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Figure 10 in In search of the true Tydeus (Acari, Tydeidae)
Figure 10. Tydeus spathulatus. The five lanceolate terminal dorsal setae (A), dorsal setae of genu I (B) and IV (C), with a detail of the tip of the former (D). Scale bar: 10 Mm.
Figure 8 in In search of the true Tydeus (Acari, Tydeidae)
Figure 8. Tydeus croceus. Prodorsum with eyespots (A), terminal dorsal setae (B), striation of genital area (C), ornamentation of femur I (D), palp (E), stylet-like movable digit of chelicera (F), dorsal seta of genu III (G). Scale bar: 10 Mm.
Figure 5 in Ectosymbionts of the non-indigenous Asian shore crab, Hemigrapsus sanguineus (Decapoda: Varunidae), in the western north Atlantic, and a search for its parasites
Figure 5. (A) Dorsal view of Carcinus maenas male (56.4 mm CW; injury to carpus of right cheliped) with five barnacles Semibalanus balanoides located in grooves around the mesogastric region of the carapace. Small colonies of Conopeum tenuissimum are scattered on the carapace. Arrow points to the opening of a Sabellaria vulgaris tube in the right hepatic area. (B) Dorsal view of Panopeus herbstii female (23.5 mm CW; right cheliped and two left walking legs missing) with large Conopeum tenuissimum colony on right side spanning frontal, progastric, hepatic, and branchial areas; another colony on carpus of the left cheliped. One large colony of Alcyonidium albescens on left side (arrow).
Figure 3 in Ectosymbionts of the non-indigenous Asian shore crab, Hemigrapsus sanguineus (Decapoda: Varunidae), in the western north Atlantic, and a search for its parasites
Figure 3. (A) Frequency of Conopeum tenuissimum versus CW (1.0 mm classes) of Hemigrapsus sanguineus collected March and April 2006 at Townsends Inlet, New Jersey. Open bars, total crabs examined (n5281); shaded bars, crabs with bryozoans (n5156). (B) Number of colonies of Conopeum tenuissimum (n5638) on specimens of Hemigrapsus sanguineus (n5119; 1.0 mm CW classes) collected March 2006 at Townsends Inlet, New Jersey. Regression equation: y (no. colonies)527.518+0.681x (CW); r250.281, p 0.0001, n5119.
Figure 2 in Ectosymbionts of the non-indigenous Asian shore crab, Hemigrapsus sanguineus (Decapoda: Varunidae), in the western north Atlantic, and a search for its parasites
Figure 2. Ventral view of Hemigrapsus sanguineus female (26.1 mm CW; same crab as Figure 1B) with a gaping abdomen caused by the accumulation of young blue mussels Mytilus edulis attached to the pleopods (white arrow shows largest mussel, 9.5 mm long). Colonies of the calcareous bryozoan Conopeum tenuissimum present on many of the pereopods. Black arrow points to the polychaete Spirorbis sp.
Figure 4 in Ectosymbionts of the non-indigenous Asian shore crab, Hemigrapsus sanguineus (Decapoda: Varunidae), in the western north Atlantic, and a search for its parasites
Figure 4. Percentages of colonies of Conopeum tenuissimum at various locations on the carapace of Hemigrapsus sanguineus based on 336 colonies from 134 crabs (Table III) collected 29 March 2006 and 26 April 2006 at Townsends Inlet. Anterolaterally are hepatic areas, posterolaterally are branchial areas; numbers in parentheses are for colonies that span the hepatic-branchial regions. Medially from anterior to posterior are frontal, progastric, mesogastric, cardiac, and intestinal locations.
Figure 1 in Ectosymbionts of the non-indigenous Asian shore crab, Hemigrapsus sanguineus (Decapoda: Varunidae), in the western north Atlantic, and a search for its parasites
Figure 1. Dorsal views of Hemigrapsus sanguineus with colonies of Conopeum tenuissimum and other ectosymbionts, collected intertidally in New Jersey. (A) Female (27.4 mm CW; first left walking leg missing) with several colonies on the carapace and on the pereopods. Arrow shows a large colony of Alcyonidium albescens covering the left posterior quadrant of the carapace. (B) Female (26.1 mm CW; same crab with mussels under abdomen, Figure 2) showing Conopeum tenuissimum colony spanning the left hepatic-branchial region of the carapace, a small medial colony and a colony in the right hepatic region, plus numerous colonies on the pereopods. (C) Male (18.5 mm CW) with a single colony of Conopeum tenuissimum covering more than half of the carapace, with some smaller colonies on the pereopods. (D) Female (24.9 mm CW; left cheliped missing) with four colonies of Conopeum tenuissimum on the carapace, two in hepatic and two in branchial areas. Barnacle is Semibalanus balanoides.
Text-fig. 3. Phylogenetic relationship of Peignecyon felinoides n. gen. et n. sp., within some selected Amphicyonidae, and some extinct caniform carnivorans. Paramiacis exilis is the outgroup. Searches were performed by means of the Branch and Bound and a Bootstrap analysis through 1,000 replicates. One tree is obtained (length 73 steps, consistency index (CI) = 0.6301, retention index (RI) = 0.7000). The numbers below nodes are Bremer indices, and the numbers above nodes are Bootstrap support percentages (only shown ≥ 50). in A New Thaumastocyoninae (Amphicyonidae, Carnivora) From The Early Miocene Of Tuchořice, The Czech Republic
Text-fig. 3. Phylogenetic relationship of Peignecyon felinoides n. gen. et n. sp., within some selected Amphicyonidae, and some extinct caniform carnivorans. Paramiacis exilis is the outgroup. Searches were performed by means of the Branch and Bound and a Bootstrap analysis through 1,000 replicates. One tree is obtained (length 73 steps, consistency index (CI) = 0.6301, retention index (RI) = 0.7000). The numbers below nodes are Bremer indices, and the numbers above nodes are Bootstrap support percentages (only shown ≥ 50).
Figure 34. Interrelationships among basal actinopterygians including the new data from Stegotrachelus finlayi. A branch and bound search reveals a in Devonian actinopterygian phylogeny and evolution based on a redescription of Stegotrachelus finlayi
Figure 34. Interrelationships among basal actinopterygians including the new data from Stegotrachelus finlayi. A branch and bound search reveals a single most parsimonious tree (length = 145; consistency index = 0.57; retention index = 0.75; rescaled consistency index = 0.43). Values immediately below the nodes are Bremer decay indices. Values above Bremer decay indices represent bootstrap support values based on a heuristic search algorithm with 10 000 pseudoreplicates and ten addition-sequence replicates. Bootstrap values in excess of 40% are included. Letters at nodes are keyed to the apomorphy list provided in Appendix S3. Line drawings adapted from: Onychodus jandemarrai, Andrews et al. (2006), with body adapted from Strunius, Jessen (1966); Miguashaia bureaui, Cloutier (1996); Uranolophus wyomingensis, Long (1993); Osteolepis macrolepidotus, Jarvik (1948); Cheirolepis canadensis, Arratia & Cloutier (1996); Cheirolepis schultzei, Arratia & Cloutier (2004); Cheirolepis tralli, Pearson & Westoll (1979); Osorioichthys marginis, Taverne (1997); Donnrosenia schaefferi, Long et al. (2008); Tegeolepis clarki, Friedman & Blom (2006); Howqualepis rostridens, Long (1988); 'Mimia' toombsi, Gardiner (1984); Krasnoyarichthys jesseni, Prokofiev (2002); S. finlayi is original; Moythomasia dugaringa, Gardiner (1984) and Brian Choo (pers. comm., 2008); Moythomasia nitida, Jessen (1968); Limnomis delaneyi, Daeschler (2000); Cuneognathus gardineri and Kentuckia hlavini, Friedman & Blom (2006); Melanecta anneae and Woodichthys bearsdeni, Coates (1998); Wendyichthys dicksoni, Lund & Poplin (1997).
Silencing of hippocampal synaptic transmission impairs spatial reward search on a head-fixed tactile treadmill task
<p>Publication data</p> <p>This repository contains the raw data files for the following manuscript:</p> <p>Title: Silencing of hippocampal synaptic transmission impairs spatial reward search on a head-fixed tactile treadmill task<br> Authors: Jake T. Jordan and J. Tiago Gonçalves<br> Pre-print in bioRxiv. doi:10.1101/2021.09.03.458092 (2021)</p> <p>A summary of all experimental groups and data tables and included as two Excel (.xlsx) files: DREADDs_Cued.xlsl and DREADDs_Spatial.xlsl, these correspond to figures 2 and 3 of the publication, respectively.</p> <p>The raw data files were acquired as described in Jordan et al. (2021a) doi:10.1016/j.xpro.2021.100770 <br> Software code for data acquisition and interpretation is available at doi:10.5281/zenodo.5196612</p>
Raw and processed SPT data for "Guided nuclear exploration increases CTCF target search efficiency"
<p><strong>Raw and processed SPT data for “Guided nuclear exploration increases CTCF target search efficiency”</strong></p> <p>Anders S. Hansen<sup>1,2,3,4,*</sup>, Assaf Amitai<sup>5,*</sup>, Claudia Cattoglio<sup>1,2,3,4</sup>, Robert Tjian<sup>1,2,3,4</sup>, Xavier Darzacq<sup>1,2,3</sup></p> <p> </p> <p>1: Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, USA;</p> <p>2: Li Ka Shing Center for Biomedical and Health Sciences</p> <p>3: CIRM Center of Excellence, University of California, Berkeley, Berkeley, USA;</p> <p>4: Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, USA;</p> <p>5: Department of Chemical Engineering, MIT, Cambridge 02139, Massachusetts, USA</p> <p>*: ASH and AA contributed equally.</p> <p> </p> <p><strong>Overview</strong></p> <p>This repository contains all the raw and processed spaSPT data associated with “Guided nuclear exploration increases CTCF target search efficiency”. In total, this dataset contains data for 1,669 single-cell movies and trajectories with 85,612,029 unique displacements. In this ReadMe file we provide the following information:</p> <ul> <li>The cell lines used in this study.</li> <li>How the data was collected.</li> <li>How to read the raw single-cell SPT spaSPT data.</li> <li>How to read the processed, quality-controlled and HMM-classified data</li> </ul> <p>The document does not contain information about how the data was analyzed. For details on how the data was analyzed and for raw code to reproduce our figures, please go to <a href="https://gitlab.com/anders.sejr.hansen/anisotropy">https://gitlab.com/anders.sejr.hansen/anisotropy</a></p> <p> </p> <p><strong>Cell lines and transient transfection constructs</strong></p> <p>In total, 20 different cell lines were studied here. The mouse embryonic stem cells (mESCs) are in the JM8.N4 background (Pettitt et al., 2009) and the human cells were U2OS osteosarcoma cells. Cell lines expressing Halo-tagged proteins were either homozygous knock-in cell lines, wild-type cell lines transiently transfected with a plasmid over-expressing the protein of interest (Lipofectamine 3000), or wild-type cell lines stably over-expressing a transgene.</p> <p>The wild-type and C59 mESC cell lines were pathogen tested using the IMPACT II test (performed by IDEXX BioResearch) and where negative for all tested pathogens (full details provided in (Hansen et al., 2017)). The wild-type and C32 U2OS cell lines were tested for mycoplasma contamination and found to be clean and further authenticated by Short Tandem Repeat Profiling (STR profiling; 100% match with U2OS; full details provided in (Hansen et al., 2017)).</p> <p>Further details on each cell line or transfection construct are provided in the table in the ReadMe PDF (the MAT-file name is the name of the MAT-file containing the raw data).</p> <p><strong>Data collection using stroboscopic photo-activation single-particle tracking (spaSPT)</strong></p> <p>For full details on how the experiments where performed, please see the Materials and Methods section of the manuscript. Here we will briefly describe the protocol.</p> <p>To systematically investigate how proteins explore the nucleus we need a very large dataset (i.e. hundreds of thousands of trajectories) of very high quality SPT data (i.e. with minimal bias and without many tracking errors). The two major sources of bias in experimental SPT data are 1) motion-blurring and 2) tracking errors (Hansen et al., 2018). First, motion-blurring biases against detecting fast-moving molecules since while the camera is exposed, a fast-moving molecule will spread its photons over many pixels and no longer resemble a diffraction limited spot, whereas a bound or slow-moving molecule will emit all its photons as a single diffraction limited spot. Since most localization algorithms readily detect diffraction limited spots, but not motion-blurs this introduces a clear bias. spaSPT overcomes this bias by strobing the excitation laser (Elf et al., 2007): using just a single 1 ms excitation pulse, we simultaneously achieve high signal-to-background (~10-fold), while minimizing motion-blur as previously demonstrated (for a more complete discussion, please see (Hansen et al., 2018)). Second, tracking errors are largely caused by high particle densities: for example, when trajectories overlap, tracking errors occur which clearly prevents accurately analysis of protein diffusion and which could lead to artefactual conclusions. By using the bright photo-activatable Janelia Fluor dyes PA-JF<sub>549</sub> and PA-JF<sub>646</sub> (Grimm et al., 2016), we can overcome this challenge (this is also known as sptPALM (Manley et al., 2008)). During the camera integration time between frame (~447 microseconds), we pulse the 405 nm photo-activation laser at an intensity such that the mean localization density is around 1 molecule per nucleus per frame. At this density, tracking errors are greatly reduced, yet we are able to collect tens of thousands of frames at this density per cell and thus obtain large amounts of data despite imaging at a very low density. For a more complete discussion of spaSPT please see (Hansen et al., 2017, 2018).</p> <p>To generate a large dataset at multiple time-scales, we collected spaSPT data (generally around 8 cells per replicate and four biological replicates; occasionally a cell would be removed from subsequent analysis if the localization density was too high and thus prone to tracking errors) at 3 camera exposure times: 4 ms (add ~447 microseconds), 7 ms (add ~447 microseconds) and 13 ms (add ~447 microseconds); this roughly corresponds to frame-rates of ~223 Hz, 133 Hz, and 74 Hz. To generate data at longer lag times (this was only done after HMM-classification; see also main paper), this data was subsampled according to the table in the ReadMe PDF.</p> <p><strong>Overview of raw, unprocessed single-cell spaSPT data</strong></p> <p>This repository contains all the raw, unprocessed spaSPT data for each single cell in the directory “UnprocessedSingleCellData”. In total, this directory contains 1669 MAT-files corresponding to the 1669 single-movies recorded here. The files are systematically named for their parent cell line, replicate number and cell number. For example, “mESC_C59_Halo-mCTCF_133Hz_Rep4_Cell8.mat” contains SPT data for mouse embryonic stem cell line clone 59 and Halo-CTCF was imaged at ~133 Hz; the file is for cell number 8 in biological replicate number 4. All the other single-cell experiments are similarly named. Each MAT-file contain all the trajectories recording from a single-cell movie and stored as a “structure array” object, “trackedPar”. trackedPar contains three variables for each trajectory:</p> <ul> <li>trackedPar.xy: “xy” is a matrix with 2 columns and a number of rows corresponding to the number of frames where the molecule was located. The first column is the x-coordinate and the second column is the y-coordinate and the units are micrometers.</li> <li>trackedPar.Frame: “Frame” is a column vector where each element is an integer describing the frame number wherein the particle was localized (each trajectory contains at most 1 gap between frames).</li> <li>trackedPar.TimeStamp: “TimeStamp” is a column vector where each element is the timepoint (in units of seconds) where the molecule was localized.</li> </ul> <p>Note that this Matlab format is directly readable by Spot-On: <a href="https://spoton.berkeley.edu/">https://spoton.berkeley.edu/</a></p> <p> </p> <p><strong>Overview of HMM-classified and processed SPT data</strong></p> <p>The raw spaSPT data was processed (to remove any rare tracking errors) and all the data merged. The data was the classified into either a “bound” or “free” state using a 2-state Hidden-Markov Model (HHM; vbSPT (Persson et al., 2013)) and then temporally subsampled to generate data at longer frame rates. Full details on how the data was processed are given in the Materials and Methods section as well as on GitLab. Raw code to reproduce all the figures from unprocessed single-cell spaSPT data is also available at GitLab: <a href="https://gitlab.com/anders.sejr.hansen/anisotropy">https://gitlab.com/anders.sejr.hansen/anisotropy</a></p> <p>All the HMM-classified data for each cell line at each frame rate is stored in a single MAT-file. The MAT-file contains two key cell arrays: “CellTracks” and “CellTrackViterbiClass”. The two cell arrays contain the XY-coordinates and classification, respectively. More, specifically, suppose trajectory <em>k</em> is made up of <em>n</em> localizations. Then:</p> <p>CellTracks{k}: a matrix of <em>n</em> rows and 2 columns containing the x,y coordinates in units of micrometers.</p> <p>CellTrackViterbiClass{k}: a column vector of length <em>n</em>-1 where each entry is an integer: either “1” or “2”. Thus, the length of this vector is 1 less than the number of rows in the CellTracks matrix. This is because only the displacements and not the localizations are classified. For example, the CellTrackViterbiClass{k} reads [1;1;1;2;2] it means that there were 6 localizations and that the first 3 displacements (1à2, 2à3, 3à4) were classified as “bound” and the last 2 displacements (4à5, 5à6) were classified as “free”. </p> <p> </p> <p> </p> <p><strong>References</strong></p> <p>Elf, J., Li, G.-W., and Xie, X.S. (2007). Probing transcription factor dynamics at the single-molecule level in a living cell. Science <em>316</em>, 1191–1194.</p> <p>Grimm, J.B., English, B.P., Choi, H., Muthusamy, A.K., Mehl, B.P., Dong, P., Brown, T.A., Lippincott-Schwartz, J., Liu, Z., Lionnet, T., et al. (2016). Bright photoactivatable fluorophores for single-molecule imaging. Nat. Methods 66779.</p> <p>Hansen, A.S., Pustova, I., Cattoglio, C., Tjian, R., and Darzacq, X. (2017). CTCF and cohesin regulate chromatin loop stability with distinct dynamics. Elife <em>6</em>.</p> <p>Hansen, A.S., Woringer, M., Grimm, J.B., Lavis, L.D., Tjian, R., and Darzacq, X. (2018). Robust model-based analysis of single-particle tracking experiments with Spot-On. Elife <em>7</em>, e33125.</p> <p>Manley, S., Gillette, J.M., Patterson, G.H., Shroff, H., Hess, H.F., Betzig, E., and Lippincott-Schwartz, J. (2008). High-density mapping of single-molecule trajectories with photoactivated localization microscopy. Nat. Methods <em>5</em>, 155–157.</p> <p>Persson, F., Lindén, M., Unoson, C., and Elf, J. (2013). Extracting intracellular diffusive states and transition rates from single-molecule tracking data. Nat. Methods <em>10</em>, 265–269.</p> <p>Pettitt, S.J., Liang, Q., Rairdan, X.Y., Moran, J.L., Prosser, H.M., Beier, D.R., Lloyd, K.C., Bradley, A., and Skarnes, W.C. (2009). Agouti C57BL/6N embryonic stem cells for mouse genetic resources. Nat. Methods <em>6</em>, 493–495.</p> <p>Teves, S.S., An, L., Hansen, A.S., Xie, L., Darzacq, X., and Tjian, R. (2016). A dynamic mode of mitotic bookmarking by transcription factors. Elife <em>5</em>.</p> <p> </p> <p> </p> <p> </p>
Supplementary Text A1. Detailed search terms and steps in all databases
<p>Supplement to the article "Efficacy and safety of PD-1 inhibitors in recurrent or metastatic nasopharyngeal carcinoma patients after failure of platinum-containing regimens: a systematic review and meta-analysis".</p> <p><strong>Supplementary Text </strong><strong>A</strong><strong>1</strong><strong>.</strong><strong> Detailed search terms and steps in all databases</strong></p>
Code and data for: Emergence of spatially structured populations by area-concentrated search
<p>The idea that populations are spatially structured has become a very powerful concept in ecology, raising interest in many research areas. However, despite dispersal being a core component of the concept, it typically does not consider the movement behavior underlying any dispersal. Using individual-based simulations in continuous space, we investigate the emergence of a spatially structured population in landscapes with spatially heterogeneous resource distribution and with organisms following simple area-concentrated search (ACS); individuals do not, however, perceive or respond to any habitat attributes per se but only to their foraging success. We investigated effects of different resource clustering patterns in landscapes (single large cluster vs. many small clusters) and different resource densities on spatial structure of populations and movement between resource clusters of individuals. As the results, we found that foraging success increased with increasing resource density and decreasing number of resource clusters. In a wide parameter space, the system exhibited attributes of a spatially structured population with individuals concentrated in areas of high resource density, searching within areas of resources, and 'dispersing' in a straight line between resource patches. 'Emigration' was more likely from patches that were small or of low quality (low resource density), but we observed an interaction effect between these two parameters. With the ACS implemented, individuals tended to move deeper into a resource cluster in scenarios with moderate resource density than in scenarios with high resource density. 'Looping' from patches was more likely if patches were large and of high quality. Our simulations demonstrate that spatial structure in populations may emerge if critical resources are heterogeneously distributed and if individuals follow simple movement rules (such as ACS). Neither the perception of habitat nor an explicit decision to emigrate from a patch on the side of acting individuals is necessary for the emergence of spatial structure.</p>
Dataset for "Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark"
<p>Dataset containing the results shown in Fig. 6 of "Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark".</p>
The Breakthrough Listen Search for Intelligent Life: Observations of 1327 Nearby Stars Over 1.10–3.45GHz
<p>This dataset is in support of publication:<br> <em>"The Breakthrough Listen Search for Intelligent Life: Observations of 1327 Nearby Stars Over 1.10–3.45GHz", </em><br> D. C. Price, J. E. Enriquez et. al.<br> The Astronomical Journal, 159:86 (16pp), 2020<br> <a href="https://doi.org/10.3847/1538-3881/ab65f1">https://doi.org/10.3847/1538-3881/ab65f1</a></p> <p>This dataset (<em>sband2019.tar.gz</em>) consists of '.dat' file outputs from the <a href="https://ui.adsabs.harvard.edu/abs/2019ascl.soft06006E/abstract">turboSETI</a> narrowband dedoppler search code. The corresponding Filterbank files used as inputs (i.e. narrowband dynamic spectra) are available at <a href="http://seti.berkeley.edu/opendata">http://seti.berkeley.edu/opendata</a>. Further information about Breakthrough Listen data formats may be found in <a href="https://ui.adsabs.harvard.edu/abs/2019PASP..131l4505L/abstract">Lebofsky et. al. (2019</a>).</p> <p>The file <em>static.tar.gz</em> contains the database of events (HDF5 files) and pregenerated images from the <a href="https://github.com/UCBerkeleySETI/event_viewer">event_viewer</a> used to manually inspect candidates.</p>
Data and code for "Search Algorithm, Repetitive Information, and Sales on Online Platform"
<p>Data and code for "Search Algorithm, Repetitive Information, and Sales on Online Platform".</p> <p>The R code containsboth code for simulation and code for estimation.</p>
Investigating Online Art Search through Quantitative Behavioral Data and Machine Learning Techniques - Dataset
<p>This dataset includes the detailed values and scripts used to study behavioral aspects of users searching online for Art and Culture by analyzing quantitative data collected by the Art Boulevard search engine using machine learning techniques. This dataset is part of the core methodology, results and discussion sections of the research paper entitled "<strong>Investigating Online Art Search through Quantitative Behavioral Data and Machine Learning Techniques</strong>"</p>
Data to support: Moving beyond heritability in the search for coral adaptive potential
<p><span>Global environmental change is happening at unprecedented rates. Coral reefs are among the ecosystems most threatened by global change. For wild populations to persist, they must adapt. Knowledge shortfalls about corals' complex ecological and evolutionary dynamics, however, stymie predictions about potential adaptation to future conditions. Here, we review adaptation through the lens of quantitative genetics. We argue that coral adaptation studies can benefit greatly from "wild" quantitative genetic methods, where traits are studied in wild populations undergoing natural selection, genomic relationship matrices can replace breeding experiments, and analyses can be extended to examine genetic constraints among traits. Individuals with advantageous genotypes for anticipated future conditions can be identified. Finally, genomic genotyping supports simultaneous consideration of how genetic diversity is arrayed across geographic and environmental distances, providing greater context for predictions of phenotypic evolution at a metapopulation scale.</span></p>
Open Government Data Corpus for Table Search
<p>Increasing amounts of structured data can provide value for research and business if the relevant data can be located. Often the data is in a data lake without a consistent schema, making locating useful data challenging. Table search is a growing research area, but existing benchmarks have been limited to displayed tables. Tables sized and formatted for display in a Wikipedia page or ArXiv paper are considerably different from data tables in both scale and style. By using metadata associated with open data from government portals, we create the first dataset to benchmark search over data tables at scale. We demonstrate three styles of table-to-table related table search. The three notions of table relatedness are: tables produced by the same organization, tables distributed as part of the same dataset, and tables with a high degree of overlap in the annotated tags. The keyword tags provided with the metadata also permit the automatic creation of a keyword search over tables benchmark. We provide baselines on this dataset using existing methods including traditional and neural approaches. </p>
Galactic Diffuse Emission Spectrum between 0.5 and 8.0 MeV and between 30 keV and 8 MeV for the search of PBH and Light Dark Matter
<p>Spectra and response files of the soft gamma-ray spectrum from Inverse Compton scattering in the Milky Way between 0.5 and 8.0 MeV, measured with INTEGRAL/SPI. The fluxes have been extracted with spimodfit assuming GALPROP v56 models according to Voyager 1, AMS-02, and Fermi/LAT data, plus different variants to change the interstellar radiation field, the diffusion properties, or the cosmic-ray halo size (<a href="https://ui.adsabs.harvard.edu/abs/2022A%26A...660A.130S/abstract">Siegert et al., 2022</a>). The respective galdef files are included. In particular:</p> <ul> <li>voyager: propagation model of Bisschoff et al. (2019), Table 2</li> <li>voyager_Dg10001: <span class="math-tex">\(\delta_1 = 0\)</span></li> <li>voyager_Dg1Dg205: <span class="math-tex">\(\delta_1 = \delta_2 = 0.5\)</span></li> <li>voyager_ISRFopt10: factor 10 stronger optical ISFR</li> <li>voyager_z8kpc_KL: thick halo with 8 kpc half-thickness</li> </ul> <p>In addition, there are spectra from 30 keV to 8 MeV (spec_0030-8000keV_<name>.fits, <a href="https://ui.adsabs.harvard.edu/abs/2022PhRvD.106b3030B/abstract">Berteaud et al., 2022</a>), separated into:</p> <ul> <li>CV: spectrum of unresolved point sources in the Galactic plane, mostly cataclysmic variables (strong systematics)</li> <li>IC: spectrum of Inverse Compton scattering of cosmic ray GeV electrons (see above)</li> <li>positronium: spectrum of positronium decay (annihilation of electrons with positrons after intermediate bound state)</li> <li>NFW: spectrum of dark matter contributions that would follow a Navarro Frenk White density profile (mostly consistent with zero)</li> <li>total: spectrum of the above combined</li> </ul> <p>The notebooks for dark matter related bounds when using this data as well as auxiliary files to create the templates are given as (<a href="https://ui.adsabs.harvard.edu/abs/2022PhRvD.106b3030B/abstract">Berteaud et al., 2022</a>, <a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.520.4167C/abstract">Calore et al., 2023</a>):</p> <ul> <li>DM_fsr.ipynb: Notebook for final state radiation bounds</li> <li>DM_fsr.py: astromodels function for final state radiation spectrum</li> <li>DM_2gamma.ipynb: Notebook for gamma gamma final state bounds</li> <li>DM_2gamma.py: astromodels function for gamma gamma spectrum</li> <li>Positronium_Spectrum.py: astromodels function for Positonium spectrum</li> <li>DM_pbh.py: script for obtaining PBH bounds</li> <li>diff_@@.csv: spectra for PBH evaporation</li> <li>total_spectrum.fits.gz: total Galactic spectrum</li> <li>NFW_spectrum.fits.gz: NFW-only spectrum</li> </ul>
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.