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Fig. 36 in Systematic revision of the parasitoid wasp genus Glyptapanteles Ashmead (Hymenoptera: Braconidae: Microgastrinae) for Australia results in a ten-fold increase in species
Fig. 36. Glyptapanteles goodwinnoakes Fagan-Jeffries, Bird & Austin sp. nov., paratype, ♀ (QM T250956). A. Lateral habitus. B. Dorsal head. C. Anterior head. D. Dorsal habitus. E. Fore wing. F. Lateral head.
Fig. 29 in Systematic revision of the parasitoid wasp genus Glyptapanteles Ashmead (Hymenoptera: Braconidae: Microgastrinae) for Australia results in a ten-fold increase in species
Fig. 29. Glyptapanteles doreyi Fagan-Jeffries, Bird & Austin sp. nov., paratype, ♀ (ANIC 32 130330), 'clade B'. A. Lateral habitus. B. Fore wing. C. Dorsal head. D. Dorsal metasoma. E. Anterior head. F. Lateral head. G. Dorsal mesosoma.
Fig. 7. A in Systematic revision of the parasitoid wasp genus Glyptapanteles Ashmead (Hymenoptera: Braconidae: Microgastrinae) for Australia results in a ten-fold increase in species
Fig. 7. A. Glyptapanteles harveyi Fagan-Jeffries, Bird & Austin sp. nov., paratype, ♀ (WAM E109889), arrow indicating faint median carina at the posterior end of the propodeum. B. G. kittelae Fagan-Jeffries, Bird & Austin sp. nov., holotype, ♀ (SAMA 32-46156), propodeum with median carina completely absent.
Fig. 8. A in Systematic revision of the parasitoid wasp genus Glyptapanteles Ashmead (Hymenoptera: Braconidae: Microgastrinae) for Australia results in a ten-fold increase in species
Fig. 8. A. Glyptapanteles andamookaensis Fagan-Jeffries, Bird & Austin sp. nov., holotype, ♀ (SAMA 32-035451), hind femur mostly dark. B. G. kittelae Fagan-Jeffries, Bird & Austin sp. nov., holotype, ♀ (SAMA 32-46156), hind femur mostly light brown.
Fig. 6. A. Glyptapanteles mnesampela Austin, 2000 in Systematic revision of the parasitoid wasp genus Glyptapanteles Ashmead (Hymenoptera: Braconidae: Microgastrinae) for Australia results in a ten-fold increase in species
Fig. 6. A. Glyptapanteles mnesampela Austin, 2000, holotype, ♀ (ANIC 32-141445), T1 and T2 pale. B. G. eburneus Fagan-Jeffries, Bird & Austin sp. nov., holotype, ♀ (AM K.517935), T1 and T2 pale. C. G. rixi Fagan-Jeffries, Bird & Austin sp. nov., holotype, ♀ (QM T250981), T2 pale, T1 darker than T2. D. G. mouldsi Fagan-Jeffries, Bird & Austin sp. nov., paratype, ♀, (QM T250978), T1 dark, T2 pale. E. G. dowtoni Fagan-Jeffries, Bird & Austin sp. nov., paratype, ♀ (QM T250953), T1 dark, T2 pale. F. G. harveyi Fagan-Jeffries, Bird & Austin sp. nov., paratype, ♀ (WAM E109889), T1 dark, T2 dark.
Fig. 5. A in Systematic revision of the parasitoid wasp genus Glyptapanteles Ashmead (Hymenoptera: Braconidae: Microgastrinae) for Australia results in a ten-fold increase in species
Fig. 5. A. Glyptapanteles albigena Fagan-Jeffries, Bird & Austin sp. nov., holotype, ♀ (ANIC 32 130334), arrow indicating a large pale gena spot. B. G. sanniopolus Fagan-Jeffries, Bird & Austin sp. nov., holotype, ♀ (ANIC 32 130370), arrow indicating a large pale gena spot. C. G. kittelae Fagan- Jeffries, Bird & Austin sp. nov., holotype, ♀ (SAMA 32-46156) arrow indicating small (clearly visible) pale gena spot. D. G. harveyi Fagan-Jeffries, Bird & Austin sp. nov., paratype, ♀ (WAM E109889), arrow indicating small (faint, barely visible) pale gena spot. E. G. baylessi Fagan-Jeffries, Bird & Austin sp. nov. paratype, ♀ (AM K.517936), gena without a pale spot.
Fig. 2 in Systematic revision of the parasitoid wasp genus Glyptapanteles Ashmead (Hymenoptera: Braconidae: Microgastrinae) for Australia results in a ten-fold increase in species
Fig. 2. Maximum likelihood phylogeny constructed using IQ-TREE ver. 1.6.12 of a concatenated COI and wingless alignment including Glyptapanteles Ashmead, 1904 from Australia, Papua New Guinea and Fiji, with specimens of Cotesia Cameron, 1891 from Australia included for contextual placement of the genus. Branch support values are given as SH-aLRT support (%) / ultrafast bootstrap support (%), with symbols representing value ranges as follows: * = 96–100; • = 91–95; ^ = 85–90; - = <85.
Fig. 3 in Systematic revision of the parasitoid wasp genus Glyptapanteles Ashmead (Hymenoptera: Braconidae: Microgastrinae) for Australia results in a ten-fold increase in species
Fig. 3. Known distribution of the described species of Glyptapanteles Ashmead, 1904 from Australia, represented by coloured circles or part circles, with each species represented by a different colour (see key to colours below map).
Fig. 4 in Systematic revision of the parasitoid wasp genus Glyptapanteles Ashmead (Hymenoptera: Braconidae: Microgastrinae) for Australia results in a ten-fold increase in species
Fig. 4. Distribution of species groups of Glyptapanteles Ashmead, 1904 in Australia. A. G. albigena species group. B. G. arcanus species group. C. G. austini species group. D. G. eburneus species group. E. G. mouldsi species group. F. G. niveus species group. G. Unplaced species of Glyptapanteles in Australia.
Results from the monitoring of veterinary medicinal product residues and other substances in live animals and animal products - Bosnia and Herzegovina
<p>This dataset contains the monitoring results of veterinary medicinal product residues and other substances measured in live animals and animal products analysed by the national competent authority of Bosnia and Herzegovina. The presence of unauthorised substances, residues of veterinary medicinal products or chemical contaminants in food may pose a risk factor for public health.</p> <p>For this reason and in order to ensure a high level of consumer protection, a comprehensive legislative framework has been established in the European Union (EU) which defines maximum limits permitted in food and monitoring programmes for the control of the presence of these substances in the food chain. Regulation (EU) No 37/2010 establishes maximum limits for residues of veterinary medicinal products in food-producing animals and animal products. Maximum residue levels for pesticides in or on food and feed of plant and animal origin are laid down in Regulation (EC) No 396/2005. Commission Regulation (EC) 1881/2006 lays down the maximum limits for the presence of certain contaminants in animal products. Council Directive 96/23/EC lays down measures to monitor certain substances and residues thereof, mainly veterinary medicinal products, in live animals and animal products. Additionally, Commission Decision 97/747/EC lays down levels and frequencies of sampling for certain animal products.</p> <p>The chemical monitoring data collected and published by EFSA include the analytical results provided by EU Member States and three pre-accession countries: Bosnia-Herzegovina, Montenegro, North Macedonia and Serbia.</p> <p>The dataset contains the results of laboratory tests from samples taken from bovines, pigs, sheep, goats, horses, poultry, rabbits, farmed game, wild game aquaculture, milk, eggs and honey.</p> <p>Targeted samples are taken with the aim of detecting illegal treatment or controlling compliance with the maximum levels laid down in the relevant legislation. This means that, in their national plans Member States target the groups of animals (species, gender, age) where the probability of finding residues is the highest.</p> <p>Suspect samples are taken as a consequence of i) non-compliant results on samples taken in accordance with the monitoring plan, ii) possession or presence of prohibited substances at any point during manufacture, storage, distribution or sale through the food and feed production chain, or iii) suspicion or evidence of illegal treatment or non-compliance with the withdrawal period for an authorised medicinal veterinary product.</p> <p>Residues of pharmacologically active substances mean active substances, excipients or degradation products and their metabolites, which remain in food.</p> <p>Unauthorised substances or products mean substances or products prohibited under European Union legislation.</p> <p>Non-compliant sample is a sample that has been analysed for the presence of one or more substances and failed to comply with the legal provisions for at least one substance. Thus, a sample can be non-compliant for one or more substances.</p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>VMPR_2020 – Veterinary Office of Bosnia and Herzegovina</p> <p>VMPR_2018 – Veterinary Office of Bosnia and Herzegovina</p>
SAT Competition 2004 Benchmarks and Raw Results
<p>Set of problems used for the SATISFIABILITY (SAT) competition organized in 2004. Problems are given in three categories: Industrial, Handmade (Now called "Crafted") and Random.</p>
Monte-Carlo-simulated MR spectra of the rat brain and their quantification results obtained with QUEST and QUEST-MM jMRUI algorithms
<p>MC-simulated spectra of the rat brain along with the corresponding basis set (metabolite signals simulated using NMRScopeB from jMRUI) and the QUEST-MM, QUEST, QUEST(Met+Back) and QUEST(Met+MM) quantification results are stored in the MC_results folder.</p> <p>Results of an in-vivo rat brain MRS signal (SPECIAL, dead time t0 = 0.134 ms, TE = 2.8 ms, at 9.4 T) quantification performed with the QUEST-MM jMRUI algorithm (origin for MC-simulation) are stored in the folder rat_results.</p> <p>All files can be loaded to jMRUI software version 5 and later.</p>
Results of Rock Magnetic and Mineralogical Analyses of Ultramafic Cores from the Oman Drilling Project
<p>This dataset comprises of original rock magnetic data, microscope images, EPMA data, and EDS reports used for analysis of serpentinized ultramafic cores drilled during the Oman Drilling Project (OmanDP).</p> <p>Rock magnetic experiments have been performed onboard D/V Chikyu and in a shielded facility located at the University of Iceland. Results included in the dataset are as follows: Mass susceptibility, Natural remanence before and after alternating field (AF) and thermal demagnetization, and thermomagnetic measurements. </p> <p>Microscopic images and EDS reports are the results of backscatter electron microscopy that was held at Seoul National University with a JEOL JSM-7100F scanning electron microscope. EPMA results were produced using the JXA-8530F electron probe micro-analyzer at the National Center for Inter-University Research Facilities (NCIRF) of Seoul National University (SNU).</p> <p> </p>
Small mammal ARTS: Orion receiver data for site radiomapping and vole tracking, and scripts and results for localization and activity estimates
<p class="FirstParagraph">This data set accompanies "An Automated Radio-Telemetry System (ARTS) for Monitoring Small Mammals". </p> <p class="FirstParagraph">The behavior of small fossorial mammals, such as voles, is extremely difficult to observe in natural environments. Small mammals were traditionally studied with labor intensive methods such as trapping and recapture or radio telemetry via homing, which require week/months of work and produce static home range estimates.</p> <p class="FirstParagraph">In pursuit of better understanding natural history and behavioral ecology we implemented an automated radio telemetry system (ARTS) to continuously monitor small mammals. We used an isotropic antenna array coupled with broadband receivers to estimate animal positions with nonlinear least squares, nonparameteric, and Bayesian trilateration methods. We then used Lomb-Scargle periodograms to estimate activity patterns of freely-behaving Prairie voles.</p>
Results of EUA survey on universities and innovation
<p>This database refers to the data collected by the European University Association (EUA) through its survey on universities and innovation in 2021. Designed to gather evidence about the state of innovation at European universities, the EUA survey took stock of how these institutions pursue their third mission and help deliver the sustainable and digital transitions. As such, it continues EUA’s long-standing work showcasing universities’ key contributions to innovation ecosystems, in a context of multiplying societal challenges and the increasing relevance of knowledge to devising new solutions. The full report published by the Association is available at: <a href="https://www.eua.eu/resources/publications/1014:universities-as-key-drivers-of-sustainable-innovation-ecosystems.html">https://www.eua.eu/resources/publications/1014:universities-as-key-drivers-of-sustainable-innovation-ecosystems.html </a></p> <p>All information that could lead to the identification of individual universities and other higher education institutions was removed from the database. The following files are available:</p> <ul> <li>EUA survey on universities and innovation</li> <li>Database in the following formats: .xlsx (Microsoft Excel) and .sav (IBM SPSS)</li> <li>Survey Codebook: includes information on all the variables and their coding (.xlsx)</li> <li>Data Management Plan.</li> </ul>
United Kingdom results from the monitoring of pesticide residues in food
<p>This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits.</p> <p>A sample is considered <strong>free of quantifiable residues</strong> if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy.</p> <p>If a sample <strong>contains quantifiable residues</strong> but within the legally permitted limit (maximum residue level, MRL), it is described as a sample with quantified residue levels within the legal limits (below or at the MRL)</p> <p>A sample is considered <strong>non-compliant</strong> with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.</p> <p> </p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>MOPER_2020 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2019 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2018 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2017 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2016 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2015 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2014 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2013 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2012 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2011 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p> </p> <p><strong>We are seeking feedback on our open data please complete the survey at the link below:<br> https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14</strong></p>
Modelled results for Potential Health and Economic Impacts of Shifting Manufacturing
<p>Files include modelled 100-year annual mean aerosol concentrations, zonal wind and meridional wind in the baseline and sensitivity simulations.</p>
PRASTOF - Results
<p>The PRASTOF (PRospective Aluminium STOck and Flows) open-source Python software aims to generate scenarios for future stocks and flows of aluminium alloys and alloying elements in line with the Shared socioeconomic pathways (SSP) framework in order to understanding the stock dynamics of this resource and enhance a better management of it towards a more sustainable and circular economy. A bottom-up approach that considers each final use sector individually was chosen to better depict the details regarding sectorial material demand, lifetimes, and alloys composition.</p> <p>This data set groups all results of PRASTOF.</p> <p>Data and code used to calculate the scenarios are available here: https://github.com/jpedneault/PRASTOF</p> <p>For the article associated with those results, see:</p> <p>Pedneault, J., Majeau-Bettez, G., Pauliuk, S., Margni, M. (2022). Sector-specific scenarios for future stocks and flows of aluminium – An analysis based on Shared Socioeconomic Pathways. Journal of Industrial Ecology. </p>
Fig. 15 in Results Of The 10-Year Monitoring Of Bat (Chiroptera, Vespertilionidae) Winter Aggregation From The North-Eastern Ukraine (Liptsy Mines, Kharkiv Region)
Fig. 15. Sex ratio in M. daubentonii throughout the year from bats recorded inside Liptsy 1: VIII–V — number of months August to June; n — number of bats.
Fig. 13 in Results Of The 10-Year Monitoring Of Bat (Chiroptera, Vespertilionidae) Winter Aggregation From The North-Eastern Ukraine (Liptsy Mines, Kharkiv Region)
Fig. 13. Bat activity (shown by lines) and relative abundance (shown by bars) during autumn swarming and spring departure periods — an example from Liptsy 1: VIII–V = number of months, August to May; IXs — summer period in September; IXa — autumn period in September; N — number of mist-netting nights; n — total number of bats in each period; Mdau — M. daubentonii; Mdas — M. dasycneme; Paur — P. auritus.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.