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Fig. 2 in Plant Type Materials from Kanagawa Prefecture (Japan) in the Herbarium of the Komarov Botanical Institute (LE; Russia): Lycophytes, Ferns, Gymnosperms, and Angiosperms (Monocots and some Dicots)
Fig. 2. Syntype of Lycopodium cryptomerinum Maxim. (LE 01009939; KPM-NX0001852).
Fig. 1 in Plant Type Materials from Kanagawa Prefecture (Japan) in the Herbarium of the Komarov Botanical Institute (LE; Russia): Lycophytes, Ferns, Gymnosperms, and Angiosperms (Monocots and some Dicots)
Fig. 1. Lectotype of Lycopodium cryptomerinum Maxim. (LE 01009938; KPM-NX0001851).
Fig. 1 in Bionomics of Bactrocera fruit flies (Diptera: Tephritidae) in Khyber Pakhtunkhwa, Pakistan; exploring performance of various trap types and their characteristics
Fig. 1. Map of Khyber Pakhtunkhwa showing (a) agro-ecological zones and (b) sampling sites.
Fig. 36 in First illustrated description of the male of Diphya macrophthalma, the type species of the genus (Araneae, Tetragnathidae)
Fig. 36. Distribution of Diphya species known in Chile and Argentina.
Fig. 2. Typical Spirurina type X in Molecular characterization of the parasitic nematode genus Crassicauda; larvae parasitic on the firefly squid and adults on beaked whales off the coast of Japan
Fig. 2. Typical Spirurina type X larva. (A) whole body, (B) head, and (C) tail.
Figure 1 in The taxonomic catalog of the Brazilian fauna: Dermaptera and Phasmatodea (Insecta), with commentaries on species list, types, authorship and distribution
Figure 1. Number of species by biome: (A) Dermaptera; (B) Phasmatodea.
Supporting Data for "Extending GPU-Accelerated Gaussian Integrals in the TeraChem Software Package to f Type Orbitals: Implementation and Applications."
<p>Raw data and output files for "Extending GPU-Accelerated Gaussian Integrals in the TeraChem Software Package to f Type Orbitals: Implementation and Applications."</p>
Oregon-R-modENCODE(#25211) wild-type fruit fly (D.melanogaster) lifespan under standard maintenance conditions
<p><span>Aging is a phenomenon that manifests itself as permanent physiological deterioration, resulting in changes like reduction of immune function and fitness-related forms of behavior. Studies on changes in lifespan in response to various environmental alterations can bring knowledge of the mechanisms driving them. To properly conduct these studies, data on lifespan under the standard husbandry conditions are required.</span></p>
Supplementary data for: Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signalling in a Model of Niemann-Pick Disease Type C
<p>Supplementary example data for the work presented in "<em>Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signalling in a Model of Niemann-Pick Disease Type C</em>". </p> <p><strong>Abstract: </strong></p> <p>Simultaneously recording network activity and ultrastructural changes of the synapse is essential for advancing our understanding of the basis of neuronal functions. However, the rapid millisecond-scale fluctuations in neuronal activity and the subtle sub-diffraction resolution changes of synaptic morphology pose significant challenges to this endeavour. Here, we use specially designed graphene microelectrode arrays (G-MEAs), which are compatible with high spatial resolution imaging across various scales as well as permit high temporal resolution electrophysiological recordings to address these challenges. Furthermore, alongside G-MEAs, we have developed an easy-to-implement machine learning algorithm to efficiently process the large datasets collected from MEA recordings. We demonstrate that the combined use of G-MEAs, machine learning (ML) spike analysis, and four-dimensional (4D) structured illumination microscopy (SIM) enables monitoring the impact of disease progression on hippocampal neurons which have been treated with an intracellular cholesterol transport inhibitor mimicking Niemann-Pick disease type C (NPC), and show that synaptic boutons, compared to untreated controls, significantly increase in size, leading to a loss in neuronal signalling capacity.</p> <p> </p>
Chronic social defeat stress induces meningeal neutrophilia via type I interferon signaling: single cell RNA sequencing data
<p>Meningeal single cell RNA sequencing data</p> <p>Meningeal samples were collected from both dorsal and ventral skull, avoiding inclusion of choroid plexus. Samples were digested in 2.5 mg/mL Collagenase D (Cat. #11088858001; Roche) and 12.5 μL of 0.5 mg/mL DNAseI (Cat. #L5002139; Worthington), put on a shaker at 370C for 30 m, diluted with cold HBSS + 0.1% BSA, and mashed through a 70 μm cell strainer prior to sorting.</p> <p>Data represent live, nucleated, singlet cells (DAPI-DRAQ5+) sorted on a BD FACS Aria Fusion into HBSS + 10% FBS prior to droplet encapsulation using 10x Genomics’ Drop-seq platform (Chromium v2).</p> <p>10X chip lane is indicate by 'group' column</p> <p>Group 1 = 4 pooled homecage control (unstressed) mice</p> <p>Group 2 = 4 pooled homecage control (unstressed) mice</p> <p>Group 3 = 4 pooled mice exposed to chronic social defeat for 14 days; tissue was collected 2 hours following final defeat</p> <p>See the following repositories for data processing:</p> <p><a href="https://github.com/maryellenlynall/2019_bcell_stress/blob/master/bcellstress20.Rmd">https://github.com/maryellenlynall/2019_bcell_stress/</a> (processing from raw files starts at bcellstress020.Rmd)</p> <p><a href="https://github.com/staceykigar/meningeal_neut/">https://github.com/staceykigar/meningeal_neut/</a></p> <p>We also provide a processed dataset (processed.RData) with assays 'counts' and 'logcounts' which is the processed single cell object saved at line "# Save object for upload to Zenodo" in script <a href="https://github.com/staceykigar/meningeal_neut/">https://github.com/staceykigar/meningeal_neut/</a>neutrophilstress01.Rmd </p> <p>Cluster annotations are in sce$Annotation</p> <p>Neutrophil subcluster annotations are in sce$Subcluster</p> <p>Sample condition is in sce$cond, where "HC" indicates homecage control and "SD" indicates chronic social defeat</p> <p>10X chip lane is in sce$group</p>
Dataset from Osika and Jania (2024): Geomorphological and historical records of the surge-type behaviour of Hansbreen (Svalbard)
<h2>Geomorphological map of the terrestrial and submarine forefield of Hansbreen</h2> <p>This dataset contains shapefiles of geomorphological features in the terrestrial and submarine forefield of Hansbreen, a marine-terminating glacier in Hornsund (southern Spitsbergen, Svalbard), associated and described further in Osika and Jania (2024).</p> <p>Field investigation was conducted in 2021-2023. Mapping was performed in QGIS 3.22 using the WGS84/UTM33N spatial reference system and based on several datasets:</p> <ul> <li>a very high-resolution orthophotomap and DEM generated and published by Błaszczyk et al. (2022),</li> <li>bathymetric data generated and published by Błaszczyk et al. (2021) and from Kartverket.</li> </ul> <p>For detailed information about data sources used for mapping, see:</p> <p>Błaszczyk M and 12 others (2021) Factors controlling terminus position of Hansbreen, a tidewater glacier in Svalbard. J. Geophys. Res.: Earth Surf., 126(2), e2020JF005763 (doi: 10.1029/2020JF005763).</p> <p>Błaszczyk M, Laska M, Sivertsen A and Jawak SD (2022) Combined Use of Aerial Photogrammetry and Terrestrial Laser Scanning for Detecting Geomorphological Changes in Hornsund, Svalbard. Remote Sens., 14(3), 601. (doi: 10.3390/rs14030601).</p> <p> </p> <p><em>This work was funded by the National Science Centre of Poland (grant no. 2021/41/N/ST10/02070).</em></p> <p>Please cite the database alongside this resource: Osika A., Jania J., 2024: Geomorphological and historical records of the surge-type behaviour of Hansbreen (Svalbard). Annals of Glaciology 65, e31. doi:10.1017/aog.2024.32</p>
Figure 11. Sciurini right astragalus, JODA 15793 in The fauna and chronostratigraphy of the middle Miocene Mascall type area, John Day Basin, Oregon, USA
Figure 11. Sciurini right astragalus, JODA 15793. Scale bar =1mm.
Figure 12 in The fauna and chronostratigraphy of the middle Miocene Mascall type area, John Day Basin, Oregon, USA
Figure 12. Euroxenomys sp. left P4, JODA 4682. A. Occlusal view. B. Medial view. Scale bar=1mm.
Figure 9 in The fauna and chronostratigraphy of the middle Miocene Mascall type area, John Day Basin, Oregon, USA
Figure 9. Balantiomys oregonensis right dp4 in occlusal view, JODA 3767. Scale bar=1mm.
Figure 10. Protospermophilus malheurensis left M1 in The fauna and chronostratigraphy of the middle Miocene Mascall type area, John Day Basin, Oregon, USA
Figure 10. Protospermophilus malheurensis left M1 in occlusal view, JODA 6416. Scale bar=1mm.
Figure 13 in The fauna and chronostratigraphy of the middle Miocene Mascall type area, John Day Basin, Oregon, USA
Figure 13. cf. Kalobatippus sp. left dp2 in occlusal view, UCR 312849. Scale bar=3mm.
Figure 7 in The fauna and chronostratigraphy of the middle Miocene Mascall type area, John Day Basin, Oregon, USA
Figure 7. Pseudaelurus sp. Right astragalus in dorsal view, JODA 15306. Scale bar=1cm.
MD Simulation Files for Dual BACH1 regulation by complementary SCF-type E3 ligases
<p>MD Simulation input files and analysis files for the SKP1-FBXO22/BACH1<sup>BTB</sup> complex, SKP1-FBXO22 by itself, BACH1<sup>BTB</sup>, and the BACH1<sup>BTB</sup> F9A mutant.</p>
Artifact for "Verifying the Option Type with Rely-Guarantee Reasoning"
<p>This repository contains the dataset and scripts to reproduce Table 1 and Table 2 in the paper "Verifying the Option Type with Rely-Guarantee Reasoning".</p> <h2>Requirements</h2> <p>Please see <code>REQUIREMENTS.md</code> for the same information, but listed separately from this README file.</p> <p>Our scripts are designed for a Unix environment (Linux, macOS, WSL, etc.). They will not work under a Windows command shell.</p> <p>Our scripts require the following tools:</p> <ul> <li> <p><a href="https://www.python.org/downloads/">Python 3</a>, version 3.9.6 or later.</p> </li> <li> <p>GNU <code>grep</code>. For macOS, install it as <code>ggrep</code> via the command <code>brew install grep</code>.</p> <ul> <li>If not already installed, <code>brew</code> can be installed via the instructions<a href="https://docs.brew.sh/Installation">here</a></li> </ul> </li> </ul> <h2>Subject programs</h2> <p>File <code>dataset.txt</code> lists the subject programs.</p> <p>The subject programs are stored under the following directories:</p> <ul> <li><code>optional-paper-base-repos</code>: all the subject programs with their build files modified to run the Optional Checker.</li> <li><code>optional-paper-annotated-repos</code>: all the subject programs, with<code>@SuppressWarnings</code> for each true positive and false positive issued by the Optional Checker (scripts count these). It also contains the Optional Checker type qualifiers that we wrote.</li> <li><code>optional-paper-intellij-repos</code>: all the subject programs with <code>@SuppressWarnings</code> for each IntelliJ warning (so they can be programmatically counted).</li> <li><code>optional-paper-errorprone-repos</code>: all the subject programs with <code>@SuppressWarnings</code> for each Error Prone warning.</li> <li><code>optional-paper-spotbugs-repos</code>: all the subject programs with <code>@SuppressWarnings</code> for each SpotBugs warning.</li> </ul> <h2>Scripts</h2> <p>Looking to reproduce <em>all</em> the data in our tables? See <a href="reproduce-data"><code>reproduce-data</code></a>.</p> <p>This repository contains the following scripts:</p> <ul> <li><a href="reproduce-data"><code>reproduce-data</code></a>: this script reproduces all the data for our tables.</li> <li><a href="compute-precision-recall-annotations"><code>compute-precision-recall-annotations</code></a>: this script generates Table 1: it computes the values for precision, recall, and the number of human-written, machine-checked annotations used by each tool.</li> <li><a href="count-style-violations"><code>count-style-violations</code></a>: this script generates Table 2: it reports the number of style violations detected by each tool.</li> <li><a href="mygrep.py"><code>mygrep.py</code></a>: this is a Python utility used by other scripts as a thin wrapper around <code>grep</code>. You need not use it directly.</li> </ul> <p>All these scripts may be executed from the root of this directory. For example, to generate Table 1:</p> <pre><code>% ./compute-precision-recall-annotations </code></pre> <h2>Resulting Data</h2> <p>Each script produces the <em>rows</em> of the dataset, excluding the column titles. Below is a brief description of each output <code>.tex</code> file:</p> <ul> <li><code>eval-statistics.tex</code>: generated by the <code>compute-precision-recall-annotations</code> script, maps to Table 1 in our paper. The columns of the data are, in order from left-to-right: <ul> <li>Tool name</li> <li>Number of true positives detected by the tool</li> <li>Number of false positives detected by the tool</li> <li>Precision</li> <li>Recall</li> <li>Total number of machine-verified annotations written as a specification across all subject programs.</li> </ul> </li> <li><code>style-violations.tex</code>: generated by the <code>count-style-violations</code>script, maps to Table 2 in our paper. The columns are the data are, in order from left-to-right: <ul> <li>Tool name</li> <li>Number of violations of style rule 1</li> <li>Number of violations of style rule 3</li> <li>Number of violations of style rule 4</li> <li>Number of violations of style rule 5</li> <li>Number of violations of style rule 6.a</li> <li>Number of violations of style rule 6.b</li> <li>Number of violations of style rule 6.c</li> <li>Number of violations of style rule 7</li> </ul> </li> </ul> <h2>Implementation</h2> <p>The implementation of the Optional Checker appears in the <code>implementation</code> directory. This folder contains the following subdirectories:</p> <ul> <li><code>optional</code>: the source code for the Optional Checker's verification logic for the Optional type system. <ul> <li><code>optional/qualifiers</code>: the definitions for each type qualifier from the Optional type system that may be used with the Optional Checker.</li> </ul> </li> <li><code>nonempty</code>: the source code for the Optional Checker's verification logic for the Non-Empty type system. <ul> <li><code>nonempty/qualifiers</code>: the definitions for each type qualifier from the Non-Empty type system that may be used with the Optional Checker.</li> </ul> </li> </ul>
The double-peaked Type Ic supernova 2019cad: another SN 2005bf-like object
<p>SN 2019cad spectra and bolometric LC.</p> <p>Paper available at: https://academic.oup.com/mnras/article/504/4/4907/6224870</p>
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.