>Dimensions: 237 rows by 16 columns</p><p>Description: LAI, fcover and fAPAR estimates derived from hemispherical images or using Sunscan Delta T device (Cambridge) if applicable</p><p>Fields: </p><ul><li><b>Plotname</b>: SAFE project plot number (Field type: Location)</li><li><b>Date</b>: Date on which photographs were taken (Field type: Date)</li><li><b>HemiUp</b>: Number of sample points = number of pictures taken - upward looking fisheye pictures (Field type: Numeric)</li><li><b>LAI_eff_v6</b>: LAI effective estimated following algorithm of Caneye version 6 (v6.3.8) (Field type: Numeric)</li><li><b>LAI_true_v6</b>: LAI true (accounting for vegetation clumping) estimated following algorithm of Caneye version 6 (v6.3.8) (Field type: Numeric)</li><li><b>LAI_eff_v5</b>: LAI effective estimated according to Caneye version 5 (Field type: Numeric)</li><li><b>LAI_true_v5</b>: LAI true (accounting for vegetation clumping) estimated according to Caneye version 5 (Field type: Numeric)</li><li><b>ALAeffv5</b>: Effective average leaf inclination angle following alogorith used in Caneye v5 (Field type: Numeric)</li><li><b>ALAtruev5</b>: True average leaf inclination angle following alogorith used in Caneye v5 (Field type: Numeric)</li><li><b>Fap_meas_Dir</b>: Black - sky direct fAPAR (fraction of absorbed photosynthetically active radiation) measured (Field type: Numeric)</li><li><b>Fap_mod_Dir</b>: Black - sky direct fAPAR (fraction of absorbed photosynthetically active radiation) modelled (Field type: Numeric)</li><li><b>Fap_meas_Dif</b>: White - sky diffuse fAPAR (fraction of absorbed photosynthetically active radiation) measured (Field type: Numeric)</li><li><b>Fap_mod_Dif</b>: White - sky diffuse fAPAR (fraction of absorbed photosynthetically active radiation) modelled (Field type: Numeric)</li><li><b>fcover</b>: Fractional canopy cover (mean). Cover fraction (fcover) is defined as the fraction of the soil covered by the vegetation viewed in the nadir direction. Using hemispherical images, the cover fraction must be integrated over a range of zenith angles (0-10 degrees) (Field type: Numeric)</li><li><b>sdfcov</b>: Fractional canopy cover (standard deviation). Cover fraction (fcover) is defined as the fraction of the soil covered by the vegetation viewed in the nadir direction. Using hemispherical images, the cover fraction must be integrated over a range of zenith angles (0-10 degrees) (Field type: Numeric)</li></ul><br></li></ol><p><b>Date range: </b>2010-07-01 to 2014-01-10</p><p><b>Latitudinal extent: </b>4.4245 to 4.7714</p><p><b>Longitudinal extent: </b>116.9477 to 117.7028</p>
Mediterranean Sea Climatic Indices
<p>Climatic indices for the Mediterranean Sea (-6.25W–36.5E, 30N–46N) from 1950 to 2015. The file "Med_Climatic_Indices.tar.gz" contains : a) an ascii file named “inventory.lst” which lists all the available indices, and b) a directory named “Med_Climatic_Indices” with all the available indices under the following structure:</p> <ul> <li>Anomalies/Annual_Decadal/Variable_decade_allmonths_z1_z2.nc</li> <li> <p>Anomalies/Seasonal_Decadal/Variable_decade_season_z1_z2.nc</p> </li> <li> <p>ArealDensity/Annual_Decadal/Variable_decade_allmonths_z1z2.nc</p> </li> <li> <p>ArealDensity/Seasonal_Decadal/ Variable_decade_season_z1z2.nc</p> </li> <li> <p>Climashift_30yrs/Anomalies/Annual/Variable_decade2_decade1_allmonths_z1_z2.nc</p> </li> <li> <p>Climashift_30yrs/Anomalies/Seasonal/ Variable_decade2_decade1_season_z1_z2.nc</p> </li> <li> <p>Climashift_30yrs/Vertical_Averages/Annual/Variable_decade2_decade1_allmonths_z1z2.nc</p> </li> <li> <p>Climashift_30yrs/Vertical_Averages/Seasonal/Variable_decade2_decade1_season_z1z2.nc</p> </li> <li> <p>LinearTrends/Annual/Variable_period_allmonths_z1z2.nc</p> </li> <li> <p>LinearTrends/Seasonal/ Variable_period_season_z1z2.nc</p> </li> <li> <p>TimeSeries/Annual/Variable_period_allmonths_z1z2.dat</p> </li> <li> <p>TimeSeries/Seasonal/ Variable_period_season_z1z2.dat</p> </li> <li> <p>VerticalAverages/Annual_Decadal/Variable_decade_allmonths_z1z2.nc</p> </li> <li> <p>VerticalAverages/Seasonal_Decadal/ Variable_decade_season_z1z2.nc</p> </li> </ul> <p>The Variable naming is:</p> <ul> <li> <p>Tanom: Temperature anomaly</p> </li> <li> <p>Sanom: Salinity anomaly</p> </li> <li> <p>Tanomvavg: Vertically averaged temperature anomaly</p> </li> <li> <p>Sanomvavg: Vertically averaged salinity anomaly</p> </li> <li> <p>OHCad: areal density Ocean Heat Content anomaly</p> </li> <li> <p>OSCad: areal density Ocean Salt Content anomaly</p> </li> <li> <p>Tanomclimashift: Temperature anomaly difference between decade2 and decade1</p> </li> <li> <p>Sanomclimashift: Temperature anomaly difference between decade2 and decade1 at</p> </li> <li> <p>Tclimashift: Vertically averaged temperature anomaly difference between decade2 and decade1</p> </li> <li> <p>Sclimashift: Vertically averaged salinity anomaly difference between decade2 and decade1</p> </li> <li> <p>OHCclimashift: Ocean Heat Content anomaly difference between decade2 and decade1</p> </li> <li> <p>OSCclimashift: Ocean Salt Content anomaly difference between decade2 and decade1</p> </li> <li> <p>Tlineartrend: Vertically averaged temperature anomaly linear trend</p> </li> <li> <p>Slineartrend: Vertically averaged salinity anomaly linear trend</p> </li> <li> <p>OHClineartrends: Ocean Heat Content anomaly linear trend</p> </li> <li> <p>OSClineartrends: Ocean Salt Content anomaly linear trend</p> </li> </ul> <p>Other naming:</p> <ul> <li> <p>allmonths: 0112 (all months from January to December)</p> </li> <li> <p>seasons: 0103 for winter, 0406 for spring, 0709for summer, 1012 for autumn</p> </li> <li> <p>decade1: stands for 19501979</p> </li> <li> <p>decade2: stands for 19802015</p> </li> <li> <p>period: stands for 19502015</p> </li> <li> <p>z1z2: vertical layer between z1 and z2 depths</p> </li> <li> <p>z1, z2: standard depth levels</p> </li> </ul> <p>Examples:</p> <ol> <li> <p>OHC_19502015_0103_0150.dat, is the winter Ocean Heat Content anomaly for the period 1950 to 2015, at 0-150 m</p> </li> <li> <p>Tanomvavg_20062015_1012_6004000.nc, is the autumn vertically averaged temperature anomaly, for the decade 2006-2015, at 600 – 4000 m</p> </li> <li> <p>Sanomclimashift_19802015_19501979_0112_5_4000.nc, is the annual salinity anomaly difference between 1980-2015 and 1950-1970 from 5 to 4000 m.</p> </li> </ol> <p><br> </p>
Fig. 1. Phylogenetic relationships of species of Eusurbus and Zentamyia. Tree generated from morpho- logical phylogenetic analysis, unambiguous apomorphies mapped on branches, black circles indicate non- homoplasious changes.
Fig. 1. Phylogenetic relationships of species of Eusurbus and Zentamyia. Tree generated from morpho- logical phylogenetic analysis, unambiguous apomorphies mapped on branches, black circles indicate non- homoplasious changes.
Global signaling profiling in a human model of tumorigenic progression indicates a role for alternative RNA splicing in cellular reprogramming.
<p>Data was collected using a LTQ-XL mass spectrometer (Thermo). Phosphopeptides were enriched from cell extracts from 3 independent biological replicates, and each replicate was analyzed as 3 technical replicates for a total of 9 LC/MS/MS runs per cell line. Cell lines are based on the MCF-10A lineage of human mammary epithelial cells, and include MCF-10A (10A), MCF-10AT (AT), MCF-10ATG3B (TG) and MCF-10ACA1a (CA).</p>
Nutrition UP 65 study: The association between 25(OH)D levels, frailty status and obesity indices in older adults database
<p>This study database derives from Nutrition UP 65 study main database. The association between 25(OH)D levels, frailty status and obesity indices in older adults was evaluated. This database was prepared according to the recommendations of the following article: "Hrynaszkiewicz Iain, Norton Melissa L, VickersAndrew J, Altman Douglas G. Preparing raw clinical data for publication: guidance for journal editors, authors, and peer reviewers BMJ 2010; 340:c181".</p>
Comparative genome analysis of Lactococcus lactis indicates niche adaptation and resolves genotype/phenotype disparity
<p>This publication describes the comparative genome analysis of 43 L. lactis strains</p> <p>The data stored here are individual alignments (alignments.zip) and the output files from gene trait matching (GTM_output.zip) used in this study.</p>
IMPREX - TUC - Local scale seasonal hydrological and drought indices forecasts
<p>The compressed files are seasonal forecasts of discharge based on ECMWF System4 (1981-2003) and GLOSEA5 (1996-2003) for the Koutsoulidis catchment, Crete, Greece. The location of the Koutsoulidis catchment as well as the methodology of data development and validation are described in the open access publication by Grillakis et al., 2018 (<a href="https://www.mdpi.com/2073-4441/10/11/1593">https://www.mdpi.com/2073-4441/10/11/1593</a>). Seasonal forecasts of Standardized Precipitation Index (SPI) based on ECMWF System4 (1981-2009) and GLOSEA5 (1996-2009) for the Messara catchment are also included. A description of the study site and methodology is included in the poster attached in the compressed file (EGU201712072-Koutroulis).</p>
Development and Implementation of Sustainable Transportation Resilience Indicators
<p>Communities are complex systems subject to a variety of hazards that can result in significant disruption to critical functions. Community resilience assessment is gaining popularity as a means to help communities better prepare for, respond to, and recover from disruption. Sustainable resilience, a recently developed concept, requires communities to assess system-wide capability to maintain desired performance levels, while simultaneously evaluating impacts to resilience due to changes in hazards and vulnerability over extended periods of time.</p> <p>In an earlier work, the authors developed a classification scheme to aid in identification, selection and application of community sustainable resilience indicators that can be tailored to a community’s needs in operationalizing the assessment process. These indicators were characterized according to whether they aligned with social, economic or environmental systems that are necessary for a community to achieve a sustainable resilience domain of survival, well-being, or full preparedness.</p> <p>Of the critical infrastructure systems that support these systems and domains, transportation is arguably the most important. This is based on the premise that transportation is a means to an end, providing the mobility that enables a community to establish and maintain a social, economic and environmental fabric. Whether it involves an educational, medical, recreational, religious, work or other purpose, absent a safe and reliable transportation system, none of these activities can be satisfactorily pursued. Moreover, in times of crisis, transportation serves as a vital artery for enabling access to and egress from impacted areas.</p> <p>The objective of this project was to establish and demonstrate a method for evaluating a community’s transportation resilience, such that if deficiencies exist, attention can be focused on mitigating those concerns. This approach was designed around the scenario of a river valley community exposed to the threat of a significant flood event, with the expectation that the methodology has the potential to be extended to assess community resilience to other natural and manmade hazards.</p>
Performance Indicators of Photovoltaic Heat Pumps
<p>Qualitative and quantitaive results included in the review 'Performance Indicators of Photovoltaic Heat Pumps' (DOI:10.1016/j.heliyon.2019.e02691) are given, together with the original data and the corresponding calculations. The files with the simulation info for the SISIFO simulation tool are also available. </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.