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S116 | REFCCS | Collision Cross Section (CCS) Values from Literature
<p>This is the collection associated with list S116 REFCCS Collision Cross Section Values from Literature on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>This dataset is an entry point for users to contribute collision cross section values from the literature for addition to the NORMAN SLE, SusDat and the PubChem Collision Cross Section annotations.</p>
Рис. 2. Распределение Значений биомассы и численности Macoma balthica по станциЯм отбора проб. Fig. 2. Distribution of the Macoma balthica biomass and abundance values at sampling stations. in Species composition and distribution of bivalve mollusks in plankton and benthos in Nevelsky Strait in summer
Рис. 2. Распределение Значений биомассы и численности Macoma balthica по станциЯм отбора проб. Fig. 2. Distribution of the Macoma balthica biomass and abundance values at sampling stations.
Рис. 5. Коррелограммы покаЗателей обилиЯ наЗемных моллюсков раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – H. lucorum, участок № 1, 2010 г.; B – Ch. tridens, участок № 2, 2011 г.; C – Ch. tridens, участок № 4, 2012 г.); D – Ch. tridens, участок № 5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 5. Spatial correlogram of the land snail different age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – H. lucorum, site 1, 2010; B – Ch. tridens, site 2, 2011; C – Ch. tridens, site 4, 2012; D – Ch. tridens, site 5, 2012 (Moran index confidence value presented by filled signs). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 5. Коррелограммы покаЗателей обилиЯ наЗемных моллюсков раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – H. lucorum, участок № 1, 2010 г.; B – Ch. tridens, участок № 2, 2011 г.; C – Ch. tridens, участок № 4, 2012 г.); D – Ch. tridens, участок № 5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 5. Spatial correlogram of the land snail different age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – H. lucorum, site 1, 2010; B – Ch. tridens, site 2, 2011; C – Ch. tridens, site 4, 2012; D – Ch. tridens, site 5, 2012 (Moran index confidence value presented by filled signs).
Рис. 4. Коррелограммы покаЗателей обилиЯ наЗемного моллюска M. cartusiana раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.); D – участок № 5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 4. Spatial correlogram of land snail M. cartusiana age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Moran index confidence value presented by filled sings). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 4. Коррелограммы покаЗателей обилиЯ наЗемного моллюска M. cartusiana раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.); D – участок № 5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 4. Spatial correlogram of land snail M. cartusiana age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Moran index confidence value presented by filled sings).
Рис. 3. Коррелограммы покаЗателей обилиЯ наЗемного моллюска B. cylindrica раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.); D – участок №5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 3. Spatial correlogram of the land snail B. cylindrica age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Moran index confidence value presented by filled signs). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 3. Коррелограммы покаЗателей обилиЯ наЗемного моллюска B. cylindrica раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.); D – участок №5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 3. Spatial correlogram of the land snail B. cylindrica age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Moran index confidence value presented by filled signs).
РИС. 11. ИЗображениЯ крючков глохидиев при раЗной настройке Яркости и контрастности (Anemina arcaeformis, р. Сита, Хабаровский кр.). А. Оптимальные Яркость и контрастность. В. ИЗбыточнаЯ контрастность. С. ИЗбыточнаЯ Яркость. МасШтаб 20 мкм. Микроскоп Zeiss EVO 40, напыление углеродом. FIG. 11. Imagines of glochidia hooks with different brightness and contrast value (Anemina arcaeformis, Sita River, Khabarovsk Krai). A. Optimal brightness and contrast. B. Excessive contrast. C. Excessive brightness. Scale bars 20 μm. Zeiss EVO 40 microscope, sputter coating with carbon. in Методика подготовки раковин глохидиев (Bivalvia, Unionidae) длЯ работы на сканируюЩем Электронном микроскопе
РИС. 11. ИЗображениЯ крючков глохидиев при раЗной настройке Яркости и контрастности (Anemina arcaeformis, р. Сита, Хабаровский кр.). А. Оптимальные Яркость и контрастность. В. ИЗбыточнаЯ контрастность. С. ИЗбыточнаЯ Яркость. МасШтаб 20 мкм. Микроскоп Zeiss EVO 40, напыление углеродом. FIG. 11. Imagines of glochidia hooks with different brightness and contrast value (Anemina arcaeformis, Sita River, Khabarovsk Krai). A. Optimal brightness and contrast. B. Excessive contrast. C. Excessive brightness. Scale bars 20 μm. Zeiss EVO 40 microscope, sputter coating with carbon.
Extreme Total Water Level values for different possible combinations along the Southern Veneto, Emilia-Romagna and Northern Marche coastlines
<p><strong>Description:</strong> This data set is the result of the calculation of the extreme values of different Total Water Level combinations, including a range of components (Mean Sea Level, Tide, Residual, wave setup, wave runup). A total of 8 possible combinations:</p> <table> <tbody> <tr> <td> <p><strong>TWL Acronyms</strong></p> </td> <td> <p><strong>Used component</strong></p> </td> </tr> <tr> <td> <p>dCUE</p> </td> <td> <p>MSL (base) + Tide (Highs) + Residual + Estimated nearshore components (setup and runup)</p> </td> </tr> <tr> <td> <p>dCUU</p> </td> <td> <p>MSL (base) + Tide (Highs) + Residual + Univariated nearshore components (setup and runup)</p> </td> </tr> <tr> <td> <p>dTU</p> </td> <td> <p>TWL dynamic [MSL (base) + Tide (Highs) + Residual + Univariated nearshore components (setup and runup)]</p> <p>Based on Sanuy et al. (2020)</p> </td> </tr> <tr> <td> <p>dWUU</p> </td> <td> <p>Water Level [MSL (base) + Tide (Highs) + Residual] + Univariated nearshore components (setup and runup)</p> </td> </tr> <tr> <td> <p>sCUE</p> </td> <td> <p>MSL (base) + Tide (Highs) + Residual + Estimated Setup</p> </td> </tr> <tr> <td> <p>sCUU</p> </td> <td> <p>MSL (base) + Tide (Highs) + Residual + Univariated Setup</p> </td> </tr> <tr> <td> <p>sTU</p> </td> <td> <p>TWL static[ MSL (base) + Tide (Highs) + Residual + Univariated setup]</p> <p>Based on Sanuy et al. (2020)</p> </td> </tr> <tr> <td> <p>sWUU</p> </td> <td> <p>Water Level [ MSL (base) + Tide (Highs) + Residual] + Univariated Setup</p> </td> </tr> </tbody> </table> <div> </div> <div>The database has 3 representative extreme values for each type of frequency of event occurrence (High, Medium, Low).</div> <p>Each coastal point has the data using 3 slopes: 0.02, 0.03, 0.04. The points have a 0.025º (~2.5 km) distance between each other and 20 m depth.</p> <p><strong>Data location</strong>: The points with the data are located along the Italian coast of Regions: Veneto, Emilia-Romagna and Marche. Using the reference system WGS 84 / UTM zone 32N (EPSG:32632).</p> <p><strong>Fundings:</strong> The creation of this dataset was supported by the Autorità di Bacino Distrettuale del Fiume Po in the framework of the project “Approfondimento tecnico-scientifico sui quadri conoscitivi in ambito costiero”, coordinator Prof. P. Ciavola, University of Ferrara.</p> <p><strong>Disclaimer:</strong> Regardless of whether the data is quality-controlled or not, the authors do not accept any responsibility for the correctness and/or appropriate interpretation of the data. The correct and appropriate interpretation of the data must follow scientific rules and is always the exclusive responsibility of the users.</p>
Data from: The scope and adaptive value of modulating aggression across breeding stages: Case study in a competitive female songbird
<p><span><span>In seasonally breeding animals, costs and benefits of territorial aggression should vary over time; however, little work thus far has directly examined the scope and adaptive value of individual-level plasticity in aggression across breeding stages. We explore these issues using </span><span>the tree swallow (</span></span><em><span><span>Tachycineta bicolor</span></span></em><span><span>), a bird species in which females compete for limited nesting sites</span> <span>before producing a single brood. We measured the aggressiveness of nearly 100 females within three different stages: (1) shortly after territory-establishment, (2) during early incubation, and (3) while caring for young chicks. </span></span><span><span>We used k-means clustering to categorize females into four distinct plasticity 'types' based on the timing, direction, and magnitude of their changes in aggression between stages. We then tested whether plasticity type and stage-specific aggression </span><span>vary</span><span> with </span><span>key</span><span> performance metrics.</span></span><span><span> Two of the four</span> <span>plasticity</span><span> types became less aggressive </span><span>across consecutive breeding stages</span><span>, consistent with population-level patterns, though these plasticity types </span><span>largely </span><span>did not differ from one another in survival or reproductive success</span></span><span><span>. A third type was characterized by high levels of among-stage plasticity</span><span>; </span><span>these females</span><span>, </span><span>had </span><span>significantly </span><span>lower body mass while parenting, </span><span>tended to hatch fewer eggs,</span> <span>and </span><span>had the lowest observed </span><span>overwinter survival </span><span>rates</span><span>. </span><span>A final type exhibited </span><span>limited</span><span> plasticity, with moderate to low levels of aggression </span><span>in all stages; </span><span>this low plasticity </span><span>-</span><span> low aggression phenotype</span><span> was not associated </span><span>with any </span><span>negative</span> <span>effects to </span><span>performance</span><span>.</span> <span>These</span><span> results reveal substantial among-individual variation in behavioral plasticity, which may reflect diverse solutions to trade-offs between current reproduction and future survival.</span></span></p>
Storage time and temperature affect plasma osmolality values in field-collected blood samples
<p>Data and code for a study assessing the effect of storage conditions on blood plasma samples, published in Comparative Biochemistry and Physiology in 2024.</p>
CEOS LPV DIRECT V2.1: A database of upscaled LAI, FAPAR and Fcover values for satellite biophysical product validation
<p>Ground references of high quality are needed to validate satellite-based products. The DIRECT V2.1 database compiles Leaf Area Index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR) and fraction of vegetation cover (FCover) averaged values over a 3 km x 3 km area. The ground data was upscaled using high spatial resolution imagery following CEOS WGCV LPV (so called CEOS LPV) LAI validation good practices (Fernandes et al., 2014) to properly account for the spatial heterogeneity of the site. Ground measurements performed during several international Cal/Val activities, including VALERI, BigFoot, SAFARI-2000, CCRS, Boston University, were compiled by S. Garrigues (Garrigues et al., 2008) in the DIRECT database, and later ingested in the CEOS LPV OLIVE tool (Weiss et al., 2014) for accuracy assessment.</p> <p>F. Camacho reviewed DIRECT to remove those sites without understory measurements (Camacho et al., 2013) and after that expanded the database adding the ImagineS network of sites (Camacho et al., 2021). DIRECT V2.1 is the last update including 44 new sites from China (Fang et al., 2019; Song et al., 2021) and 2 more sites from ESA FRM4Veg project (Brown et al., 2021).</p> <p>The CEOS LPV DIRECT V2.1 database constitutes a major effort of the international community to provide ground reference for the validation of satellite-based LAI and FAPAR ECVs, with a total of 176 sites around the world (7 main biome types) and 280 LAI values, 128 FAPAR and 122 FCOVER values covering the period from 2000 to 2021.</p> <p> </p> <p><strong><u>Data description</u></strong></p> <ul> <li>LAI, LAIeff, FAPAR and FCOVER upscaled values over 3 km x 3 km.</li> <li>LAI_NoUnderstory, refers to sites where only overstory was measured and thus are not recommended for accuracy assessment of satellite products.</li> </ul> <p><strong><u>File contents</u></strong></p> <p>A Header " Sites":</p> <p>General information for each site (coordinates, landcover, method, reference)</p> <p>For each variable:</p> <ul> <li># number of the site (site description in Sites)</li> <li>Lat_cen, latitude centre of 3km x 3km</li> <li>Lon_cen, longitude centre of 3km x 3km</li> <li>Site name, name of the site</li> <li>Date, MM/DD/YYYY</li> <li>Mean, average value over 3km x 3km</li> <li>Uncert, uncertainty over 3 km x 3km (STD)</li> </ul> <p> </p>
Figure 1 in The value of regular monitoring and diverse sampling techniques to assess aquatic non-native species: a case study from Orkney
Figure 1. Locations of the monitoring sites. A: North of Orkney Mainland and northern isles, B: Scapa Flow and southern isles. For corresponding site names refer to Supplementary material Table S1.
Figure 3 in The value of regular monitoring and diverse sampling techniques to assess aquatic non-native species: a case study from Orkney
Figure 3. The total number of sites for which each non-native or cryptogenic species has been recorded for each sampling method for 2016 and 2017. NB: Only species recorded in these two years are reported in this figure. Abbreviations: Cm: Caprella mutica, As: Ascidiella scabra, Bh: Bonnemaisonia hamifera, Sj: Schizoporella japonica, Ce: Corella eumyota, Aa: Ascidiella aspersa, Cp: Colpomenia peregrina, Cf: Codium fragile ssp. fragile, Ti: Tricellaria inopinata, Ah: Asterocarpa humilis, Bs: Botryllus schlosseri, Bl: Botrylloides leachii, Mh: Melanothamnus harveyi, Bf: Bugulina fulva, Dj: Dasysiphonia japonica, Jm: Jassa marmorata, Mi: Monocorophium insidiosum, Ma: Monocorophium acherusicum, Cs: Ctenodrilus serratus, Tj: Telmatogeton japonicus, Pa: Potamopyrgus antipodarum, Cb: Crassicorophium bonellii, Dl: Diplosoma listerianum.
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable.
Figure 2 in Value and impacts of collecting vertebrate voucher specimens, with guidelines for ethical collection
Figure 2. Decision process when considering collecting voucher specimens. Questions to consider are given in blue, with responses to each question given in black. Directions on how to proceed through the process are given in green. Further details and examples are provided in the text
Figure 1 in Value and impacts of collecting vertebrate voucher specimens, with guidelines for ethical collection
Figure 1. Geographic distribution of Museum Victoria's non-marine Victorian vertebrate specimens. Each pixel represents the number of specimens collected within a 100 km2 grid (10 x 10 km square) from the earliest georeferenced specimen (collected in 1858) to 2012, spanning 154 years. Yellow, green, light blue, and dark blue pixels represent single (1), low (2 – 10), moderate (11 – 100), and high (> 100) numbers of specimens, respectively. White pixels represent areas with no specimens. Histograms represent the number of specimens by decade.
Mapping the existing food systems, value chains and markets for agroecological products
<table> <tbody> <tr> <td>The food systems and markets of our focal farming systems are mapped as well as communities across the value chain, including operations from production over to food disposal after consumption, all along with the contribution of these operations to socio-economic and environmental outcomes. The datasets are the results of survey/interviews on the above topic in the CANALLS project ALLs.<span> </span></td> </tr> </tbody> </table>
Dataset: First Trust Small Cap Value AlphaDEX Fund (FYT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: First Trust Large Cap Value AlphaDEX Fund (FTA) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: First Trust Mid Cap Value AlphaDEX Fund (FNK) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: iShares MSCI Japan Value ETF (EWJV) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
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.