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Text-fig. 2. Metacheiromys marshii, AMNH 131777, drawing of basicranium in ventral view with isosurface from CT scans of left petrosal inserted (compare with Simpson 1931: fig. 7). Much of the mastoid exposure on the specimen's left side is damaged. Numbers 1 to 4 indicate depressions that based on the right side include a thin layer of entotympanic; 1 to 3 are between petrosal and basioccipital and 4 is petrosal only. The white arrow in the lower left passes through a canal between the petrosal and exoccipital for the auricular branch of the vagus nerve. Abbreviations: abX – grooves and foramina for auricular branch of vagus nerve, as – alisphenoid, astp – alisphenoid tympanic process, bo – basioccipital, bs – basisphenoid, eam – roof of external acoustic meatus, ec – ectotympanic, en – entotympanic, eo – exoccipital, es – epitympanic sinus of squamosal, fm – foramen magnum, fo – foramen ovale, gf – glenoid fossa, hf – hypoglossal foramen, ips – foramen for inferior petrosal sinus, ljf – lateral jugular foramen, me – mastoid exposure of petrosal, mjf – medial jugular foramen, mt – muscular tubercle, mtc – musculotubal canal, oc – occipital condyle, pa – porus acousticus (hidden), pas – parasphenoid, pgp – postglenoid process, pr – promontorium of petrosal, ps – presphenoid, smf – stylomastoid foramen, sof – superior orbital fissure, sq – squamosal, tca – tympanic canaliculus, th – tympanohyal, tm – tubular external acoustic meatus. in Skeletal Anatomy Of The Basicranium And Auditory Region In The Metacheiromyid Palaeanodont Metacheiromys (Mammalia, Pholidotamorpha) Based On High-Resolution Ct Scans
Text-fig. 2. Metacheiromys marshii, AMNH 131777, drawing of basicranium in ventral view with isosurface from CT scans of left petrosal inserted (compare with Simpson 1931: fig. 7). Much of the mastoid exposure on the specimen's left side is damaged. Numbers 1 to 4 indicate depressions that based on the right side include a thin layer of entotympanic; 1 to 3 are between petrosal and basioccipital and 4 is petrosal only. The white arrow in the lower left passes through a canal between the petrosal and exoccipital for the auricular branch of the vagus nerve. Abbreviations: abX – grooves and foramina for auricular branch of vagus nerve, as – alisphenoid, astp – alisphenoid tympanic process, bo – basioccipital, bs – basisphenoid, eam – roof of external acoustic meatus, ec – ectotympanic, en – entotympanic, eo – exoccipital, es – epitympanic sinus of squamosal, fm – foramen magnum, fo – foramen ovale, gf – glenoid fossa, hf – hypoglossal foramen, ips – foramen for inferior petrosal sinus, ljf – lateral jugular foramen, me – mastoid exposure of petrosal, mjf – medial jugular foramen, mt – muscular tubercle, mtc – musculotubal canal, oc – occipital condyle, pa – porus acousticus (hidden), pas – parasphenoid, pgp – postglenoid process, pr – promontorium of petrosal, ps – presphenoid, smf – stylomastoid foramen, sof – superior orbital fissure, sq – squamosal, tca – tympanic canaliculus, th – tympanohyal, tm – tubular external acoustic meatus.
Text-fig. 5. Metacheiromys marshi, AMNH 131777, left petrosal isosurface from CT scans in tympanic view; the posttympanic process of the squamosal and some possible entotympanic are also included. a – isosurface; b – line drawing with labels. Numbers 1 to 4 indicate depressions on medial flange. Abbreviations: ams – anteromedial septum, aptt – anteroventral process of tegmen tympani, cct – canal for chorda tympani nerve, ci – crista interfenestralis, cof – cochlear fossula, cp – crista parotica, ctp – caudal tympanic process, en? – possible entotympanic, epc – epitympanic crest, ew – epitympanic wing, fc – facial canal, fv – fenestra in Skeletal Anatomy Of The Basicranium And Auditory Region In The Metacheiromyid Palaeanodont Metacheiromys (Mammalia, Pholidotamorpha) Based On High-Resolution Ct Scans
Text-fig. 5. Metacheiromys marshi, AMNH 131777, left petrosal isosurface from CT scans in tympanic view; the posttympanic process of the squamosal and some possible entotympanic are also included. a – isosurface; b – line drawing with labels. Numbers 1 to 4 indicate depressions on medial flange. Abbreviations: ams – anteromedial septum, aptt – anteroventral process of tegmen tympani, cct – canal for chorda tympani nerve, ci – crista interfenestralis, cof – cochlear fossula, cp – crista parotica, ctp – caudal tympanic process, en? – possible entotympanic, epc – epitympanic crest, ew – epitympanic wing, fc – facial canal, fv – fenestra
Text-fig. 4. Metacheiromys marshii, AMNH 131777, basicranial isosurface from CT scans in oblique posteroventral view: left petrosal in blue and small piece of left entotympanic in red. The white arrow on the left petrosal passes through a canal traversed by the tympanic nerve. Abbreviations: abX – foramen for auricular branch of vagus nerve, aptt – anteroventral process of tegmen tympani, as – alisphenoid, bo – basioccipital, bs – basisphenoid, ctp – caudal tympanic process, eam – squamosal roof of external acoustic meatus, ec – ectotympanic, en – entotympanic, eo – exoccipital, hf – hypoglossal foramen, ips – foramen for inferior petrosal sinus, ljf – lateral jugular foramen, me – mastoid exposure of petrosal, mjf – medial jugular foramen, mt – muscular tubercle, oc – occipital condyle, pcf – posterior carotid foramen, pp – paroccipital process of petrosal, pr – promontorium of petrosal, ptp – posttympanic process of squamosal, smf – stylomastoid foramen, sq – squamosal, stf – stapedial artery foramen, tca – tympanic canaliculus, th – tympanohyal. in Skeletal Anatomy Of The Basicranium And Auditory Region In The Metacheiromyid Palaeanodont Metacheiromys (Mammalia, Pholidotamorpha) Based On High-Resolution Ct Scans
Text-fig. 4. Metacheiromys marshii, AMNH 131777, basicranial isosurface from CT scans in oblique posteroventral view: left petrosal in blue and small piece of left entotympanic in red. The white arrow on the left petrosal passes through a canal traversed by the tympanic nerve. Abbreviations: abX – foramen for auricular branch of vagus nerve, aptt – anteroventral process of tegmen tympani, as – alisphenoid, bo – basioccipital, bs – basisphenoid, ctp – caudal tympanic process, eam – squamosal roof of external acoustic meatus, ec – ectotympanic, en – entotympanic, eo – exoccipital, hf – hypoglossal foramen, ips – foramen for inferior petrosal sinus, ljf – lateral jugular foramen, me – mastoid exposure of petrosal, mjf – medial jugular foramen, mt – muscular tubercle, oc – occipital condyle, pcf – posterior carotid foramen, pp – paroccipital process of petrosal, pr – promontorium of petrosal, ptp – posttympanic process of squamosal, smf – stylomastoid foramen, sq – squamosal, stf – stapedial artery foramen, tca – tympanic canaliculus, th – tympanohyal.
Fig. 4. Geometric parameters for the numerical optimization process. a in Scientific Note Proposal of a Sluice-type Fish Pass
Fig. 4. Geometric parameters for the numerical optimization process. a) water level in the pool, b) intermediate wall height c) level difference between the pools, d) opening under the sluice, e) the dimension of the pool and f) position of the wall (see Fig. 3 for legend).
Figure 8 in The giraffe (Giraffa camelopardalis) cervical vertebral column: a heuristic example in understanding evolutionary processes?
Figure 8. These graphs compare the measured individual cervical vertebrae lengths of fossil Giraffids (Table 4), compared with extant giraffes and the 'other ungulate' group used in this study. The measurement of total vertebral column lengths (TVLs) for the fossil giraffids were generated from the regressions derived for extant giraffes or 'other ungulates', whereas the lengths of the individual cervical vertebrae were taken from the literature (see Table 4). Note that the specimens for Giraffa sp., Samotherium, and Paleotragus germaini appear to scale in a manner similar to extant giraffes, whereas those of Paleotragus primaevus, Climacoceras, and Canthumeryx appear to fall within the range of ungulates that do not demonstrate cervical elongation.
Figure 6 in The giraffe (Giraffa camelopardalis) cervical vertebral column: a heuristic example in understanding evolutionary processes?
Figure 6. Graphs of total vertebral column length plotted against the body lengths of C2–C7 vertebrae of all of the extant specimens studied. Other ungulates represent all species studied except the giraffe, camel, and llama. Note that for all specimens of the giraffe the vertebral lengths are longer than one would predict on the basis of a generalized ungulate regression, and scale more steeply than the ungulates. The dotted line on the ungulate plot is an extension of the ungulate regression that allows us to establish a comparison with the camel.
Figure 9 in The giraffe (Giraffa camelopardalis) cervical vertebral column: a heuristic example in understanding evolutionary processes?
Figure 9. These graphs compare the measured individual cervical vertebrae lengths of fossil Giraffids (Table 4) with extant giraffes and the 'other ungulate' group used in this study. The measurement of normalized vertebral column lengths for the fossil giraffids were generated from the regressions derived for extant giraffes or 'other ungulates', whereas the lengths of the individual cervical vertebrae were taken from the literature (see Table 4). Note that the specimens for Giraffa sp., Samotherium, and Paleotragus germaini appear to scale in a manner similar to extant giraffes, whereas those of Paleotragus primaevus, Climacoceras, and Canthumeryx appear to fall within the range of ungulates that do not demonstrate cervical elongation.
Figure 5 in The giraffe (Giraffa camelopardalis) cervical vertebral column: a heuristic example in understanding evolutionary processes?
Figure 5. Graph of total cervical vertebral length (TCL) vs. individual vertebral length of all the extant specimens studied. Note the way in which the giraffe cervical vertebrae scale in accordance with those seen in the other extant ungulates studied, with the only exception being the youngest giraffe (which was excluded from the regression analysis, but was placed on the graph for comparison).
Figure 2 in The giraffe (Giraffa camelopardalis) cervical vertebral column: a heuristic example in understanding evolutionary processes?
Figure 2. Photographs of the lateral aspect of non-articulated giraffe vertebrae C6, C7, T1, and T2, demonstrating the osteological differences between cervical and thoracic vertebrae. Note the size of the transverse foramen in C7, the lack of a transverse foramina in T1 and T2, and the longer spinous process of T1 compared with C6 and C7.
Figure 4 in The giraffe (Giraffa camelopardalis) cervical vertebral column: a heuristic example in understanding evolutionary processes?
Figure 4. Upper panel: the percentage contribution of the remaining vertebral regions to the vertebral column length minus that of the cervical of giraffes aged from calf to adult (ages are estimates). Lower panel: the percentage contributions of the remaining vertebral regions to the vertebral column length minus that of the cervical of the extant ungulates studied, in comparison with the adult giraffes. The percentage occupied by the various spinal regions in the giraffe falls into the same ranges observed in other ungulates when the cervical vertebrae are not included. Key: l, lumbar; s, sacral; t, thoracic.
Figure 3 in The giraffe (Giraffa camelopardalis) cervical vertebral column: a heuristic example in understanding evolutionary processes?
Figure 3. Upper panel: the percentage contribution of the vertebral regions to the entire length of the vertebral column of giraffes aged from calf to adult (ages are estimates). In the calf, the cervical vertebrae occupy approximately 45% of the total vertebral length. As the animal matures, this increases to between 52 and 54%. Lower panel: the percentage contribution of the vertebral regions to the entire length of the vertebral column of the extant ungulates studied, compared with the adult giraffe. Note that only in the giraffes do the cervical vertebrae occupy more than half of the entire vertebral column. Key: c, cervical; l, lumbar; s, sacral; t, thoracic.
Figure 1 in The giraffe (Giraffa camelopardalis) cervical vertebral column: a heuristic example in understanding evolutionary processes?
Figure 1. Photograph of the left aspect of giraffe vertebrae C6–T2, demonstrating how they are articulated in a living individual, and the differences between cervical and thoracic vertebrae. Note the size of the transverse foramen in C7 and the longer spinous process of T1 compared with C6 and C7.
Data from: Fractal triads efficiently sample ecological diversity and processes across spatial scales
<p>The relative influence of ecological assembly processes, such as environmental filtering, competition, and dispersal, vary across spatial scales. Changes in phylogenetic and taxonomic diversity across environments provide insight into these processes, however, it is challenging to assess the effect of spatial scale on these metrics. Here, we outline a nested sampling design that fractally spaces sampling locations to concentrate statistical power across spatial scales in a study area. We test this design in northeast Utah, at a study site with distinct vegetation types (including sagebrush steppe and mixed conifer forest), that vary across environmental gradients. We demonstrate the power of this design to detect changes in community phylogenetic diversity across environmental gradients and assess the spatial scale at which the sampling design captures the most variation in empirical data. We find clear evidence of broad-scale changes in multiple features of phylogenetic and taxonomic diversity across aspect. At finer scales, we find additional variation in phylodiversity, highlighting the power of our fractal sampling design to efficiently detect patterns across multiple spatial scales. Thus, our fractal sampling design and analysis effectively identify important environmental gradients and spatial scales that drive community phylogenetic structure. We discuss the insights this gives us into the ecological assembly processes that differentiate plant communities found in northeast Utah.</p>
Binary Classification as a Phase Separation Process (data repository)
<p><strong>For version 0.0.2 (from 2021) see below:</strong></p> <p>This is a data repository for the paper "Binary classification as a phase separation process", by Rafael Monteiro.</p> <ul> <li>Website with description of this project: <a href="https://rafael-a-monteiro-math.github.io/Binary_classification_phase_separation/index.html">https://rafael-a-monteiro-math.github.io/Binary_classification_phase_separation/index.html</a></li> <li>Github: <a href="https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation">https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation</a></li> </ul> <p>This is a second version, which I wrote using tensorflow. It is much smaller (5 Gb when decompressed), a remarkable improvement when compared to the more than 100 Gb of the previous version).</p> <p>The new files are </p> <ul> <li>PSBC_BCs.tar.gz</li> <li>PSBC_classifier_PCA.tar.gz</li> <li>PSBC_dataset.tar.gz</li> <li>PSBC_libs_grids_statistics.tar.gz</li> <li> PSBC_notebooks.tar.gz</li> </ul> <p>Their content is explained in the file README_v2.pdf</p> <p><strong>UPDATE: <a href="https://drive.google.com/drive/folders/18l_92HuHDWJDkZnvXRuyGedcyC_3YZ2M?usp=sharing">a Google Colab folder is also available</a>. You can also find all the data and libraries there, unpacked.</strong></p> <p>For usage, see the Git-hub. </p> <blockquote> <p><strong>NOTE)</strong> I will keep the content for the previous version available in my Github as well. It is still a "nice exercise" to do all that is done in this new version in numpy, as done there. <strong><em>(Or, I should say, they should be studied as a cautionary tale of what to avoid.)</em></strong></p> </blockquote> <p> </p> <p><strong>For version 0.0.1 (from 2020) see below:</strong></p> <p>This is a data repository for the paper "Binary classification as a phase separation process", by Rafael Monteiro.</p> <ul> <li>Website with description of this project: <a href="https://rafael-a-monteiro-math.github.io/Binary_classification_phase_separation/index.html">https://rafael-a-monteiro-math.github.io/Binary_classification_phase_separation/index.html</a></li> <li>Github: <a href="https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation">https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation</a></li> </ul> <p>Therein you will find</p> <ul> <li>Examples</li> <li>1D toy model examples</li> <li>Computational statistics</li> <li>Several trained PSBC on MNIST dataset, with different parameter configurations</li> <li>Extra simulations, investigating normalization properties, low dimensional models that fail due to "too much" model compression, and comparison among ANNs, KNNs, and the PSBC in 1D</li> </ul> <p>If you want to know</p> <ol> <li>how to read the data</li> <li>how to access computational statistics, raw data, and examples</li> <li>how to use the data stored in this data repository</li> </ol> <p>see the guide README.pdf on GitHub page at <a href="https://github.com/rafael-a-monteiro-math/Binary_Classification_Phase_Separation">Binary_Classification_Phase_Separation</a>, where a script that downloads (and organizes) all this data is also available ("download_PSBC.sh).</p> <p>I did not include a copy of the train-test set (0-1dubset of the MNIST database) in every folder with simulations. But you can find a copy of the normalized dataset in the tar ball "PSBC_Examples.tar.gz" as</p> <p>data_test_normalized_MNIST.csv and data_train_normalized_MNIST.csv.</p> <p> </p>
HYPERCOG_Process data_Welding log_2020-07-30
<p>The HyperCOG project addresses the full digital transformation of process industry through an innovative Industrial Cyber-Physical System and Data Analytics. It is based on advanced technologies that enable the development of a hyperconnected network of digital nodes. The nodes can catch outstanding streams of data in real-time, which together with the high computing capabilities, provide sensing, knowledge and cognitive reasoning, making companies robust in the face of variant scenarios. The breaking-edge system proposed in this work is validated on productivity, environmental and replicability aspects on three use cases of three di_erent sectors: steel, cement and chemical.</p> <p>Participating entities: LORTEK. The data was gathered and used in the proof of concept of the architecture introduced in the paper in the way that is described in it.</p> <p><strong>Dataset 1: Welding process data (xlsx files)</strong></p> <p>Real time data of the process of a welding cell on Excel sheets. The data is obtained at a frequency of 100 Hz and variables of voltage, current, temperature, gas flux, etc. are registered in the Excel file by rows.</p> <p><strong>Dataset 2: Temperature and movement of the piece constructed (zip files)</strong></p> <p>Data of temperatures obtained by thermocouple sensors and distortion of the structure measured by a laser sensor. The zip files contain coma separated values of 8 thermocouples welded to the substrate of the piece constructed by the welding cell. The reading of a laser sensor is also recorded along the x coordinate of the movement of the robot for synchronization purposes.</p> <p>The article corresponding to these datasets are available in open access in </p> <pre><a href="https://doi.org/10.5281/zenodo.5533904">https://doi.org/10.5281/zenodo.5533904</a></pre>
Interindividual differences influence multisensory processing during spatial navigation
<p>The dataset contains the raw data from two experiments. We investigate the ability of using auditory, visual or audiovisual landmarks for spatial navigation through the environment.</p>
Data for: Analysis of Conformational Exchange Processes using Methyl-TROSY-Based Hahn Echo Measurements of Quadruple-Quantum Relaxation
<p>Raw experimental data used in associated publication. A full list of experiments is provided in the README.md file.</p>
Datasets and OpenLCA foreground data processes for the article: Understanding environmental trade-offs and resource demand of direct air capture technologies through comparative life-cycle assessment
<p>This data set contains the supplementary data sets (1-3) and exported foreground data processes from OpenLCA for the manuscript “Understanding environmental trade-offs and resource demand of direct air capture technologies through comparative life-cycle assessment”, submitted to Nature Energy.</p> <p>This repository contains:</p> <ul> <li>Supplementary data set 1: Ancillary calculations and numerical values for HT-Aq DAC</li> <li>Supplementary data set 2: Ancillary calculations and numerical values for TSA DAC</li> <li>Supplementary data set 3: Ancillary calculations and numerical values shown in plots and table 3</li> <li>Foreground data from OpenLCA. OpenLCA process model for different cases of HT-Aq DAC and TSA DAC. To re-run the LCA calculations, OpenLCA (freeware) and the Ecoinvent 3.5 database (license required) need to be installed on a standard desktop computer or laptop with at least 8 GB RAM.</li> </ul>
Data from: Cryogenic land surface processes shape vegetation biomass patterns in northern European tundra
<p>Tundra ecosystems have experienced changes in vegetation composition, distribution, and productivity over the past century due to climate warming. However, the increase in above-ground biomass (AGB) may be constrained by cryogenic land surface processes (LSP) that cause topsoil disturbance and variable microsite conditions. These effects have remained unaccounted for in tundra biomass models, although they can impact multiple opposing feedbacks between the biosphere and atmosphere, ecosystem functioning and biodiversity. Here, by using field-quantified data from northern Europe, remote sensing, and machine learning, we show that LSP substantially constrain AGB in tundra. The three surveyed LSP (cryoturbation, solifluction and nivation) collectively reduced AGB by an average of 123.0 g m<sup>-2 </sup>(-30.0%). This effect was significant over landscape positions and was especially pronounced in snowbed environments, where the mean reduction in AGB was 57.3%. Our results imply that LSP are pivotal in shaping future patterns of tundra biomass, as long as cryogenic ground activity is retained by climate warming.</p> <p>These are the key data and codes related from Aalto et al., (2021).</p> <p> </p>
Escherichia coli DNA replication study: processed alignment data
<p>Genomes are replicated by large protein complexes called replisomes. In bacterial DNA replication, two replisomes replicate the DNA starting from the same origin site and proceeding in opposite directions. Understanding their movement in vivo has been challenging. We used quantitative genome sequencing to characterize the dynamics of bacterial replisomes at 5 different temperatures (17, 22, 27, 32 and 37 °C) in exponential growth (3 replicates) or in stationary phase (one experiment at 17, 27 and 37 °C).</p> <p>The data deposited here give the coordinates of the sequence reads (deposited under the BioProject PRJNA772106) covering the Escherichia coli str. K-12 substr. MG1655 complete genome (accession number U00096.3).</p> <p>The file archive contains data files for each sample, at nucleotide resolution and binned in intervals of 10,000 base pairs. It also contains a C program to perform the binning and a README summarising how the alignment was done. <em>Please note that once uncompressed, the data will take 5 Gb of disks space in total.</em></p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.