Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
14,867
datasets available to search
ShareScore release 0.9.0
Dataset results
14,867 results for “determination”
Figure 20. A in Functional morphology of neck musculature in the Tyrannosauridae (Dinosauria, Theropoda) as determined via a hierarchical inferential approach
Figure 20. A, origin scar of m. splenius capitis from C2 of Tyrannosaurus rex (BHI 3033), in anterodorsal view. B, area of possible insertions of m. splenius capitis (medial part) on the occiput of Daspletosaurus torosus (CMN 8506; the specimen is incomplete and the image partly mirrored), with moment arms shown for dorsiflexion and lateroflexion. C, insertions of m. longus colli dorsalis/m. transversospinalis cervicis onto posterior and dorsal surfaces of epipophyses, from C2 to C5. The most prominent insertion is a posteriorly concave scar on the C2 epipophysis. D, centres of rotation (white circles) and moment arms (lines) for insertions of m. longus colli dorsalis/m. transversospinalis cervicis, on cervical vertebrae of Tyrannosaurus rex (BHI 3033). Centres of rotation are estimated to be at positions similar to those Selbie, Thomson & Richmond (1993) determined for intervertebral flexion in cats.
Figure 21. A in Functional morphology of neck musculature in the Tyrannosauridae (Dinosauria, Theropoda) as determined via a hierarchical inferential approach
Figure 21. A, origin scars of m. longissimus capitis superficialis (C7–D1 parapophyses) and m. longissimus capitis profundus (C6–C3 parapophyses) of Tyrannosaurus rex (AMNH 5027). B, short moment arm of m. longissimus capitis superficialis for neck plus head dorsiflexion. C, paroccipital process insertions and moment arms of m. longissimus capitis superficialis on Daspletosaurus torosus (CMN 8506). D, basioccipital insertions and moment arms of m. longissimus capitis profundus on Daspletosaurus torosus (CMN 8506; the specimen is incomplete and the images are partly mirrored).
Figure 16 in Functional morphology of neck musculature in the Tyrannosauridae (Dinosauria, Theropoda) as determined via a hierarchical inferential approach
Figure 16. Schematic diagram of major neck muscles of Tyrannosaurus rex in dorsal view. A–C represent successively deeper layers, and other conventions are as in Figure 15. M. complexus inserts dorsally on the squamosal, and m. iliocostalis capitis inserts along the ventral edge of the paroccipital process. M. longissimus capitis superficialis inserts between these on the lateral edge of the paroccipital process. In C a probable lateral part of m. spinalis capitis is signified by a '?'.
Figure 11. A in Functional morphology of neck musculature in the Tyrannosauridae (Dinosauria, Theropoda) as determined via a hierarchical inferential approach
Figure 11. A, origins (dark shapes) and insertions (lighter outlined shapes) of mm. intertransversarii aponeuroses on posterior cervical vertebrae of Struthio camelus. Origins are from anterior faces of lateral tubercles, and insertions are onto posterior projections of the lateral tubercles. Arrows represent lines of action whereby insertions are drawn towards the origins to effect intervertebral lateroflexion. B, origins (dark) and insertions (lighter) of mm. inclusii on posterior cervical vertebrae of Struthio camelus. Origins are from anterior faces of the costal processes, and insertions are onto the lateral and dorsolateral tubercles. Arrows represent lines of action whereby insertions are drawn towards the origins. C, origins (dark-filled shapes) and insertions (light-filled shapes) of lateral portions of mm. intertransversarii in Caiman crocodylus. Arrows represent lines of action from origin to insertion, by which the muscles would lateroflex the anterior vertebra of each pair relative to the posterior one.
Figure 9. A in Functional morphology of neck musculature in the Tyrannosauridae (Dinosauria, Theropoda) as determined via a hierarchical inferential approach
Figure 9. A, insertions of (dark grey) m. longus colli dorsalis pars cranialis onto anterior cervical epipophyses of Haliaeetus leucocephalus. B, schematic origin (light grey) of m. longus colli dorsalis pars cranialis, from cervicodorsal region of Struthio camelus. C, schematic origins (dark grey) of m. longus colli dorsalis pars cranialis from epipophyses (processes dorsales) of posterior cervicals in Struthio camelus. Slips from these origins coalasce with the main belly of the muscle, as demarcated by the light-shaded lines. The main belly of the muscle continues dorsally. D, schematic origin and insertions of m. longus colli dorsalis pars caudalis. This muscle subsystem originates from the cervicodorsal region and sends multiple bellies to insert on posterior cervical epipophyses. E, origins of m. longissimus cervicis/m interarticulares of Caiman crocodylus. The gradient-filled slips represent association of origins with the fascia surrounding the muscles. F, anteriormost insertions (dark grey) of m. longissimus cervicis/m. interarticulares, and m. transversospinalis cervicis, of Caiman crocodylus.
Figure 10. A in Functional morphology of neck musculature in the Tyrannosauridae (Dinosauria, Theropoda) as determined via a hierarchical inferential approach
Figure 10. A, several muscles of Pelicanus occidentalis, disseted in lateral view. Abbreviations are as in the main text. Several slips of m. rectus capitis dorsalis (m. r.c.d.) converge ventrally, towards tendinous insertions on the basioccipital tuberosities. M. complexus has an unusual lateral, tendinous insertion. B, posterolateral view of m. longissimus capitis superficialis (outlined in dark grey) of Alligator mississippiensis. The posterior origin and the insertion are tendinous. M. iliocostallis capitis (m. il. cap.) is depicted, and m. constrictor colli has not been dissected away. The neck is slightly dorsiflexed in this view.
Figure 12. A in Functional morphology of neck musculature in the Tyrannosauridae (Dinosauria, Theropoda) as determined via a hierarchical inferential approach
Figure 12. A, origins of m. rectus capitis lateralis (m. r.c.l.) and rectus capitis ventralis (m. r.c.v.) of Corvus brachyrhynchos, from C2 prosessus ventralis. B, all origins of m. r.c.v. from anterior cervicals of another specimen of Corvus brachyrhynchos, and its m. rectus capitis lateralis origin from C2. Both images are ventrolateral views. C, ventrolateral view of m. rectus capitis ventralis (with light outline) of Falco columbarius.
Figure 8. A in Functional morphology of neck musculature in the Tyrannosauridae (Dinosauria, Theropoda) as determined via a hierarchical inferential approach
Figure 8. A, lateral view of m. longus colli dorsalis pars cranialis (m. l.c.d. cranialis), m. longus colli dorsalis pars caudalis (m. l.c.d. caudalis) and mm. intertransversarii (mm. intertrans.) of Cygnus columbianus. Note posterior slips contributing to m. l.c.d. cranialis, ventrally inserting heads of m. l.c.d. caudalis and multiple tendinous divisions of all mm. intertrans. B, left m. transversospinalis cervicis (m. trans. cerv., outlined in grey) of Caiman crocodylus, inserts by a white tendon onto C1. M. spinocapitis posticus (m. sp. cap. post.) and m. longissimus capitis superficialis (m. long. cap. sup., with part of surrounding fascia left on) are also outlined in grey. Superficial muscles have been removed.
Figure 7. A in Functional morphology of neck musculature in the Tyrannosauridae (Dinosauria, Theropoda) as determined via a hierarchical inferential approach
Figure 7. A, dorsal view of m. biventer cervicis (m. biv. c., dark outline) and m. longus colli dorsalis pars cranialis of Aquila chrysaetos. Note the tendon intervening between anterior and posterior bellies of m. biv. c. M. complexus has been removed. B, dorsal view of m. transversospinalis capitis (m. trans. cap., dark outline) of Alligator mississippiensis. Medial and lateral portions of m. trans. cap. in Alligator are not distinguished here. C, lateral view of m. trans. cap. of Alligator mississippiensis (dark outline). D, m. complexus (dark outline) and m. rectus capitis lateralis (light outline) of Pelicanus occidentalis in lateral view. E, m. complexus of Pelicanus occidentalis, outlined in dark grey on the right. This is a dorsal view, with anterior towards the top. F, dorsal view of dissected Alligator mississippiensis, with m. epistrpheo-capitis lateralis, m. spinocapitis posticus and m. transversospinalis cervicis outlined in grey. M. transversospinalis capitis has been removed. G, m. splenius capitis of Anas platyrhynchos, from C2 to the occiput, outlined in grey, in posterior view. M. complexus and m. biventer cervicis have been removed. H, m. epistropheo-capitis medialis/m. altoïdius capitis of Caiman crocodylus, outlined in grey. M. transversospinalis capitis and m. epistropheo-capitis lateralis have been removed.
Figure 5 in Functional morphology of neck musculature in the Tyrannosauridae (Dinosauria, Theropoda) as determined via a hierarchical inferential approach
Figure 5. Attachments of muscles inserting on the occiput of birds. A, posterior cervical vertebrae of Strutio camelus, showing osteological origin of posterior belly of m. biventer cervicis. B, anterior cervical vertebrae of Haliaeetus leucocephalus, with origins of m. complexus, m. splenius capitis and m. rectus capitis dorsalis (outlined). M. complexus originates from the epipophyses dorsally (as in Haliaeetus leucocephalus), and sometimes the lateral tubercles ventrally. For adjoining origins of m. complexus and m. rectus capitis dorsalis from the lateral tubercles, the latter is the anterior of each pair. C, occiput of Struthio camelus, depicting all insertions. In Struthio camelus and many other birds m. splenius capitis lateralis inserts laterally onto the occiput, but in other birds part of m. complexus inserts here.
Figure 4 in Functional morphology of neck musculature in the Tyrannosauridae (Dinosauria, Theropoda) as determined via a hierarchical inferential approach
Figure 4. Comparison of major sites of muscle attachment on the necks of tyrannosaurids versus crocodilians and birds. A, cervical series of Caiman crocodylus (above; C1–C9) and Tyrannosaurus rex (below; C1–C10), depicting similarities of posterior transverse process morpohology. B, cervical series of Asio flamaeus (above) and Tyrannosaurus rex (below), depicting morphological similarities of epipophysis and the C2 neural spine. In all three groups the anterior transverse processes are smaller than their posterior counterparts (to show this clearly the C2 cerivical rib of T. rex is not pictured). Cervical rib morphology differs markedly among these archosaurs. The specimens are scaled to similar lengths from C1 to C9. The Tyrannosaurus rex is a composite reconstruction of C1 from Osborn (1905), BHI 3033 (C2) and AMNH 5027 (the remaining bones).
Figure 3 in Functional morphology of neck musculature in the Tyrannosauridae (Dinosauria, Theropoda) as determined via a hierarchical inferential approach
Figure 3. Flow chart for extant behavioural interpolation, which enables inference of muscle-modulated behaviours in extinct animals. The certainty of behavioural inference in an extinct taxon is inversely related to the behaviour's specificity. Behaviour can be inferred by phylogenetic bracketing if similarity of muscle function is established in extant clades by kinematic and physiological considerations, and if these functions correlate with similar behaviours in the extant groups.
Figure 19. A in Functional morphology of neck musculature in the Tyrannosauridae (Dinosauria, Theropoda) as determined via a hierarchical inferential approach
Figure 19. A, origins of m. transversospinalis capitis (C2–C9), m. complexus (C2–C5) and m. splenius capitis from C2 and possibly C3, of Tyrannosaurus rex (BHI 3033). B, rugose scarring of m. transversospinalis capitis insertion on parietals of Tyrannosaurus rex (AMNH 5029). C, insertion of m. transversospinalis capitis onto parietals of Daspletosaurus torosus (CMN 8506; the specimen is incomplete and the image partly mirrored), with moment arms for lateral and dorsiflexion. D, insertions and moment arms for m. complexus (two dorsal) and m. iliocostalis capitis (ventral) on occiput of Daspletosaurus torosus (CMN 8506; the specimen is incomplete and the image partly mirrored for clarity).
Figure 2. A in Functional morphology of neck musculature in the Tyrannosauridae (Dinosauria, Theropoda) as determined via a hierarchical inferential approach
Figure 2. A, inference of muscle function for turning the head in extant animals. Axes represent levels of corroboration of muscle topological and kinematic morphology, physiology and observed behaviour. The volume subtended by these levels of certainty is the behavioural inference space for the muscle. The muscles m. transversospinalis capitis lateralis and m. complexus, homologous and present in crocodilians and birds, respectively, are depicted as examples with different levels of corroboration along the axes and more or less certain inference of function. Physiological activity is shown as confirmed by electromyography in birds, but only hypothesized in crocodilians. B, inferring the function of m. complexus for turning the head in tyrannosaurids. Axes represent levels of inference (Witmer, 1995) for morphology and physiology, and level of inference for kinematic action of the muscle based on its reconstructed morphology (see text for explanation). Because physiological muscle function during movements is corroborated in only one pole of the extant bracket, and the physiology does not leave osteological correlates, a Level II′ inference is the best possible for the extinct taxon.
Figure 1 in Functional morphology of neck musculature in the Tyrannosauridae (Dinosauria, Theropoda) as determined via a hierarchical inferential approach
Figure 1. Divisions of amniote neck and craniocervical musculature, superimposed on the cervical vertebrae of Caiman crocodylus. Different shades represent transversospinalis, longissimus, iliocostalis and longus/medial iliocostalis divisions.
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>
Data for Altered Glia-Neuron Communication in Alzheimer's Disease Affects WNT, p53, and NFkB Signaling Determined by snRNA-seq
<p><strong>data.tar.gz contains all files from the data directory associated with the 230313_TS_CCCinHumanAD GitHub project and includes the following:</strong></p><ul><li><strong>CellRangerCounts/</strong><ul><li><strong>GSE157827/</strong><ul><li><strong>post_soupX/ : </strong>contains 21 directories for 21 samples, which each contain 3 files obtained from ambient RNA removal with soupX. Below is a representative example, but this repo contains 1 directory per sample:<ul><li><strong>SAMN16100290_S01_AD/</strong><ul><li><strong>barcodes.tsv</strong></li><li><strong>genes.tsv</strong></li><li><strong>matrix.mtx</strong></li></ul></li></ul></li><li><strong>pre_soupX/ : </strong>contains 21 directories for 21 samples, which each contain 2 files obtained from Cell Ranger after aligning fastq files to the reference genome. Below is a representative example, but this repo contains 1 directory per sample:<ul><li><strong>SAMN16100290_S01_AD/</strong><ul><li><strong>filtered_feature_ bc_matrix.h5</strong></li><li><strong>Raw_feature_bc_matrix.h5</strong></li></ul></li></ul></li></ul></li><li><strong>GSE174367/ : </strong>contains 19 directories for 19 samples, which contain 3 files each from Cell Ranger alignment of fastq files to the reference genome. Below is a representative example, but this repo contains 1 directory per sample:<ul><li><strong>SAMN19128610_S1_CTRL/</strong><ul><li><strong>barcodes.tsv</strong></li><li><strong>genes.tsv</strong></li><li><strong>Matrix.mtx</strong></li></ul></li></ul></li></ul></li><li><strong>ccc/</strong><ul><li><strong>nichenet_grn/</strong><ul><li><strong>gr_network_human_21122021.rds : </strong>accessed in October 2023, gene regulation network – gene regulatory information from MultiNicheNet</li><li><strong>ligand_tf_matrix_nsga2r_final.rds: </strong>accessed in October 2023, ligand tf matrix for signaling path determination from MultiNicheNet</li><li><strong>signaling_network_human_21122021.rds : </strong>accessed in October 2023, signaling network – protein-protein interaction information from MultiNicheNet</li><li><strong>weighted_networks_nsga2r_final.rds : </strong>accessed in October 2023, networks weighted by literature evidence from MultiNicheNet</li></ul></li><li><strong>nichenet_prior/</strong><ul><li><strong>ligand_target_matrix.rds : </strong>accessed in April 2023, ligand to target matrix from NicheNet</li><li><strong>lr_network.rds : </strong>accessed in April 2023, ligand-receptor matrix from NicheNet</li></ul></li><li><strong>nichenet_v2_prior/</strong><ul><li><strong>ligand_target_matrix_nsga2r_final.rds : </strong>accessed in June 2023, ligand to target matrix from MultiNicheNet used to predict target genes.</li><li><strong>lr_network_human_21122021.rds : </strong>accessed in June 2023, ligand-receptor matrix from MultiNicheNet used to predict ligand-receptor pairs.</li></ul></li><li><strong>geo_multinichenet_output.rds </strong>: MultiNicheNet output for Morabito et al., 2021 data</li><li><strong>geo_signaling_igraph_objects.rds </strong>: list of igraph objects for 17 overlapping LRTs and their signaling mediators in the Morabito et al., 2021 dataset. </li><li><strong>gse_multinichenet_output.rds</strong> : MultiNicheNet output for Lau et al., 2020 data</li><li><strong>gse_signaling_igraph_objects.rds</strong> : list of igraph objects for 17 overlapping LRTs and their signaling mediators in the Lau et al., 2020 dataset </li></ul></li><li><strong>seurat_preprocessing/</strong><ul><li><strong>geo_filtered_seurat.rds : </strong>merged and filtered seurat object of Morabito et al., 2021 data</li><li><strong>geo_integrated_seurat.rds :</strong> seurat object integrated using harmony of Morabito et al., 2021 data</li><li><strong>geo_clustered_seurat.rds : </strong>clustered seurat object of Morabito et al., 2021 data</li><li><strong>geo_processed_seurat.rds : </strong>processed seurat object with final cell type assignments at specified resolution of Morabito et al., 2021 data</li><li><strong>gse_filtered_seurat.rds : </strong>merged and filtered seurat object of Lau et al., 2020 data</li><li><strong>gse_integrated_seurat.rds : </strong>seurat object integrated using harmony of Lau et al., 2020 data</li><li><strong>gse_clustered_seurat.rds :</strong> clustered seurat object of Lau et al., 2020 data</li><li><strong>gse_processed_seurat.rds : </strong>processed seurat object with final cell type assignments at specified resolution of Lau et al., 2020 data </li></ul></li></ul>
Abb. 13 in Die Arten der Gattung Dahlica ENDERLEIN 1912 in Oberösterreich: Determination, Verbreitung, Vergesellschaftung (Lepidoptera, Psychidae)
Abb. 13: Genetische Abstände verschiedener Populationen von Dahlica sauteri als Taxon-Tree (siehe Text).
Abb. 10 in Die Arten der Gattung Dahlica ENDERLEIN 1912 in Oberösterreich: Determination, Verbreitung, Vergesellschaftung (Lepidoptera, Psychidae)
Abb. 10: Entschupptes Vorderbein eines Männchens von Siederia listerella (Niederösterreich, Drösing /March, 2.5.1987 am Licht 22:30, leg. J. Ortner, det. E. Hauser). Die Epiphyse ist durch den Pfeil gekennzeichnet. Abb. 11: Epiphyse aus Abb. 10 (in gleicher Lage, stärker vergrössert). Nach einem REM-Foto des Autors. Die Oberfläche weist eine schuppige Struktur auf. Abb. 12: Aderung der Discoidalzelle im Vorderflügel: (A) Dahlica und Siederia (mit Anhangzelle). (B) Eosolenobia (mit Anhangzelle und eingeschobener Zelle).
Abb. 5 in Die Arten der Gattung Dahlica ENDERLEIN 1912 in Oberösterreich: Determination, Verbreitung, Vergesellschaftung (Lepidoptera, Psychidae)
Abb. 5: Ein mit ausgestreckter Legeröhre lockendes Weibchen von D. sauteri (etwa Mitte) und parthenogenetische Weibchen von D. lichenella während der Eiablage. Fundort: Lindaumauer bei Gaflenz. Abb. 6: Fühlerglied eines Weibchens, leicht verändert nach SAUTER (1956). StB Stiftborsten mit Sockel; Sb = Sensilla basiconica, KB = kurze Borsten, Sch = Schuppe. Abb. 7: Ausschnitt aus dem Fühler eines Weibchens von D. lazuri aus der Schweiz (Via Mala-Schlucht, 1700m), mit geraden Stiftborsten auf Sockeln.
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.