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Data from: Host manipulation by an ichneumonid spider ectoparasitoid that takes advantage of preprogrammed web-building behaviour for its cocoon protection
Host manipulation by parasites and parasitoids is a fascinating phenomenon within evolutionary ecology, representing an example of extended phenotypes. To elucidate the mechanism of host manipulation, revealing the origin and function of the invoked actions is essential. Our study focused on the ichneumonid spider ectoparasitoid Reclinervellus nielseni, which turns its host spider (Cyclosa argenteoalba) into a drugged navvy, to modify the web structure into a more persistent cocoon web so that the wasp can pupate safely on this web after the spider's death. We focused on whether the cocoon web originated from the resting web that an unparasitized spider builds before moulting, by comparing web structures, building behaviour and silk spectral/tensile properties. We found that both resting and cocoon webs have reduced numbers of radii decorated by numerous fibrous threads and specific decorating behaviour was identical, suggesting that the cocoon web in this system has roots in the innate resting web and ecdysteroid-related components may be responsible for the manipulation. We also show that these decorations reflect UV light, possibly to prevent damage by flying web-destroyers such as birds or large insects. Furthermore, the tensile test revealed that the spider is induced to repeat certain behavioural steps in addition to resting web construction so that many more threads are laid down for web reinforcement.
Fig. 8 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)
Fig. 8. Plot of the non-metric multidimensional scaling (nMDS) based on the by Bray–Curtis similarity index for logarithmic values of meiobenthos taxa density in the recognized habitats of the Snake Island MPA (Black Sea).
Fig. 7 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)
Fig. 7. Cluster analysis dendrogram based on meiobenthos density on the different habitats in MPA of the Snake Island (Black Sea).
Fig. 4 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)
Fig. 4. The average density (N, means ± SE ind.·m–2) and biomass (B, means ± SE mg·m–2) of the total meiobenthos with contribution permanent and temporary taxa in the different habitats of the Snake Island MPA (Black Sea).
Fig. 3 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)
Fig. 3. Meiobenthic community structure of different substrate types in the MB143 habitat of the Snake Island MPA (Black Sea).
Fig. 2 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)
Fig. 2. The average density (N, means ± SE ind.·m–2) and biomass (B, means ± SE mg·m–2) of the total meiobenthos of different substrate types in the MB143 habitat of the Snake Island MPA (Black Sea).
Fig. 1 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)
Fig. 1. The map-scheme of the study area near the Snake Island (north-western Ukrainian shelf of the Black Sea).
Fig. 6 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)
Fig. 6. The contribution (%) of each meiobenthic taxon to the average density and biomass in the different habitats of the Snake Island MPA (Black Sea).
Determining non-significant bits on a C++ implementation of the LeNet-5 convolutional neural network to be used for storing error correcting codes to protect weights and biases. Robustness assessment of the network after integrating the proposed codes.
<p>The architecture of the LeNet-5 convolutional neural network (CNN) was defined by LeCun in its paper "Gradient-based learning applied to document recognition" (<a href="https://ieeexplore.ieee.org/document/726791">https://ieeexplore.ieee.org/document/726791</a>) to classify images of hand written digits (MNIST dataset).</p><p>This architecture has been customized to use Rectified Linear Unit (ReLU) as activation functions instead of Sigmoid.</p><p>It consists of the following layers:</p><ul><li><strong>conv1</strong>: Convolution 2D, 1 input channel (28x28), 3 output channels (28x28), kernel size 5, stride 1, padding 2.</li><li><strong>relu1</strong>: Rectified Linear Unit (3@28x28).</li><li><strong>max1</strong>: Subsampling buy max pooling (3@14x14).</li><li><strong>conv2</strong>: Convolution 2D, 3 input channels (14x14), 6 output channels (14x14), kernel size 5, stride 1, padding 2.</li><li><strong>relu2</strong>: Rectified Linear Unit (6@14x14).</li><li><strong>max2</strong>: Subsampling buy max pooling (6@7x7).</li><li><strong>fc1</strong>: Fully connected (294, 147)</li><li><strong>fc2</strong>: Fully connected (147, 10)</li></ul><p>The fault hypotheses for this work include the occurrence of:</p><ul><li><strong>S0</strong>/<strong>S1</strong>: multiple adjacent stuck-at-0 and stuck-at-1 faults to determine the least significant bits of weights and biases that could be used to store the proposed error correcting codes.</li><li><strong>BF</strong>: single, double, and triple bit-flip faults to assess the robustness of the considered CNN</li></ul><p>In the memory cells containing all the parameters of the CNN: </p><ul><li><strong>w</strong>: weights (float32)</li><li><strong>b</strong>: biases (float32)</li></ul><p>All the images (10000) from the MNIST dataset have been used as workload.</p><p>The weights and biases of the LeNet-5 architecture have been protected using six different error correcting codes that have been deployed in the least significant bits of these elements.</p><p>The parity check matrices (H = P I) that define these ECCs are:</p><ul><li><strong>SEC(32, 26)</strong> (Hamming) under a <i>classic policy </i>(see methodology below):</li></ul><p><i> 11010010001000011101101000 100000</i></p><p><i> 10101001000100011011010100 010000</i></p><p><i> 01100100100010010110110010 001000</i></p><p><i> 00011100010001001110001101 000100</i></p><p><i> 00000011110000100001111011 000010</i></p><p><i> 00000000001111100000000111 000001</i></p><ul><li><strong>SEC(23, 18)</strong> (Hamming) under a <i>conservative policy</i> (see methodology below):</li></ul><p><i> 111100001111000000 10000</i></p><p><i> 110011101000111000 01000</i></p><p><i> 101011010100100110 00100</i></p><p><i> 010110110010010101 00010</i></p><p><i> 001101110001001011 00001</i></p><ul><li><strong>SEC(13, 9)</strong> (Hamming) under an <i>aggressive policy </i>(see methodology below):</li></ul><p><i> 110111000 1000</i></p><p><i> 101100110 0100</i></p><p><i> 011010101 0010</i></p><p><i> 111001011 0001</i></p><ul><li><strong>DEC(32, 21)</strong> (low redundancy and reduced overhead DEC) under a <i>classic policy </i>(see methodology below):</li></ul><p><i> 111000011001010010000 10000000000</i></p><p><i> 110110000011101000000 01000000000</i></p><p><i> 101011000110000010001 00100000000</i></p><p><i> 100101101000110001000 00010000000</i></p><p><i> 011010101100100000100 00001000000</i></p><p><i> 010101010100001001010 00000100000</i></p><p><i> 001100110010010100100 00000010000</i></p><p><i> 000011110001000110010 00000001000</i></p><p><i> 000000001111001101001 00000000100</i></p><p><i> 000000000000111100111 00000000010</i></p><p><i> 000000000000000011111 00000000001</i></p><ul><li><strong>DEC(28, 18)</strong> (low redundancy and reduced overhead DEC) under a <i>conservative policy </i>(see methodology below):</li></ul><p><i> 111111000000000000 1000000000</i></p><p><i> 110100111100000000 0100000000</i></p><p><i> 110000100011110000 0010000000</i></p><p><i> 001110010011001100 0001000000</i></p><p><i> 101100001010101010 0000100000</i></p><p><i> 010001001101010110 0000010000</i></p><p><i> 001011000101101001 0000001000</i></p><p><i> 101000011000110101 0000000100</i></p><p><i> 010001110000011011 0000000010</i></p><p><i> 000010100110000111 0000000001</i></p><ul><li><strong>DEC(17, 9)</strong> (low redundancy and reduced overhead DEC) under an <i>aggressive policy </i>(see methodology below):</li></ul><p><i> 111110000 10000000</i></p><p><i> 111001100 01000000</i></p><p><i> 110101010 00100000</i></p><p><i> 101010110 00010000</i></p><p><i> 101101001 00001000</i></p><p><i> 100110101 00000100</i></p><p><i> 100011011 00000010</i></p><p><i> 110000111 00000001</i></p><p>This dataset contains the raw data obtained from:</p><ul><li>running exhaustive fault injection campaigns for increasingly multiple stuck-at faults in the least significant bits of all weights and biases (simultaneously) and for all the images in the workload.</li><li>running statistical fault injection campaigns for single, double, and triple bit-flip faults, randomly targeting the considered locations and images in the workload.</li></ul><h3>Files information</h3><ul><li><i>no_ecc </i>folder: Results obtained for the original (not protected) version of the CNN.<ul><li><i>golden_run.csv</i>: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults.</li><li><i>sampling_SBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for single bit-flip faults.</li><li><i>sampling_DBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults.</li><li><i>sampling_TBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for triple bit-flip faults.</li><li><i>locating_sensitive_bits </i>folder: Prediction obtained for all the images considered in the workload in presence of stuck-at-0/stuck-at-1 faults that simultaneously target the N least significant bits of all weights and biases. There is one file for each parameter of type of fault and range of targeted bits. Files for bits in the range [11, 0] are not included as they obtain eactly the same results as the Golden Run (faults do not alter the behaviour of the network).</li></ul></li><li><i>sec/classic</i>, <i>sec/conservative</i>, and <i>sec/aggressive</i> folders: They contain the results obtained for the CNN protected by SEC(32, 26), SEC(23, 18), and SEC(13, 9), respectively.<ul><li><i>golden_run.csv</i>: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults. It must be noted that this file could be different that the golden_run.csv file for the original version of the CNN, as deploying the ECC in the weights and biases may have affected the behaviour of the network.</li><li><i>sampling_SBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for single bit-flip faults. They should all be tolerated by the definition of the ECC.</li><li><i>sampling_DBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults. They could be more harmful than for the unprotected version of the CNN, as the ECC may erroneously flip correct bits.</li></ul></li><li><i>dec/classic</i>, <i>dec/conservative</i>, and <i>dec/aggressive </i>folders: They contain the results obtained for the CNN protected by DEC(32, 21), DEC(28, 18), and DEC(17, 9), respectively.<ul><li><i>golden_run.csv</i>: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults. It must be noted that this file could be different that the golden_run.csv file for the original version of the CNN, as deploying the ECC in the weights and biases may have affected the behaviour of the network.</li><li><i>sampling_DBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults. They should all be tolerated by the definition of the ECC.</li><li><i>sampling_TBF_10000.csv</i>: Prediction obtained for running 10000 statistical fault injection experiments for triple bit-flip faults. They could be more harmful than for the unprotected version of the CNN, as the ECC may erroneously flip correct bits.</li></ul></li></ul><h3>Methodology information</h3><p>First, the CNN was used to classify all the images of the workload in the absence of faults to get a reference to determine the impact of faults. This is <i>golden_run.csv</i> file.</p><p>To locate non-significant bits in weights and biases, fault injection experiments were executed targeting all elements of all parameters of the CNN using the following procedure:</p><ul><li>The initial mask targeted only the least significant bit</li><li>Until the mask targets all bits of the elements (32 bits as they are single-precision floating point values):<ul><li>Affect the bits (setting them to 0 or 1 in case of stuck-at-0 or stuck-at-1 faults) identified by the mask for all elements of all parameters.</li><li>Classify all the images of the workload in the presence of this fault. The obtained output was stored in a given .csv file.</li><li>Remove the fault from the CNN by restoring the affected bits to its previous value.</li><li>Add the next adjacent bit to the mask, so it targets an additional least significant bit.</li></ul></li></ul><p>The analysis of the obtained results may help in determining which bits can be used to store an ECC:</p><ul><li>which bits never affect the behaviour of the CNN, as the predicted classification is exactly the same than in the absence of faults.</li><li>which bits midly affect the behaviour of the CNN, as although the predicted classifications differ from those in the absence of faults, the accuracy of the network is barely affected.</li><li>which bits greatly affect the behaviour of the CNN, as the accuracy of the network is significantly affected.</li></ul><p>Accordingly, three different policies have been identified for deploying an ECC using these bits:</p><ul><li><strong>Classic policy</strong>: The ECC protects as much bits as possible.</li><li><strong>Conservative policy</strong>: The ECC protects all those bits that may affect the prediction of the network.</li><li><strong>Aggressive policy</strong>: The ECC protects only those bits that significantly affect the accuracy of the network.</li></ul><p>After designing and deploying a single ECC and a double ECC for each of the identified policies, fault injection experiments were executed to verify their behaviour in the presence of faults.</p><p>Single and double ECCs were tested against single and double bit-flip, respectively (all faults should be tolerated,) and double and triple bit-flips, respectively (a correct bit could be erroneously flipped.)</p><p>Due to the heavy computational load of the decoders, statistical injection was used to run the required fault injection campaigns with a sample size (number of experiments) of 10000.</p><p>Each experiment consisted in:</p><ul><li>Randomly selecting the image to process, and the parameter, element, and bits (mask) to be targeted by the fault.</li><li>Affecting the bits (inverting them) identified by the mask.</li><li>Classifying the selected image of the workload in the presence of this fault. The obtained output was stored in a given .csv file.</li><li>Removing the fault from the CNN by restoring the affected bits to its previous value.</li></ul><h3>List of variables (Name : Description (Possible values))</h3><ul><li><strong>IMGID</strong>: Integer number identifying the considered image (1-9999).</li><li><strong>TENSORID</strong>: Integer number identiying the parameter affected by the fault (0 - No fault, 1 - conv1.w, 2 - conv1.b, 3 - conv2.w, 4 - conv2.b, 5 - fc1.w, 6 - fc1.b, 7 - fc2.w, 8 - fc2.b).</li><li><strong>ELEMID</strong>: Integer number identiying the element of the parameter affected by the fault (-1 - No fault, [0-2] - conv1.b, [0-74] - conv1.w, [0-5] - conv2.b, [0-149] - conv2.w, [0-146] - fc1.b, [0-43217] - fc1.w, [0-9] - fc2.b, [0-1469] - fc2.w).</li><li><strong>MASK</strong>: 8-digit hexadecimal number identifying those bits affected by the fault ([00000000 - No fault, FFFFFFFF - all 32 bits faulty]).</li><li><strong>FAULT</strong>: String identiying the type of fault (NF - No fault, BF - bit-flip, S0 - Stuck-at-0, S1 - Stuck-at-1).</li><li><strong>SOFTMAX</strong>: 10 decimal numbers obtained after applying the softmax function to the provided output. They represent the probability of the image of belonging to the corresponding category for classification.</li><li><strong>PRED</strong>: Integer number representing the category predicted for the processed image.</li><li><strong>LABEL</strong>: integer number representing the actual category for the processed image.</li></ul>
Mixed population trends inside a California protected area: Evidence from long-term community science monitoring
<p><span>Protected areas are one of the most widespread and accepted conservation interventions, yet their population trends are rarely compared to regional trends to gain insight into their effectiveness. Here, we leverage two long-term community science datasets to demonstrate mixed effects of protected areas on long-term bird population trends. We analyzed 31 years of bird transect data recorded by community volunteers across all major habitats of Stanford University's Jasper Ridge Biological Preserve to determine the population trends for a sample of 66 species. We found that nearly a third of species experienced long-term declines, and on average, all species declined by 12%. Further, we averaged species trends by conservation status and key life history attributes to identify correlates and possible drivers of these trends. Observed increases in some cavity-nesters and declines of scrub-associated species suggest that long-term fire suppression may be a key driver, reshaping bird communities through changes in forest and chaparral structure and composition. Additionally, we compared our results to those of the North American Breeding Bird Survey's Central California Coast region (n = 55 species) to place Jasper Ridge in a broader context. Most species experienced similar directional population trends inside vs. outside of the preserve, and only eight species (14.5%) did better inside this small, protected area. Therefore, we must identify relevant management strategies for declining populations and explicitly consider how existing protected areas target and manage each species. Further, this analysis underscores the importance of local and national community science for revealing nuanced long-term bird population trends.</span></p>
Fig. 2 in Fish assemblage of the Mamanguape Environmental Protection Area, NE Brazil: abundance, composition and microhabitat availability along the mangrove-reef gradient
Fig. 2. Ontogenetic patterns of habitat use in Abudefduf saxatilis, Anisotremus surinamensis, Lutjanus alexandrei, and L. jocu along the sub-areas of Mamanguape Mangrove-Reef system, NE Brazil, showing an increase in individual size classes from the Estuarine to the Reef zone. Mann Whitney U Test showed significant size differences between all sub-areas (for A. saxatilis, Transition vs. Reefs: U = 491, Z = -6.02, p = 0.00; for A. surinamensis, Transition vs. Reefs: U = 1338, Z = -6.83, p = 0.00; for L. alexandrei, Peixe-Boi vs. Transition: U = 0.00, Z = -3.39, p = 0.00; and Tanques vs. Transition: U = 0.00, Z = -2.92, p = 0.00; for L. jocu, Peixe-Boi vs. Transition: U = 7.5, Z = -3.38, p = 0.00), except between Tanques and Peixe-Boi for L. alexandrei (U = 65, Z = 0.76, p = 0.46).
Fig. 3 in Fish assemblage of the Mamanguape Environmental Protection Area, NE Brazil: abundance, composition and microhabitat availability along the mangrove-reef gradient
Fig. 3. Canonical Correspondence Analysis of fishes and environmental parameters from Mamanguape Mangrove-Reef system, NE Brazil: (a) fish species (symbols) in relation to microhabitat categories (vectors) - Eigenvalues: axis 1, 0.56; axis 2, 0,20; r species-environment: axis 1, 0.87; axis 2, 0.56; First two axes accounted for 64.9 % of the variance; (b) fish trophic groups and subareas (symbols) in relation to environmental categories (vectors) - Eigenvalues: axis 1, 0.49; axis 2, 0.39; r species-environment: axis 1, 0.79; axis 2, 0.76; First two axes accounted for 51.6 % of the variance. Monte-Carlo test of all canonical axes were significant (p <0.01), 999 permutations. Abbreviations as follows - fish species: Abusax: Abudefduf saxatilis; Acabah: Acanthurus bahianus; Acacoe: A. coeruleus; Achlin: Achirus lineatus; Anisur: Anisotremus surinamensis; Anivir: A. virginicus; Batsop: Bathygobius soporator; Centrop: Centropomus sp.; Cithspil - Citharichthys spilopterus; Corglau - Coryphopterus glaucofraenum; Dactvol - Dactylopterus volitans; Echnau: Echeneis naucrates; Epiadc: Epinephelus adscensionis; Eucmel: Eucinostomus melanopterus; Haepar: Haemulon parra; Hipprei: Hippocampus reidi; Lutana: Lutjanus analis; Lutale: L. alexandrei; Lutjoc: L. jocu; Micrbra: Microphis brachyurus; Myroce: Myrichthys ocellatus; Rypran: Rypticus randalli; Scarus: Scarus sp.; Sparis: Sparisoma sp.; Sphtes: Sphoeroides testudineus; Stefus: Stegastes fuscus; Stevar: S. variabilis; trophic groups: RH - Roving herbivore; TH - Territorial herbivore; OM - Omnivore; CA - Carnivore; IM - Invertivore of mobile prey.
Fig. 1 in Fish assemblage of the Mamanguape Environmental Protection Area, NE Brazil: abundance, composition and microhabitat availability along the mangrove-reef gradient
Fig. 1. Mamanguape estuary, State of Paraíba, NE Brazil, showing surveyed sub-areas: 1) Tanques; 2) Peixe-Boi; 3) Cação; 4) Transition; and 5) Reefs. Dashed areas represent sandbanks.
Raw data for publication: Bioinspired Living Coating System for Wood Protection: Exploring Fungal Species on Wood Surfaces Coated with Biofinish during its Service Life
<p>Weather Data.xlsx</p> <p>This file contains hourly local weather conditions in Izola, Slovenia from October 2021- August 2022</p> <p>Number of colonies.xlsx</p> <p>This file contains the number of fungal colonies isolated from the InnoRenew CoE facade</p> <p>FUNGAL STRAINS_DNA sequence analysis.xlsx</p> <p>This file contains the Genomic DNA of the fungal strains detected on the InnoRenew CoE facade</p>
Data from: Sleep and subjective age: Protect your sleep if you want to feel young
<p>The current studies examined the impact of insufficient sleep and sleepiness on the subjective experience of age. Study 1, a cross-sectional study of 429 participants (282 females (66%), 144 males, 3 other; age range 18-70) showed that for each additional day of insufficient sleep in the last 30 days, subjective age increased by 0.23 years. Study 2, an experimental crossover sleep restriction study (N = 186; 102 females (55%); 84 males; age range 18-46) showed that two nights of sleep restriction (4h in bed/night) made people feel 4.44 years older compared to sleep saturation (9h in bed/night). Additionally, moving from feeling extremely alert (KSS score of 1) to feeling extremely sleepy (KSS score of 9) was associated with feeling 10 years older in both studies. These findings provide compelling support for insufficient sleep and sleepiness to exert a substantial influence on how old we feel, and that safeguarding sleep is likely a key factor in feeling young.<strong> </strong></p>
Figure 12 in Increasing awareness for soil biodiversity and protection
Figure 12. Hands-on element: A smelling post with olfactory samples of various defense secretions of soil organisms.
Fig. 2–11 in Scarabaeoidea (Insecta: Coleoptera) Of The Kaliningrad Region (Russia): The Commented Actual Checklist, Assessment Of Rarity And Notes To Regional Protection
Fig. 2–11. Sampling and observation locations in the Kaliningrad Region: 2 –Ceruchus chrysomelinus (red points), Sinodendron cylindricum (blue points), and Lucanus cervus (green point); 3 – Dorcus parallelepipedus (green points), Platycerus caprea (red points), and P. caraboides (yellow points); 4 – Trox sabulosus (green points), and Trox scaber (red points); 5 – Geotrupes spiniger (green points), Geotrupes stercorarius (blue points), and Trypocopris vernalis (red points); 6 – Aphodius brevis (yellow point), A. borealis (green point), A. coenosus (red point), and A. fasciatus (blue points); 7 – Aphodius conspurcatus (red points), A. melanostictus (green points), Aegialia sabuleti (blue point), and Copris lunaris (yellow point); 8 – Aphodius varians (blue points), A. porcus (yellow points); A. distinctus (red points), and A. subterraneus (green points); 9 – Rhyssemus puncticollis (blue points), Psammodius asper (green points), and Oxyomus sylvestris (red points); 10 – Onthophagus coenobita (blue point), O. taurus (red points), O. gibbulus (yellow point), and O. nuchicornis (green points); 11 – Maladera holosericea (green points), and Omaloplia nigromarginata (red points).
Fig. 46–51 in Scarabaeoidea (Insecta: Coleoptera) Of The Kaliningrad Region (Russia): The Commented Actual Checklist, Assessment Of Rarity And Notes To Regional Protection
Fig. 46–51. The images of the living regional Scarabaeoidea specimens in nature: 46 – A. dubia, habitually coloured form (05 July 2011); 47 – Phyllopertha horticola (11 June 2018); 48 – Hoplia graminicola (11 June 2018); 49 – Protaetia marmorata (28 May 2010); 50 – P. metallica (07 July 2011); 51 – Cetonia aurata (20 June 2010).
Fig. 28–33 in Scarabaeoidea (Insecta: Coleoptera) Of The Kaliningrad Region (Russia): The Commented Actual Checklist, Assessment Of Rarity And Notes To Regional Protection
Fig. 28–33. The images of the living regional Scarabaeoidea specimens in nature: 28 – Aphodius rufipes (11 September 2018); 29 – A. prodromus (01 Oktober 2018); 30 – A. porcus (10 September 2018); 31 – A. sordidus (17 September 2018); 32 – A. foetens (10 September 2018); 33 – A. conspurcatus (15 Oktober 2018).
Fig. 52–57 in Scarabaeoidea (Insecta: Coleoptera) Of The Kaliningrad Region (Russia): The Commented Actual Checklist, Assessment Of Rarity And Notes To Regional Protection
Fig. 52–57. The images of the living regional Scarabaeoidea specimens in nature: 52 – Oxythyrea funesta (15 June 2010); 53 – Gnorimus nobilis (08 July 2011); 54 – Osmoderma barnabita, male (09 July 2018); 55 and 56 – Trichius fasciatus, colour variations (14 July 2010); 57 – Valgus hemipterus, female (11 May 2018).
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.