Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
30,334
datasets available to search
ShareScore release 0.9.0
Dataset results
30,334 results for “Responsiveness”
Fig. 2 in Diel movement patterns of Pacific sugar limpet, Patelloida saccharina (Gastropoda: Patellogastropoda: Lottiidae) in response to semi-diurnal tides of Samal Island, Philippines
Fig. 2. Patelloida saccharina clamped on: A, bare rock face; B, rock covered with sand and turf algae. C, portion of the 5 × 5 cm grid quadrat laid on a reef patch with three P. saccharina individuals. Apices of limpet shells were painted with nail polish. Photographs by EY Zapanta and MA Fortaleza.
Fig. 4 in Response of mucous cells of the gills of traíra (Hoplias malabaricus) and jeju (Hoplerythrinus unitaeniatus) (Teleostei: Erythrinidae)
Fig. 4. Mean values (± SEM) of mucous cell density, individual surface area and mucous cell fractional area (MCFA) at filament epithelial surface of Hoplerythrinus unitaeniatus exposed to deionized water and high ion concentration water.
Fig. 2. a in Response of mucous cells of the gills of traíra (Hoplias malabaricus) and jeju (Hoplerythrinus unitaeniatus) (Teleostei: Erythrinidae)
Fig. 2. a. TEM photomicrograph of mucous cell (MC) in the filament epithelium of Hoplias malabaricus. Note pavement cells (PVC) covering MC. b. TEM photomicrograph of mucous cell (MC) in the filament epithelium of Hoplerythrinus unitaeniatus. Note different electrondensity of secretory granules (G). c. SEM photomicrograph of filament epithelium showing mucous granules releasing by MC between PVC cells, at epithelial surface. Scale bar in mm. Arrows indicate mucous granules.
Dataset - University Social Responsibility in the context of Social Appropriation of Knowledge: A Systematic Literature Mapping Approach (2015-2020)
<p>From abstract:</p> <p>University Social Responsibility (USR) should be seen beyond a yearly report and be understood as a process of Social Appropriation of Knowledge (SAK) though different stakeholders around higher education. The object of this literature mapping is to analyze the recent developments regarding USR regarding SAK in a higher education context where different stakeholders are involved. Following a PRISMA flow mapping process, six general-designed questions of 286 articles are revised for this paper as to discover how SAK happens within universities. All articles were retrieved from Scopus and Web of Sciences databases. The findings include a USR field divided between models´ approach and experience such as service-learning. Furthermore, sustainability and the environment take a special role within USR in the context of SAK that must be analyzed by itself as there is a wide interest in the topic. </p>
Cereal yield response to climate change in key grain producing areas of the Northern Hemisphere
<p>Vulnerability mapping is a field of increasing importance as we search for an ecologically and economically sustainable<br> climate-smart land-use system, which is necessary for the development of green infrastructure and agricultuVulnerability<br> mapping is a growing field with increasing importance in developing an ecologically and economically sustainable climate-smart land-use system, which may serve the advancement of green infrastructure and agricultural production. The<br> transformation of the climatic regime has an undeniable impact on plant production, but we rarely have long enough data<br> series to scrutinize the unfolding effects of the microregional patterns of the global climate-cereal yield relationship. To fill this<br> gap, I will analyze three regions of the Northern Hemisphere under continental climate, which represent key landscapes<br> concerning global food production and security, contributing a multi-way knowledge transfer through training, teaching, and<br> different form of communication activities. The relationship between climate indices, maize, and wheat yields will be analyzed<br> applying high geographical resolution data from Northern China, Hungary, and northern states of the US Midwest at a<br> landscape scale by Köppen-Geiger zones using linear, local, and spatial regression and bootstrap resampling tests and<br> developed data visualization techniques. This offers a unique opportunity to comparatively analyze the differences in<br> climate-cereal yield associations by landscape types and regions. The expected results will provide highly supportive factors<br> for planning and managing an ecologically and economically sustainable land-use system. The intended publications,<br> lectures, and collaborative actions will have a positive impact on domestic and EU research. The planned training and the<br> expected results of the action will further strengthen my career opportunities and put me in a position where I will be<br> considered for a tenured position at a research institution in Hungary.</p>
Differences in carbon acquisition could explain adaptive responses in a Baltic Sea pico-phytoplankton
<p>The data uploaded are the basis of study on the carbon update in a Baltic Sea Pico-plankton species. Our study addresses a challenging question concerning the adaptive responses to warming and carbon uptake related strategies in a pico-phytoplankton species, which are, unlike bloom-forming, larger phytoplankton, contributing to the carbon cycle and the base of food-webs all year round. In addition, we address this question also in non-exponential growth conditions. This is important considering that most phytoplankton species are likely not at exponential phase all the time. We show that space for time substitution experiments provide a valid method for investigating questions about the adaptive potential in pico-phytoplankton that the measurements of potential growth on organic carbon sources can potentially highlight differences in carbon acquisition strategies.</p>
Echolocating toothed whales use ultra-fast echo-kinetic responses to track evasive prey
<p>Visual predators rely on fast-acting optokinetic responses to track and capture agile prey. Most toothed whales, however, rely on echolocation for hunting and have converged on biosonar clicking rates reaching 500/s during prey pu rsuits. If echoes are processed on a click by click basis, as assumed, neural responses 100x faster than those in vision are required to keep pace with this information flow. Using high resolution bio-logging of wild predator prey interactions we show that toothed whales adjust clicking rates to track prey movement within 50 200 ms of prey escape responses. Hypothesising that these stereotyped biosonar adjustments are elicited by sudden prey accelerations, we measured echo kinetic responses from trained harb our porpoises to a moving target and found similar latencies. High biosonar sampling rates are, therefore, not supported by extreme speeds of neural processing and muscular responses. Instead, the neuro kinetic response times in echolocation are similar to those of tracking responses in vision, suggesting a common neural underpinning.</p>
Multiscale Temporal Response of Salt Intrusion to Transient River and Ocean Forcing
<p>Model and validation data presented in Payo Payo et al, Multiscale Temporal Response of Salt Intrusion to Transient River and Ocean Forcing (JGR-Oceans, 2021, submitted).</p> <p>We used a 3-dimensional unstructured hydrodynamic model 3-dimensional unstructured hydrodynamic model (FVCOM Finite Volume Community Ocean Model)understand river and tide forcing to disentangle the multiscale temporal pattern of salt intrusion in estuaries subject to transient forcing due to river and tide. We implemented the Pearl River Delta as a case study for 2007/2008.</p> <p>The dataset consists of:</p> <p>·Measured and modelled salinity and elevation data at the validation stations.</p> <p>·River discharge data</p> <p>·SSH data</p> <p>·Salt intrusion length</p>
Understanding behavioural responses to human-induced rapid environmental change: A meta-analysis
<p>Behavioural responses are often the first reaction of an organism to human induced rapid environmental change (HIREC), yet current empirical evidence provides no consensus about the main environmental features that animals respond to behaviourally or which behaviours are responsive to HIREC. To understand how changes in behaviour can be predicted by different forms of HIREC, we conducted a meta-analysis of the existing empirical literature focusing on behavioural responses to five axes of environmental change (climate change, changes in CO<sub>2</sub>, direct human impact, changes in nutrients and biotic exchanges) in five behavioural domains (aggression, exploration, activity, boldness and sociability) across a range of taxa but with a focus on fish and bird species. Our meta-analysis revealed a general absence of directional behavioural responses to HIREC. However, the absolute magnitude of the effect sizes was large. This means that animals have strong behavioural responses to HIREC, but the responses are not clearly in any particular direction. Moreover, absolute magnitude of the effect sizes differed between different behaviours and different forms of HIREC: Exploration responded more strongly than activity, and climate change induced the strongest behavioural responses. Model heterogeneities identified that effect sizes varied primarily because of study design, and the specific sample of individuals used in a study; phylogeny also explains significant variation in our bird model. Based on these results, we make four recommendations to further our understanding: 1) a more balanced representation of laboratory and field studies, 2) consideration of context dependency, 3) standardisation of the methods and definitions used to quantify and study behaviours, and 4) consideration of the role for individual differences in behaviour.</p>
CTAO Instrument Response Functions - prod5 version v0.1
<p>CTAO Instrument Response Functions - prod5 version v0.1</p> <p>The CTA Observatory (CTAO) will provide very wide energy range and excellent angular resolution and sensitivity in comparison to any existing gamma-ray detector. Energies down to 20 GeV will allow CTAO to study the most distant objects. Energies up to 300 TeV will push CTAO beyond the edge of the known electromagnetic spectrum, providing a completely new view of the sky. This data repository provides access to performance evaluation and instrument response functions (IRFs) for CTA.</p> <ul> <li>IRF version: prod5 v0.1</li> <li>Telescope model and site configuration: <a href="https://zenodo.org/record/6218687">prod5-model</a></li> <li>Publication date: Sep 2021</li> <li>Archived webpage with performance figures included: <a href="/cta-science/montecarlo-results/public-irfs-zenodo/cta-prod5-zenodo/-/blob/preview/Website.md">CTAO Performance Description (file Website.md)</a></li> <li>Licence: this work is licensed under a <a href="/cta-science/montecarlo-results/public-irfs-zenodo/cta-prod5-zenodo/-/blob/preview/LICENSE">Creative Commons Attribution 4.0 International License</a>.</li> </ul> <p>Please use the contact address open-data@cta-observatory.org for any inquiries.</p> <p>Citation and Acknowledgements:</p> <p>In cases for which the CTA instrument response functions are used in a research project, we ask to add the following acknowledgement in any resulting publication:</p> <p>"This research has made use of the CTA instrument response functions provided by the CTA Consortium and Observatory, see <a href="https://www.cta-observatory.org/science/cta-performance/">https://www.ctao-observatory.org/science/cta-performance/</a> (version prod5 v0.1; [citation]) for more details."</p> <p>Please use the following BibTex Entry for [citation] in the reference section of your publication:<br> <a href="https://zenodo.org/record/5499840/export/hx">https://zenodo.org/record/5499840/export/hx</a></p> <p>Description</p> <p>Monte Carlo Simulations:</p> <p>The performance values are derived from detailed Monte Carlo (MC) simulations of the CTA instrument based on the CORSIKA air shower code (v7.71, with the hadronic interaction models QGSjet-II-04 and URQMD, [1]) and telescope simulation tool sim_telarray [2]. A power- law gamma-ray spectrum with photon index 2.62 was assumed in the calculations, although none of the instrument response functions (e.g. differential flux sensitivities, effective areas, angular or energy resolutions) depends on the assumed spectral shape of the gamma-ray source. Background cosmic-ray spectra of proton and electron/positron particle types are modelled according to recent measurements from cosmic-ray instruments.</p> <p>Nominal telescope pointing is assumed, with all telescopes pointing directions parallel to each other (performance estimation for other pointing modes, e.g. divergent pointing will be provided in the future). Performance estimations are available for three zenith angles (20 deg, 40 deg, and 60 deg), and for each zenith angle for two different azimuth angles (corresponding to pointing towards the magnetic North and South). There are significant performance differences found between the two azimuthal pointing directions (especially for the Northern site) as the impact of the geomagnetic field is large enough to influence notably the air shower development. For general studies, the use of the azimuth-averaged instrument response functions is recommended.</p> <p>Instrument Response Functions (IRFs):</p> <p>The analysis has been tuned to maximize the performance in terms of flux sensitivity. The optimal analysis cuts depend on the duration of the observation, therefore the IRFs are provided for 3 different observation times, from 0.5 to 50 h. IRFs are provided as binned histogram or FITS tables. It should be stressed, that the full potential of CTA in terms of angular and energy resolution is not revealed by these IRFS, due to the focus on the optimisation for best flux sensitivity.</p> <p>In general all histograms are binned with a 0.2-binning on the logarithmic energy axis (5 bins per decade); some selected histograms (e.g. effective areas or energy migration matrices) are provided with a finer binning. Effective area and energy migration matrix are available in a double version: one for the case in which there is no a priori knowledge of the true direction of incoming gamma rays (e.g. for the observation of diffuse sources), and another for observations of point-like objects (including among the analysis cuts one on the angle between the true and the reconstructed gamma-ray direction).</p> <p>IRFs are provided in ROOT format and as FITS tables. The FITS tables can be used directly as input to science analysis tools. The values of the IRFs are identical for the different file format, with one exception: the angular point-spread function is approximated by a Gaussian function for the FITS tables, while the ROOT files contain the full distribution.</p> <p>Telescope layouts are preliminary and subject to change. The following array layouts (Alpha configuration) have been assumed:</p> <ul> <li> CTA South with 14 MSTs and 37 SSTs (see [figure](figures/CTA-Performance-prod5-v0.1-South-Alpha-Layout.png))</li> <li> CTA North with 4 LSTs and 9 MSTs (see [figure](figures/CTA-Performance-prod5-v0.1-North-Alpha-Layout.png))</li> </ul> <p>Two zip files are uploaded:</p> <ul> <li>full archive with IRFs in FITS and ROOT format: cta-prod5-zenodo-v0.1.zip</li> <li>partial archive with IRFs in FITS format only: cta-prod5-zenodo-fitsonly-v0.1.zip</li> </ul> <p>File Naming (examples):</p> <ul> <li>Prod5-North-40deg-AverageAz-4LSTs09MSTs.18000s-v0.1.root: IRF for CTA Northern site on La Palma, 40 deg zenith angle, azimuth-averaged pointing, optimised for 5 hours of observation time</li> <li>Prod5-South-20deg-AverageAz-14MSTs37SSTs.180000s-v0.1.fits.gz: IRF for CTA Southern site in Paranal, 20 deg zenith angle, azimuth-averaged pointing, optimised for 50 hours of observation time</li> </ul> <p>List of files:</p> <p>FITS format:</p> <ul> <li>fits/CTA-Performance-prod5-v0.1-North-20deg.FITS.tar.gz</li> <li>fits/CTA-Performance-prod5-v0.1-North-40deg.FITS.tar.gz</li> <li>fits/CTA-Performance-prod5-v0.1-North-60deg.FITS.tar.gz</li> <li>fits/CTA-Performance-prod5-v0.1-South-20deg.FITS.tar.gz</li> <li>fits/CTA-Performance-prod5-v0.1-South-40deg.FITS.tar.gz</li> <li>fits/CTA-Performance-prod5-v0.1-South-60deg.FITS.tar.gz</li> </ul> <p>ROOT format:</p> <ul> <li>root/CTA-Performance-prod5-v0.1-North-20deg.tar.gz</li> <li>root/CTA-Performance-prod5-v0.1-North-40deg.tar.gz</li> <li>root/CTA-Performance-prod5-v0.1-North-60deg.tar.gz</li> <li>root/CTA-Performance-prod5-v0.1-South-20deg.tar.gz</li> <li>root/CTA-Performance-prod5-v0.1-South-40deg.tar.gz</li> <li>root/CTA-Performance-prod5-v0.1-South-60deg.tar.gz</li> </ul> <p>IRFs for subarrays of e.g., MSTs only are in the files named MSTSubArray (similar for all other telescope types).</p> <p>References</p> <ul> <li>[1] <a href="https://www.ikp.kit.edu/corsika/">https://www.ikp.kit.edu/corsika/</a></li> <li>[2] Bernloehr, K. 2008, Astroparticle Physics, 30, 149</li> </ul> <p>Acknowledgements</p> <p>We would like to thank the computing centres that provided resources for the generation of the Prod 5 Instrument Response Functions (IRFs):</p> <ul> <li>CAMK, Nicolaus Copernicus Astronomical Center, Warsaw, Poland</li> <li>CIEMAT-LCG2, CIEMAT, Madrid, Spain</li> <li>CYFRONET-LCG2, ACC CYFRONET AGH, Cracow, Poland</li> <li>DESY-ZN, Deutsches Elektronen-Synchrotron, Standort Zeuthen, Germany</li> <li>GRIF, Grille de Recherche d’Ile de France, Paris, France</li> <li>IN2P3-CC, Centre de Calcul de l’IN2P3, Villeurbanne, France</li> <li>IN2P3-CPPM, Centre de Physique des Particules de Marseille, Marseille, France</li> <li>IN2P3-LAPP, Laboratoire d Annecy de Physique des Particules, Annecy, France</li> <li>INFN-FRASCATI, INFN Frascati, Frascati, Italy</li> <li>INFN-T1, CNAF INFN, Bologna, Italy</li> <li>INFN-TORINO, INFN Torino, Torino, Italy</li> <li>MPIK, Heidelberg, Germany</li> <li>OBSPM, Observatoire de Paris Meudon, Paris, France</li> <li>PIC, port d’informacio cientifica, Bellaterra, Spain</li> <li>prague_cesnet_lcg2, CESNET, Prague, Czech Republic</li> <li>praguelcg2, FZU Prague, Prague, Czech Republic</li> <li>UKI-NORTHGRID-LANCS-HEP, Lancaster University, United Kingdom</li> </ul>
Variation in biomass allocation and root functional parameters in response to fire history in Brazilian savannas
<p>Dataset associated with the manuscript "<strong>Variation in biomass allocation and root functional parameters in response to fire history in Brazilian savannas" </strong> (Le Stradic et al.). It includes 5 different datasets and for each one we provided metadata.</p> <p>above_below_b_SBI: it includes data related to aboveground and belowground biomass. Aboveground data were collected in circular plots of 0.5m2 and belowground biomass was collected using an auger of 5cm of diameter, every 10cm up to 40cm and every 20cm up to 1m depth. See the method section in the manuscript for full details.</p> <p>below_b_wet_all_SBII: it includes data related to belowground biomass (collected in the first 1m of soil, during the wet season, January-February 2018), including values for each soil depth.</p> <p>root_trait_SBI: it includes all root functional parameters for samples collected in the first 10 cm of soil.</p> <p>Sampling_data: it includes information associated with sampling areas (localization, GPS point, fire history).</p> <p>soil.expand.SBI: it includes all soil data.</p> <p> </p> <p><strong>Abstract</strong></p> <ol> <li>Fire is a fundamental ecological factor in savannas because it affects vegetation dynamics and ecosystem functioning. However, the effects of fire on belowground compartments, including biomass and root traits, and their regeneration remain poorly understood. In this study, we assess the variation of above- and belowground plant components along fire-history gradients in Brazilian open savannas and investigate whether vegetation and soil composition changes are associated with the responses of belowground biomass and root traits.</li> <li>The study was conducted in eight sampling areas of open savanna (<em>campo sujo</em>) the Cerrado (Brazilian savannas), located along a gradient of time since the last fire (1–34 years); the number of fires that occurred within the past 34 years (0–9 fires) varied by sampling area. In each sampling area, we measured above- and belowground biomass, root depth distribution, root functional parameters, and nutrient levels in the upper soil layers (0–10 cm).</li> <li>Rapid recovery of aboveground live biomass after a fire was primarily due to resprouting of graminoids. This recovery was associated with an increase in absorptive root biomass in the upper soil layer in the most recently burnt sites, whereas root biomass was unaffected in deeper layers. Root parameters remained constant regardless of fire history but responded to variations in vegetation structure and soil resources. Specific root length (SRL) decreased with K, Mg<sup>2+</sup>, Al<sup>3+</sup>, N, and C and increased with P concentration. In contrast, root tissue density (RTD) and absorptive root proportion were negatively correlated with soil P. RTD was strongly associated with the aboveground biomass of graminoids. Soil texture impacted the root system: the proportion of absorptive root increased with fine sand content in the soil, inversely to transport root biomass. The relationship between fire and soil composition was insignificant.</li> <li><em>Synthesis</em>. In savannas, fire stimulates absorptive root biomass in response to the higher demand for belowground resources. This response is correlated with shoot regrowth after a fire. Variations in morphological root parameters are not directly associated with fire history; instead, they reflect differences in soil chemistry, especially soil P and graminoid biomass changes.</li> </ol>
Phosphomapping of S. cerevisiae Pkc1 in response to hydroxyurea treatment
<p>We performed phosphorylation site mapping on of budding yeast Pkc1 applying a quantitative mass spectrometry (MS)-based proteomics approach based on stable isotope labeling with amino acids in cell culture (SILAC). We determined changes in the Pkc1 phosphorylation pattern in response to genotoxic stress induced by Hydroxyurea treatment. This dataset includes all MS RAW files.</p>
Predicting Response: Waste-based biopolymer slurry recipe for 3d-printing
<p>Waste-based biopolymer slurry recipe preparation description for 3d-printing. It combines cellulose floc fibers and xanthan gum. </p> <p>This recipe has been used for the print experiment in the paper:</p> <p>Rossi G., Chiujdea R., Colmo C., ElAlami C., Nicholas P., Tamke M., Ramsgaard Thomsen M. (2021) A material monitoring framework: tracking the curing of 3d printed cellulose-based biopolymers. In Acadia 2021 Realignments. </p>
Satellite Sensor Relative Spectral Response data
<p>Satellite Spectral Response functions for a number of imaging sensors. Currently supports:</p> <ul> <li>Himawari-8 AHI</li> <li>Himawari-9 AHI</li> <li>GOES-16 ABI</li> <li>GOES-17 ABI</li> <li>GOES-18 ABI</li> <li>GOES-19 ABI</li> <li>NOAA AVHRR/1, AVHRR/2, AVHRR/3 (NOAA-6 errors in ch1 & 2 fixed)</li> <li>Metop AVHRR/3</li> <li>TIROS-N AVHRR/1</li> <li>Envisat AATSR</li> <li>Sentinel-3A, B, C & D SLSTR</li> <li>Sentinel-3A&B OLCI - mean rsr</li> <li>Meteosat SEVIRI</li> <li>Terra/Aqua MODIS</li> <li>Suomi-NPP VIIRS</li> <li>JPSS-1 (NOAA-20) VIIRS</li> <li>JPSS-2 (NOAA-21) VIIRS</li> <li>Sentinel-2A MSI</li> <li>Sentinel-2B MSI</li> <li>Landsat-8 OLI & TIRS</li> <li>Landsat-9 OLI & TIRS</li> <li>HY-1C COCTS</li> <li>Metop-SG-A1 MetImage - NB! simulated data - not measured!</li> <li>Sentinel-3B OLCI - mean rsr</li> <li>FY-3D MERSI-2</li> <li>FY-3F MERSI-3</li> <li>FY-4A AGRI</li> <li>FY-4B AGRI</li> <li>FY-3B VIRR</li> <li>FY-3C VIRR</li> <li>GEO-KOMPSAT-2A AMI</li> <li>Meteosat-12 FCI (version from EUMETSAT, April 2022)</li> <li>MTG-I1 FCI (same as above)</li> <li>Electro-L N2 MSU-GS</li> <li>Arctica-M 1 MSU-GS/A</li> <li>DSCOVR EPIC</li> <li>Sentinel-C MSI</li> </ul> <p> </p> <p>Changelog for the Relative Spectral Response data<br>=================================================</p> <p>Version v1.5.0 (Tue Sep 30 09:53:59 PM CEST 2025)</p> <p>-------------------------------------------</p> <p>* Added SLSTR RSRs for Sentinel-3C and -D. Updated with the latest versions from ESA for Sentinel-3A and -B.</p> <p> </p> <p>Version v1.4.1 (Tue Oct 29 03:45:40 PM CET 2024)</p> <p>-------------------------------------------</p> <p> * Added RSRs for Landsat-8 and -9 OLI & TIRS. Original RSR as provided by NASA. Previously we had only Landsat-8 OLI</p> <p> </p> <p>Version v1.4.0 (Tue Sep 24 03:35:15 PM CEST 2024)</p> <p>-------------------------------------------</p> <p> * Added RSRs for Sentinel-2C MSI and updated the MSI RSR for Sentinel 2A & B</p> <p> </p> <p>Version v1.3.2 (Mon Jul 15 12:32:12 PM CEST 2024)</p> <p>-------------------------------------------</p> <p> * Corrected the file name for the RSRs of Mersi-3 onboard FY-3F</p> <p> </p> <p>Version v1.3.1 (Mon Jul 15 11:12:10 AM CEST 2024)<br>-------------------------------------------</p> <p> * Added SRFs (RSRs) for the Mersi-3 sensor onboard FY-3F</p> <p> </p> <p>Version v1.3.0 (Fri May 3 04:11:43 PM CEST 2024)<br>-------------------------------------------</p> <p> * Added SRFs (RSRs) for the Mersi-1 sensor onboard FY-3A/B/C<br> * Added SRFs for the Mersi-RM sensor onboard FY-3G<br> * Added SRFs for the GHI (Geostationary High-speed Imager) sensor onboard FY-4B<br> * Added SRFs for GOCI-II (Geostationary Ocean Color Imager: Follow-on) sensor onboard GK-2B (GEO-KOMPSAT-2B)</p> <p> </p> <p>Version v1.2.4 (Sat Oct 21 12:25:00 PM CEST 2023)</p> <p>-------------------------------------------</p> <p>* Normalized RSR responses for the EPIC sensor. Now values are scaled to be</p> <p> between 0 and 1.</p> <p> </p> <p>Version v1.2.3 (Fri Oct 20 01:58:29 2023)</p> <p>-------------------------------------------</p> <p>* Added RSR file for EPIC on DSCOVR (responses not normalized)</p> <p> </p> <p>Version v1.2.2 (Tue Nov 15 14:44:03 2022)<br>-------------------------------------------</p> <p> * Added RSR file for AGRI onboard FY-4B<br> * Corrected RSR file for AGRI onboard FY-3B<br> Values were between 0 and 100, now scaled to between 0 and 1</p> <p> </p> <p>Version v1.2.1 (Mon Oct 24 16:26:14 2022)<br>-------------------------------------------</p> <p> * Changed name of the FY-3D MERSI-2 RSR file, removing the hyphen:<br> New name = rsr_mersi2_FY-3D.h5<br> </p> <p>Version v1.2.0 (Tue Sep 27 21:08:13 2022)<br>-------------------------------------------</p> <p> * Added VIIRS RSR for JPSS-2/NOAA-21:<br> Based on the J2_VIIRS_RSR_DAWG_At-Launch_Public_Release_V2_Jul2019.zip<br> The data read are the Detector wise files under J2_VIIRS_Detector_RSR_V2:</p> <p> J2_VIIRS_RSR_DNBLGS_Detector_Fused_V2FS.txt<br> J2_VIIRS_RSR_I1_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_I2_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_I3_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_I4_Detector_V2F.txt<br> J2_VIIRS_RSR_I5_Detector_V2F.txt<br> J2_VIIRS_RSR_M10_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M11_Detector_V2F.txt<br> J2_VIIRS_RSR_M12_Detector_V2F.txt<br> J2_VIIRS_RSR_M13_Detector_V2F.txt<br> J2_VIIRS_RSR_M14_Detector_V2F.txt<br> J2_VIIRS_RSR_M15_Detector_V2F.txt<br> J2_VIIRS_RSR_M16A_Detector_V2F.txt<br> J2_VIIRS_RSR_M1_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M2_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M3_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M4_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M5_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M6_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M7_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M8_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M9_Detector_WVCOR_Fused_V2F.txt</p> <p> The two files here were not considered:<br> J2_VIIRS_RSR_DNBMGS_Detector_Fused_V2FS.txt<br> J2_VIIRS_RSR_M16B_Detector_V2F.txt</p> <p> * Correted errors concerning NOAA-6 channel 1 and 2. These proved to be wrong<br> (a factor of 10 scale error), as the files they were based on were<br> wrong. For AVHRR-1 we have been using the ascii files from NOAA STAR<br> (https://www.star.nesdis.noaa.gov/smcd/spb/fwu/homepage/AVHRR/spec_resp_func/index.html). Example<br> of the start of the file for NOAA-6 channel-1:</p> <p> %> cat NOAA_6_A103C001.txt<br> Wavelegth (nm) Normalized RSF<br> 5600.000000 0.071000<br> 5700.000000 0.449000<br> 5800.000000 0.739000<br> 5900.000000 0.813000<br> 6000.000000 0.806000<br> 6200.000000 0.919000<br> 6400.000000 1.000000<br> ...</p> <p> Here we have instead used the xls files on which the NOAA-STAR files are based: AVHRR1_SRF_only.xls</p> <p> For TIROS-N there seem to be a typo in the wavelengths array for channel-1<br> on TIROS-N: A 640 nm should most likely have been 840 nm. This has been<br> corrected in the hdf5 file.<br> </p>
Can predators stabilize host-parasite interactions? Changes in aquatic predator identity alters amphibian responses and parasite abundance across life stages
<p><span><span>The role of parasites can change depending on the food web community. Predators, for instance, can amplify or dilute parasite </span><span>effects on their hosts. Likewise, exposure to parasites or predators at one life stage can have long-term consequences on individual performance and survival, which can influence population and disease dynamics. To understand how predators affect amphibian parasite infections across life stages, we manipulated exposure of northern leopard frog (<em>Rana pipiens</em>) tadpoles to three predators (crayfish [<em>Orconectes rusticus</em>], bluegill [<em>Lepomis macrochirus</em>], or mosquitofish [<em>Gambusia affinis</em>]) and to trematode parasites (<em>Echinostoma</em> spp.) in mesocosms and followed juveniles in outdoor terrestrial enclosures through overwintering. Parasites and predators both had strong impacts on metamorphosis with bluegill and parasites individually reducing metamorph survival. However, when fish were present, the negative effects of parasites on survival were not apparent, likely because fish altered community composition via increased algal food resources. Bluegill also reduced snail abundance, which could explain the reduced abundance of parasites in surviving metamorphs. Bluegill and parasite exposure increased mass at metamorphosis, which increased metamorph jumping, swimming, and feeding performance, suggesting larger frogs would experience better terrestrial survival. Effects on size at metamorphosis persisted in the terrestrial environment but did not influence overwintering survival. Based on our results, we constructed stage-structured population models to evaluate the lethal and sublethal effects of bluegill and parasites on population dynamics. Our models suggested that the positive effects of bluegill and parasites on body size may have greater effects on population growth than the direct effects of mortality.</span> <span>This study illustrates how predators can alter the outcome of parasitic infections and highlights the need for long-term experiments that investigate how changes in host-parasite systems alter population dynamics. We show some predators reduce parasite effects and have indirect positive effects on surviving individuals potentially increasing host population persistence. </span></span></p>
Evolutionary responses of energy metabolism, development, and reproduction to artificial selection for increasing heat tolerance in Drosophila subobscura
<p><span>Adaptation to warming conditions involves increased heat tolerance and metabolic changes to reduce maintenance costs and maximize biological functions close to fitness. Evidence shows that energy metabolism evolves in response to warming conditions, but we know little about how heat stress intensity determines the evolutionary responses of metabolism and life history traits. Here, we evaluated the evolutionary responses of energy metabolism and life-history traits to artificial selection for increasing heat tolerance in Drosophila subobscura, using two protocols to measure and select heat tolerance: slow and fast ramping protocols. We found that the increase in heat tolerance was associated with reduced activity of the enzymes involved in the glucose-6-phosphate branchpoint, but no changes in the metabolic rate in selected lines. We also found that the evolution of increased heat tolerance increased the early fecundity in selected lines and increased the egg-to-adult viability only in the slow-ramping selected lines. This work shows heat tolerance can evolve under different thermal scenarios but with different evolutionary outcomes on associated traits depending on the heat stress intensity. Therefore, spatial and temporal variability of thermal stress intensity should be taken into account to understand and predict the adaptive response to ongoing and future climatic conditions.</span></p>
Supplemental Data and Code for "An exact version of Life Table Response Experiment analysis, and the R package exactLTRE"
<p>This dataset enables the user to repeat the analyses presented in the manuscript "An exact version of Life Table Response Experiment analysis, and the R package exactLTRE." It is comprised of two compressed archives: one which contains code, and one which contains data.</p>
Responsible Research and Innovation (RRI) at an Institutional Level
<p>This is a short animation film about responsible research and innovation (RRI). The video shows why it is important for researchers, research institutes to build relationships with society and how to integrate this into their institutional operations. The video was created by the Co-Change Project which supports the implementation of institutional change among research and innovation actors in the areas of <em>research ethics, open science, science education, gender equality, and sustainability</em>.</p> <p>On Youtube you find it here: https://www.youtube.com/watch?v=fOG5U2QweBo</p>
Data from: Species-specific traits mediate avian demographic responses under past climate change
<p>Anticipating species' responses to environmental change is a<span> pressing mission in biodiversity conservation. Despite decades of research investigating how climate change may affect population sizes, historical context is lacking and the traits which mediate demographic sensitivity to changing climate remain elusive. We use whole-genome sequence data to reconstruct the demographic histories of 263 bird species over the past million years and identify networks of interacting morphological and life-history traits associated with changes in effective population size (<em>N<sub>e</sub></em>) in response to climate warming and cooling. Our results identify direct and indirect effects of key traits representing survival, reproduction, and dispersal processes on long-term demographic responses to climate change and highlight traits most likely to influence population responses to ongoing climate warming.</span></p>
Robust group- but limited individual-level (longitudinal) reliability and insights into cross-phases response prediction of conditioned fear
<p>Here we follow the call to target measurement reliability as a key prerequisite for individual-level predictions in translational neuroscience by investigating i) longitudinal reliability at the individual and ii) group level, iii) internal consistency and iv) response predictability across experimental phases. 120 individuals performed a fear conditioning paradigm twice six months apart. Analyses of skin conductance responses, fear ratings and blood oxygen level dependent functional magnetic resonance imaging (BOLD fMRI) with different data transformations and included numbers of trials were conducted. While longitudinal reliability was rather limited at the individual level, it was comparatively higher for acquisition but not extinction at the group-level. Internal consistency was satisfactory. Higher responding in preceding phases predicted higher responding in subsequent experimental phases at a weak to moderate level depending on data specifications. In sum, the results suggest that while individual-level predictions are meaningful for (very) short time frames, they also call for more attention to measurement properties in the field.</p>
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.