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3,136 results for “Terrestrialization”
Figure 1 from: Backeljau T, Breugelmans K, Jordaens K, Adriaens E, Remon J, Quintana Cardona J (2013) DNA barcodes and phylogenetic affinities of the terrestrial slugs Arion gilvus and A. ponsi (Gastropoda, Pulmonata, Arionidae). ZooKeys 365: 83-104. https://doi.org/10.3897/zookeys.365.6104
Figure 1 - Arion ponsi Quintana Cardona, 2007 from Menorca (Balearic Islands, Spain).
Figure 3 from: Giurginca A, Šustr V, Tajovský K, Giurginca M, Matei I (2015) Spectroscopic parameters of the cuticle and ethanol extracts of the fluorescent cave isopod Mesoniscus graniger (Isopoda, Oniscidea). In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 111–125. https://doi.org/10.3897/zookeys.515.9395
Figure 3 - IR spectra of the ethanol extract of Mesoniscus graniger graniger.
Figure 2 from: Hornung E (2015) In commemoration of Prof. M.R. Warburg and of his contribution to terrestrial Isopod biology (31 May 1931, Berlin–9 February 2014, Haifa). In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 1–11. https://doi.org/10.3897/zookeys.515.9909
Figure 2 - Distribution of published papers by animal groups.
Figure 1 from: Hornung E (2015) In commemoration of Prof. M.R. Warburg and of his contribution to terrestrial Isopod biology (31 May 1931, Berlin–9 February 2014, Haifa). In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 1–11. https://doi.org/10.3897/zookeys.515.9909
Figure 1 - The distribution of MR Warburg's papers on isopods by topic.
Figure 2 from: Simões MH, Souza-Silva M, Ferreira RL (2015) Cave physical attributes influencing the structure of terrestrial invertebrate communities in Neotropics. Subterranean Biology 16: 103-121. https://doi.org/10.3897/subtbiol.16.5470
Figure 2 - List of sampled higher taxa and their species richness.
Figure 2 from: Kashani GM (2016) Iranian terrestrial isopods of the family Cylisticidae Verhoeff, 1949 with a description of a new species (Isopoda, Oniscidea). ZooKeys 582: 157-165. https://doi.org/10.3897/zookeys.582.7199
Figure 2 - Cylisticus masalicus sp. n.; A conglobated B active.
Figure 2 from: Kashani GM, Abedini A, Montesanto G (2018) Terrestrial isopods of the family Eubelidae Budde-Lund, 1899 from Iran, with description of a new species (Isopoda, Oniscidea). In: Hornung E, Taiti S, Szlavecz K (Eds) Isopods in a Changing World. ZooKeys 801: 177-187. https://doi.org/10.3897/zookeys.801.23340
Figure 2 APeriscyphisvittatusBKoweitoniscustamei. Scale bar: 1 mm.
Figure 2 from: Nasu T, Kitagawa K, Karasawa S (2018) Species compositions of terrestrial isopods in public parks of a commuter town in Japan. In: Hornung E, Taiti S, Szlavecz K (Eds) Isopods in a Changing World. ZooKeys 801: 389-399. https://doi.org/10.3897/zookeys.801.21875
Figure 2 Frequencies of species richness of native, exotic, and total species in parks
Data from: Time-lagged effects of habitat fragmentation on terrestrial mammals in Madagascar
<p>In our paper “Time-lagged effects of habitat fragmentation on terrestrial mammals in Madagascar” (Conservation Biology) we quantified the magnitude of time-lagged effects of habitat fragmentation for 157 nonvolant terrestrial mammal species in Madagascar, one of the biodiversity hotspots with the highest rates of habitat loss and fragmentation. Therefore, we calculated the proportion of habitat area for each species that are too small to support populations for at least 100 years. First, we refined species’ geographic ranges based on habitat preferences and elevation limits. Secondly, we calculated population sizes of distinct populations by multiplying habitat area with population density. Finally, we used the Ricker-Logistic growth equation to simulate population trajectories for 100 years and determined whether each population could survive for at least 100 years. We used allometric relationships to obtain the required population parameters for this analysis, and we quantified the consequences of uncertainty in these parameter estimates by repeating the analyses with a range of plausible parameter values. Based on the median outcomes, the results showed that for 34 species (22% of the 157 species) at least 10% of their current habitat contained unviable populations. Based on 0.95-quantile values, following a precautionary principle, for 108 species (69%) at least 10% of their habitat contained unviable populations. These results highlight the need to preserve continuous habitat and improve connectivity between habitat fragments. Moreover, our findings may help to identify species for which time-lagged effects are most severe and which may thus benefit the most from conservation actions.</p> <p>In this dataset we provide the taxonomy and trait data for each species analysed in this study. These trait data were used as input to the allometric relationships to predict population parameters. In addition, we provide the different plausible values for each population parameter and species, used in this study.</p>
Effects of diesel exhaust particles on the health and survival of the buff-tailed bumblebee Bombus terrestris after acute and chronic oral exposure.
<p>Contents the data sets belonging to the publication <strong>Effects of diesel exhaust particles on the health and survival of the buff-tailed bumblebee <em>Bombus terrestris </em>after acute and chronic oral exposure. </strong>in the Journal of Hazardous materials. Included are the data sets to the experiments exploring the effects of acute and chronic oral toxicity of DEP respectively, and a data set including the PAH content of the analysed DEP samples</p>
Open data for "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate"
<p>______________________________________________________<br> This page contains public-domain data required to reconstruct simulation results in the manuscript "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate," submitted by the following author.</p> <p>Author: Hisashi SATO (JAMSTEC) <br> email : hsatoscb_(at)_gmail.com</p> <p>______________________________________________________<br> 1. Folder "Code"<br> Detailed descriptions are available on the code. </p> <p>1-1. MachineLearningComparison.R<br> Machine learning programs using random forest (RF), naive Bayes classifier (NV), and support vector machine (SVM) algorithms.</p> <p>1-2. Analyse_MapSimilarity.R<br> Calculate coincidences of simulated potential natural vegetation (PNV) maps simulated by different models.</p> <p>1-3. Visualize_VCE.R<br> Generating VCE (Visualize Climate Image) for training CNN models.</p> <p>1-4. Visualize_Maps.R<br> Visualizing global PNV maps.</p> <p>1-5. Visualize_ClimateHistgrams.R<br> Visualizing histograms of climate datasets.</p> <p>______________________________________________________<br> 2. Folder "Input"</p> <p>2-1. Unified_BIOCLIM_WorldClim.csv<br> Input data for the current climate.<br> This file contains the following variables.<br> lon Longitude at the center of the grid<br> lat Latitude at the center of the grid<br> bio1~19 Average climate indices from BIOCLIM (AveI)<br> CDD~WSDI Extreme climate indices (CEI)<br> c1~c16 Fraction of PNV from MODIS data<br> tavg01~tavg12 Monthly mean air temperature from January to December (Ave)<br> prec01~prec12 Monthly precipitation from January to December (Ave)</p> <p>2-2. Unified_BIOCLIM_WorldClimFutureRCP85.csv<br> Input data for future climate (@RCP8.5)<br> Including variables are the same as Unified_BIOCLIM_WorldClim.csv</p> <p>2-3. BIOCLIM_RefNo.csv<br> This CSV file contains the following information for each grid.<br> lat: Latitude at the center of the grid<br> lon: Longitude at the center of the grid<br> latNo: Latitude number corresponding to the image file name<br> lonNo: Longitude number corresponding to the image file name<br> lineNo: No use. Don't mind.<br> vegNo: Most dominant PNV based on the Unified_BIOCLIM_WorldClim.csv</p> <p>______________________________________________________<br> 3. Folder "Output"</p> <p>3-1. PNV_sim<br> 3-2. PNV_sim_RCP85.csv<br> Current and future PNV maps from various models. These files are the main output files from the code MachineLearningComparison.R. For PNV maps from CNN models (m4p1~6) were supplemented. Detailed methods to build CNN models, please refer to the following manuscript.<br> Sato, H. & T. Ise (2022). "Predicting global terrestrial biomes with the LeNet convolutional neural network." Geoscientific Model Development 15(7): 3121-3132.</p> <p>Labels indicate combinations of machine-learning-algorithm and dataset for training the model. For example, In case of "m1p1", that column shows the simulation result of models trained with randomForest (RF) algorithm and Ave dataset.<br> m1: randomForest (RF)<br> m2: Support vector machine (SVM)<br> m3: Naive Bayes (NB)<br> m4: Convolutional Neural Network (CNN), which is NOT analysed in this code<br> p1: Ave<br> p2: Ave + CEI<br> p3: Ave + CEIpart<br> p4: AveI <br> p5: AveI + CEI<br> p6: AveI + CEIpart</p>
Terrestrial and biological activities shaped the fate of dissolved organic nitrogen in a subtropical river-dominated estuary and adjacent coastal area
<p>Here are two files including the nutrient and environmental parameters and other data in the Pearl River Estuary.</p>
Seaward transport of resuspended sedimentary terrestrial organic matter driven by tidal oscillation in the river-estuary-coast continuum
<p>Original data of paper named “Seaward transport of resuspended sedimentary terrestrial organic matter driven by tidal oscillation in the river-estuary-coast continuum”for publication consideration by <em>Global Biogeochemical cycle。</em></p>
Judaculla Rock July 2017 - Terrestrial Model
This model was done by Dr. Jannie Loubser and Joel Logan, GISP on a field visit on 7/22/2017 for Straum Unlimited, LLC. This model was done after Stratum Unlimited completed a lengthy project of removing grafitti from the rock. This model was done entirely from photos from an IPhone 6 PLUS. This site can be visited by the public. It is on an a one acre site that Jackson County, NC owns. Source: Objaverse 1.0 / Sketchfab
Effects of Tribulus Terrestris on Sexual Function in Post-menopausal Women
ClinicalTrials.gov study NCT01407445. IPD Sharing: Not stated. Countries: 1. Publications: 0.
TETRA (Terrestrial Trunked Radio)
ClinicalTrials.gov study NCT00395668. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Terrestrial Trunked Radio (TETRA) and Cognitive Functions in Healthy Subjects
ClinicalTrials.gov study NCT01117597. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Acute Effects of Tribulus Terrestris on Anaerobic Power and Sprint Performance in Combat Sport Athletes
ClinicalTrials.gov study NCT07164547. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Effects of Tribulus Terrestris in Women With Hypoactive Sexual Desire Disorder
ClinicalTrials.gov study NCT01971099. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Efficacy and Safety Study of Products Based on Tribulus Terrestris, L. in Men With Oligospermia
ClinicalTrials.gov study NCT06260007. IPD Sharing: NO. Countries: 1. Publications: 0.
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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)
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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.