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FIGURE 1. A. Colours of Paraputo blackmani Joshi sp. n. adult females preserved in 70% ethanol; B. Type slide, with holotype specimen ringed in blue (indicated by a red arrow). in --A--new--species--of--Paraputo--Laing--1929--(Hemiptera:--Coccomorpha:-- Pseudococcidae)--from--India
FIGURE 1. A. Colours of Paraputo blackmani Joshi sp. n. adult females preserved in 70% ethanol; B. Type slide, with holotype specimen ringed in blue (indicated by a red arrow).
FIGURE 2. Paraputo blackmani Joshi sp. n., collection site and life stages. A. Butea monosperma (Lam.) Taub. tree with infested part indicated by arrows; B. A furrow left by a broken branch, with exposed cambium tissue infested by mealybugs; C. Mealybug nymphs, indicated by arrows; D. Adult females; E.Ants, Nylanderia sp. (Hymenoptera: Formicidae), attending the mealybugs; F. Spalgis epius (Westwood) larva (Lepidoptera: Lycaenidae) preying on the mealybugs. in --A--new--species--of--Paraputo--Laing--1929--(Hemiptera:--Coccomorpha:-- Pseudococcidae)--from--India
FIGURE 2. Paraputo blackmani Joshi sp. n., collection site and life stages. A. Butea monosperma (Lam.) Taub. tree with infested part indicated by arrows; B. A furrow left by a broken branch, with exposed cambium tissue infested by mealybugs; C. Mealybug nymphs, indicated by arrows; D. Adult females; E.Ants, Nylanderia sp. (Hymenoptera: Formicidae), attending the mealybugs; F. Spalgis epius (Westwood) larva (Lepidoptera: Lycaenidae) preying on the mealybugs.
Data in "The uncertainties in the laboratory-measured short-wave refractive indices of mineral dust aerosols and the derived optical properties: A theoretical assessment"
<p>This is the data for publication "The uncertainties in the laboratory-measured short-wave refractive indices of mineral dust aerosols and the derived optical properties: A theoretical assessment"</p> <p>Version 1: data</p> <p>Version 2: rename the data files and add a readme file</p>
Urban Heat: Forward-Looking Climate Modelling for West-Africa: extra indicator
<p>We produced actionable data on heat stress in cities to inform analysis and client dialogue on the part of World Bank teams. We applied an urban-scale climate modeling framework to generate datasets describing modeled heat stress exposure for present-day and future conditions under selected climate scenarios. This dataset serves as a supplement to the previous two datasets: https://zenodo.org/doi/10.5281/zenodo.11085333 and https://zenodo.org/doi/10.5281/zenodo.11073297. It contains an extra indicator Heat Index (HI) based on the apparent temperature (AT) for 12 cities in west Africa. </p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for HI across three scenarios (<strong>present, SSP2-4.5, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2031-2050, and 2051-2070</strong>). The present period refers to 2001-2020, while the other two periods correspond to the two SSP scenarios.</li> <li>The indicator is available in both <strong>NetCDF</strong> and <strong>GeoTiff</strong> formats. It is named as HIAT in the file name with extra information such as scenario, resolution, projection, etc. </li> <li>The indicator is calculated at a resolution from <strong>100 m </strong>to <strong>200 m </strong>(depending on the size of the city). Additionally, downscaled versions of the indicator is provided at a resolution of <strong>30 m</strong>.</li> <li>The Heat Index is calculated as the <strong>yearly average number of days when apparent temperature reaches 105 F</strong>. More information regarding the definition and calculation of HI can be found: Rohat, G., Flacke, J., Dosio, A., Dao, H., & Van Maarseveen, M. (2019). Projections of human exposure to dangerous heat in African cities under multiple socioeconomic and climate scenarios. <em>Earth's Future</em>, <em>7</em>(5), 528-546.</li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format are available. </li> <li>The indicators in NetCDF and GeoTiff format as well as the visualized PNG files can be found in the <strong>{city}_HIAT.zip </strong>(for the cities with future projection) or <strong>{city}</strong><strong>_present_HIAT.zip </strong>(for those cities without future projection).</li> <li>More information about other indicators, including the simulation, methodology, all available data list, contact information, etc. can be found in the other two datasets.</li> </ul>
ERA5-Land selected indicators daily aggregates for Africa, 2005
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2005.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
Figure 3 in Tetraploidy in the Boettger's dwarf clawed frog (Pipidae: Hymenochirus boettgeri) from the Congo indicates non-conspecificity with the captive population
Figure 3. Karyotypes of (A) Hymenochirus boettgeri (IVB-H-CG17-356, male) with 18 homologous chromosome pairs, and (B) Hymenochirus sp. (IVB-H-Hsp06, female) with 10 pairs of A chromosomes and one B chromosome, arranged from Giemsa-stained chromosomes. Chromosomes were cut from metaphase spreads on the left. Long lines in karyotype arrangements indicate the position of chromosome centromere. Short vertical and horizontal lines correspond to the scale = 10 μm.
Figure 4 in Tetraploidy in the Boettger's dwarf clawed frog (Pipidae: Hymenochirus boettgeri) from the Congo indicates non-conspecificity with the captive population
Figure 4. Hymenochirus boettgeri (IVB-H-CG17-356, male), sequential fluorescent chromosome mapping—DAPI, CMA3, C-banding, ribosomal DNA (rDNA) FISH, small nuclear DNA (snDNA) FISH; and non-sequential whole-genome painting on metaphase spread. A, DAPI (black and white, B&W) consistently stains all 36 chromosomes. B, CMA3 banding in green shows nucleolar secondary constriction (NOR locus) on the p arm of chromosome 4 that co-localizes with 28S. C, C-banding (B&W, brighter staining) highlights heterochromatic blocks on telomeric and pericentromeric regions of seven homologous chromosomes (14 arrows). D, FISH with 28S (red) ribosomal probes shows the p arm of chromosome 4. E, FISH with U1 (red) and U2 (green) snDNA probes shows very weak signals. The U1 probe maps to the q arm of chromosome 1, the U2 probe maps to the q arm of chromosome 8. F, genomic in situ hybridization (GISH) with Hymenochirus sp. whole-genome painting DNA probe that hybridizes to all 36 chromosomes with different intensity. Arrows show the less intensely painted chromosome pair. Scale bars represent 10 μm.
Figure 6 in Tetraploidy in the Boettger's dwarf clawed frog (Pipidae: Hymenochirus boettgeri) from the Congo indicates non-conspecificity with the captive population
Figure 6. Schematic representation of the chromosomal location of the U1 (red) and U2 (green) snDNAs, 5S (dark blue) and 28S (yellow) rDNAs, and C-bands (dark grey) in H. boettgeri (Congo) and Hymenochirus sp. (captive population). The haploid A chromosome set of each species, 18 chromosomes in H. boettgeri and 10 chromosomes in Hymenochirus sp., is arranged in descending order of size. The B chromosome of Hymenochirus sp. is depicted separately from the A chromosomes and is entirely covered in grey, as revealed by C-banding. The 5S rDNA locus was not detected in H. boettgeri and is only depicted in the Hymenochirus sp. karyotype. Mapping of the U1 snDNA locus identified a pericentric inversion or copy number reduction/expansion visible on non-homologous regions of chromosome 1. Created with BioRender.com.
Figure 5 in Tetraploidy in the Boettger's dwarf clawed frog (Pipidae: Hymenochirus boettgeri) from the Congo indicates non-conspecificity with the captive population
Figure 5. Hymenochirus sp. (IVB-H-Hsp06, female), sequential fluorescent chromosome mapping (DAPI, CMA3, C-banding, rDNA FISH), non-sequential snDNA FISH, and whole-genome painting on metaphase spread. A, DAPI (B&W) counter-stained metaphase spread shows all 21 chromosomes. B, CMA3 banding in green shows NOR locus on the p arm of chromosome 4. CMA3 signal co-localizes with 28S locus. C, C-banding (B&W, brighter staining) highlights heterochromatic blocks on telomeric and pericentromeric regions of almost all chromosomes. In addition, the whole B chromosome is intensely banded (arrow). D, 5S (green) and 28S (red) rDNA loci are located on the q arm of chromosome 6 and p arm of chromosome 4, respectively. The 5S rDNA is situated on two different chromosomal loci within the single q arm. E, the snDNA loci U1 (red) and U2 (green) are located on the p arm of chromosome 1 and the q arm of chromosome 8, respectively. F, the GISH experiment of the H. boettgeri whole-genome painting probe, which hybridizes on Hymenochirus sp. chromosomes. All chromosomes are painted (red) except one, B chromosome, which shows no GISH signal and is DAPI-positive (arrow). Scale bars represent 10 μm.
Figure 2 in Tetraploidy in the Boettger's dwarf clawed frog (Pipidae: Hymenochirus boettgeri) from the Congo indicates non-conspecificity with the captive population
Figure 2. Phylogenetic trees of dwarf clawed frogs. Maximum likelihood mtDNA (16S, left) and nDNA (rag1, right) trees showing the positions of karyotyped individuals (in bold) in the context of available molecular sampling retrieved from GenBank (acc. nos. listed). For sampling details, see Supporting Information, Table S1.
Figure 1 in Tetraploidy in the Boettger's dwarf clawed frog (Pipidae: Hymenochirus boettgeri) from the Congo indicates non-conspecificity with the captive population
Figure 1. Dwarf clawed frogs, Hymenochirus sp. (captive population) and H. boettgeri. A, Hymenochirus sp., female (IVB-H-Hsp06) and male (IVB-H-Hsp02) in amplexus. B, Hymenochirus sp., female in dorsolateral view (IVB-H-Hsp04). In (A) and (B), note the relatively smooth flanks and hindlegs with homogeneous, unenlarged tubercles. C, Hymenochirus boettgeri from the north-western part of the Republic of the Congo (male, IVB-H-CG17-356). D, holotype of H. boettgeri, female (ZMB 11521). The area marked by the red rectangle is detailed in (E). F, Hymenochirus boettgeri from the same locality as the karyotyped individual (IVB-H-CG17-112, male). In (C–F), note the enlarged and spiny tubercles on the flanks and hindlegs typical for H. boettgeri.
DMI Klimaatlas v2024a - Fremskrivninger af det danske klima (Projections of climate indicators in Denmark)
<p>See below for English version</p> <p> ---</p> <p><strong>DMI Klimaatlas v2024a - Fremskrivninger af det danske klima</strong></p> <p>Klimaatlas leverer ét samlet datagrundlag for det fremtidige danske klima. Klimaatlas er udarbejdet på baggrund af DMI's egne data, internationale samarbejder og viden fra rapporter fra FN’s Klimapanel (IPCC). Finansieringen kommer fra Finansloven 2018 og 2022. Klimaindikatorer er udregnet og samlet for hele Danmark, kommuner, alle vandoplande (afvandingsområder) og kyststrækninger, i et højtopløsningsgitter (1x1 km). </p> <p>Denne opdatering tilføjer 6 nye indikatorer for brandfare og tørre perioder:</p> <ul> <li>Brandfare <ul> <li>Gennemsnit af brandfareindekset.</li> <li>Dage med ’meget høj’ brandfare.</li> <li>Dage med ’ekstrem’ brandfare.</li> </ul> </li> <li>Tørre perioder <ul> <li>Andel af år/årstider som er ’tørre’.</li> <li>Tørre perioder på minimum 5 døgn.</li> <li>Tørre perioder på minimum 10 døgn.</li> </ul> </li> </ul> <p> Alle indikatorer fra tidligere udgaver findes også i dette datasæt.</p> <p>Ud over dette er 7 nye klimamodeller blevet tilføjet til ensemblet og antallet af modeller dermed steget til 72. Alle indikatorer er blevet genberegnet med den udvidet ensemble.</p> <p>Formatet af Excel-regnearkerne er blevet opdateret med fokus på maskinlæsbarhed.</p> <p>Datasættet indeholder de følgende elementer:</p> <ul> <li><strong>DMI_Klimaatlas_v2024a_Danmark_rapport.pdf</strong> – Klimaatlas rapport som giver et overblik over klimaforandring i Danmark.</li> <li><strong>DMIrapport_24_11.pdf</strong> - "Methods used in the Danish Climate Atlas", tekniske rapport som beskriver hvordan Klimaatlas data beregnes (på engelsk).</li> <li><strong>DMI_Klimaatlas_v2024a_Alle_indikator.xlsx</strong> – Microsoft Excel regneark med alle Klimaatlas indikatorer.</li> <li><strong>DMI_Klimaatlas_v2024a_Kommune_rapporter.zip</strong> - PDF rapporter om klimaforandringer i alle 98 kommuner i Danmark.</li> <li><strong>DMI_Klimaatlas_v2024a_Excel_regnearker.zip</strong> - Microsoft Excel-regneark med indikatorer opdelt efter kommuner, vandopleande eller kystrækninger.</li> <li><strong>DMI_Klimaatlas_v2024a_Udvidet_havniveau.xlsx</strong> – Udvidet havniveau datasæt til ekspertbrugere som har behov for en højere tidsopløsning, længere tidsdækning og eller andre scenarier for havniveaustigning.</li> <li><strong>DMI_Klimaatlas_v2024a_NetCDF_indicators.zip</strong> – Indikatorer på en 1km gitter over Danmark i NetCDF format.</li> </ul> <p>----</p> <p><strong>DMI Klimaatlas v2024a - Projections of climate indicators in Denmark</strong></p> <p>DMI’s <em>Klimaatlas</em> provides data for the future Danish climate. Klimaatlas is based on DMI's own data, international collaborations and knowledge from reports by the Intergovernmental Panel on Climate Change (IPCC). Funding comes from the Danish Finance Act 2018 and 2022. Climate indicators are calculated and compiled for all of Denmark, municipalities, all water basins (catchment areas) and coastlines, on a high-resolution grid (1x1 km). </p> <p>This update adds 6 new indicators for fire danger and dry periods:</p> <ul> <li>Fire danger <ul> <li>Average of the fire danger index.</li> <li>Days with ‘very high’ fire danger.</li> <li>Days with ‘extreme’ fire danger.</li> </ul> </li> <li>Dry periods <ul> <li>Dry periods of minimum 5 days.</li> <li>Dry periods of minimum 10 days. </li> <li>Proportion of years/seasons that are ‘dry’. </li> </ul> </li> </ul> <p>All indicators from previous versions are also available in this dataset.</p> <p>In addition, 7 new climate models have been added to the ensemble, increasing the number of models to 72. All indicators have been recalculated with the extended ensemble.</p> <p>The format of the Excel spreadsheets has been updated with a focus on machine readability.</p> <p>The dataset consists of the following elements:</p> <ul> <li><strong>DMI_Klimaatlas_v2024a_Danmark_rapport.pdf</strong> – <em>Klimaatlas</em> report describing the effects of climate change in Denmark (in Danish).</li> <li><strong>DMIrapport_24_11.pdf</strong> - "Methods used in the Danish Climate Atlas", technical report describing the methods used in generating <em>Klimaatlas</em> data.</li> <li><strong>DMI_Klimaatlas_v2024a_Alle_indikator.xlsx</strong> – Microsoft Excel spreadsheet with all <em>Klimaatlas</em> indicators (in Danish).</li> <li><strong>DMI_Klimaatlas_v2024a_Kommune_rapporter.zip</strong> - PDF report summarising the findings of <em>Klimaatlas</em> for each of the 98 municipalities in Denmark (in Danish).</li> <li><strong>DMI_Klimaatlas_v2024a_ Excel_regnearker.zip</strong> - Microsoft Excel spreadsheet with indicators divided by municipalities ("<em>Kommune</em>"), coastal stretches ("<em>Kyststrækninger</em>") and drainages ("<em>Vandoplande</em>") (in Danish).</li> <li><strong>DMI_Klimaatlas_v2024a_Udvidet_havniveau.xlsx</strong> – Extended sea level rise dataset for expert uses that have a need for a higher time resolution, longer time coverage or other climate scenarios for sea level rise (in Danish).</li> <li><strong>DMI_Klimaatlas_v2024a_NetCDF_indicators.zip</strong> – Indicators on a 1km grid over Denmark in the NetCDF format (in Danish).</li> </ul>
Codes: An early warning indicator trained on stochastic disease-spreading models with different noises
<p>This dataset contains the training data (Version V1) and all the codes (Version V2) of the paper entitled "An early warning indicator trained on stochastic disease-spreading models with different noises." </p> <p>Time series and corresponding residuals from white noise (equation 2.5), environmental noise (equation 2.8), and demographic noise (equation 2.9) are stored in the training_data_WhiteN, training_data_EnvN, and training_data_DemN folders, respectively. All residuals of the time series are contained in the training_resids folder, which also includes labels and groups of the training data. For details on the data generation process, please refer to section 3.1 in the paper. </p>
Dataset for "Stability of H2O-rich fluid in the deep mantle indicated by the MgO-SiO2-H2O phase relations at 23 GPa and 2000 K" by H. Fei
<p>This dataset provides the EPMA and XRD data for "Stability of H2O-rich fluid in the deep mantle indicated by the MgO-SiO2-H2O phase relations at 23 GPa and 2000 K" by H. Fei.</p>
Inheritance taxation and human capital indicators
<p>Data on inheritance taxation and human capital indicators</p>
Abundance Trend Indicator - Models, Prediction, Stacked Environmental Data and Training Set Similarity
<p># Readme</p> <p>These trained models can be used to predict the abundance trends of New Zealand's forest species and can be used together with the code in https://github.com/lnilya/abundance-trend-indicator</p> <p>Since the process of using the models requires coding expertise and some setting up, please make sure to reach out to ilya.shabanov@vuw.ac.nz for any questions. All files will require the code in the repository to be read and used. </p> <p>If you want to explore the results generated with these models, please visit https://ati-nz-predictions-7e6f3d514735.herokuapp.com/ for a user-friendly, interactive UI.</p> <p>## Contents</p> <p>_models: Contains the trained models (Artificial Neural Network (ANN), Random Forest (RF), SVMW (Support vector machine) and GLM (logistic regression)) at different degrees of noise filtering, different datasets and variable sets. The model files also contain test and training scores. To load the files please refer to the readme in the code repository: ttps://github.com/lnilya/abundance-trend-indicator</p> <p><br>_predictions/_environment: Contains the predictor variables for the study area (New Zealand, 1950-2019) that are needed by the models to make predictions. </p> <p>_predictions/_similarity: Contains the masks of areas that can be predicted by models and are similar to the training set.</p> <p>_predictions/_ati: Contain the predicted results for the abundance trend. These can be explored on https://ati-nz-predictions-7e6f3d514735.herokuapp.com/ </p> <p> </p>
Data to accompany "Harnessing Artificial Intelligence for efficient systematic reviews: A case study in ecosystem condition indicators"
<p>This dataset includes the data used for analysis and results that are the basis of the journal article Nicholson Thomas, I., Roche, P., Gret-Regamey, A. in revei<em>w. Harnessing Artificial Intelligence for efficient systematic reviews: A case study in ecosystem condition indicators. </em>It includes the details of papers used in testing of prompts and their respective classifications applied by expert reviewers and gpt-3.5 model.</p>
Data and Code for the manuscript "Confounding effects of leaf angle dynamics on vegetation indices - implications for monitoring vegetation from space"
<p>This repository includes the data and code to reproduce the results for the study "<em>Confounding effects of leaf angle dynamics on vegetation indices - implications for monitoring vegetation from space</em>". The study shows that leaf angle dynamics systematically confound widely applied vegetation indices. Moreover, it is demonstrated that these effects are not random but tightly linked to abiotic environmental conditions. These findings demonstrate both challenges and opportunities for using VIs for vegetation monitoring.</p>
How stable are solar-type cycles over half a century? Chromospheric S-indices Mt. Wilson vs. TIGRE
<p> <br>by Klaus-Peter Schröder (1); Jürgen Schmitt (2); Marco Mittag (2)<br>Departamento de Astronomia, Universidad de Guanajuato, Mexico; Hamburger<br>Sternwarte, University of Hamburg, Germany</p> <p>Since autumn 2013, the robotic 1.2m telescope TIGRE (formerly the Hamburg Robotic<br>Telescope) is operating in Guanajuato (Mexico, and see poster by Gonzalez et al.). Regularly<br>using the same calibration stars for S-indices as did the Mount Wilson project (see Baliunas et<br>al. 1995), our R=20,000 HEROS echelle spectra have provided a decade of chromospheric<br>activity monitoring of the same ca. 100 solar-type stars, including a smaller number with<br>pronounced cycles (see Schröder et al. 2013) of periods of o(10yrs), which were already<br>observed at Mount Wilson since the 1970ies. Hence, on a timescale of half a century, we can<br>now use those same stars, first shown by Olin C. Wilson and his group to perform solar-type<br>activity cycles, to probe the stability of these cycles. This is motivated by the solar cycle<br>variability, which is evident, when comparing the last two, relatively weak maxima monitored by<br>TIGRE, with the Mount Wilson data from the 1970ies and 1980ies. Hence, we here show that<br>this is not a singular behavior. Strong cycles seem to be less variable, while weaker cycles like<br>the solar are natural to change between stronger and weaker performance. In the case of the<br>Sun this behavior is consistent with an o(century)-period as of the semi-regular Gleissberg<br>cycle, as well as with the occurrence of grand-minima.</p> <p><br>Theme(s): The Sun and Cool Stars in the Time Domain, Cool Stars as Stellar Systems</p>
Fig 8 in New species of Kuqaia from the Lower Jurassic of Sweden indicates a possible water flea (Crustacea: Branchiopoda) affinity
Fig 8. Geological ranges, palaeogeographic distributions, and apparent abundance pattern of Kuqaia. The base and top of Kuqaia stratigraphic ranges from each starred locality were tentatively calibrated based on material from offshore mid-Norway and the northern North Sea. The palaeogeographic map was modified after [38] under the Creative Commons Attribution 4.0 International License.
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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.