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Cebulka (Polish dark web cryptomarket and image board) messages data
<h3><strong>General Information</strong></h3> <p>1. <strong>Title of Dataset</strong></p> <p>Cebulka (Polish dark web cryptomarket and image board) messages data.</p> <p>2. <strong>Data Collectors</strong></p> <p>Haitao Shi (The University of Edinburgh, UK); Patrycja Cheba (Jagiellonian University); Leszek Świeca (Kazimierz Wielki University in Bydgoszcz, Poland).</p> <p>3. <strong>Funding Information</strong></p> <p>The dataset is part of the research supported by the Polish National Science Centre (Narodowe Centrum Nauki) grant 2021/43/B/HS6/00710.</p> <p>Project title: “Rhizomatic networks, circulation of meanings and contents, and offline contexts of online drug trade” (2022-2025; PLN 956 620; funding institution: Polish National Science Centre [NCN], call: OPUS 22; Principal Investigator: Piotr Siuda [Kazimierz Wielki University in Bydgoszcz, Poland]).</p> <h3><strong>Data Collection Context</strong></h3> <p>4.<strong> Data Source</strong></p> <p>Polish dark web cryptomarket and image board called Cebulka (<a href="http://cebulka7uxchnbpvmqapg5pfos4ngaxglsktzvha7a5rigndghvadeyd.onion/index.php">http://cebulka7uxchnbpvmqapg5pfos4ngaxglsktzvha7a5rigndghvadeyd.onion/index.php</a>). </p> <p>5. <strong>Purpose</strong></p> <p>This dataset was developed within the abovementioned project. The project focuses on studying internet behavior concerning disruptive actions, particularly emphasizing the online narcotics market in Poland. The research seeks to (1) investigate how the open internet, including social media, is used in the drug trade; (2) outline the significance of darknet platforms in the distribution of drugs; and (3) explore the complex exchange of content related to the drug trade between the surface web and the darknet, along with understanding meanings constructed within the drug subculture.</p> <p>Within this context, Cebulka is identified as a critical digital venue in Poland’s dark web illicit substances scene. Besides serving as a marketplace, it plays a crucial role in shaping the narratives and discussions prevalent in the drug subculture. The dataset has proved to be a valuable tool for performing the analyses needed to achieve the project’s objectives.</p> <h3><strong>Data Content</strong></h3> <p>6. <strong>Data Description</strong></p> <p>The data was collected in three periods, i.e., in January 2023, June 2023, and January 2024.</p> <p>The dataset comprises a sample of messages posted on Cebulka from its inception until January 2024 (including all the messages with drug advertisements). These messages include the initial posts that start each thread and the subsequent posts (replies) within those threads. The dataset is organized into two directories. The “cebulka_adverts” directory contains posts related to drug advertisements (both advertisements and comments). In contrast, the “cebulka_community” directory holds a sample of posts from other parts of the cryptomarket, i.e., those not related directly to trading drugs but rather focusing on discussing illicit substances. The dataset consists of 16,842 posts.</p> <p>7. <strong>Data Cleaning, Processing, and Anonymization</strong></p> <p>The data has been cleaned and processed using regular expressions in Python. Additionally, all personal information was removed through regular expressions. The data has been hashed to exclude all identifiers related to instant messaging apps and email addresses. Furthermore, all usernames appearing in messages have been eliminated.</p> <p>8. <strong>File Formats and Variables/Fields</strong></p> <p>The dataset consists of the following files:</p> <ul> <li>Zipped .txt files (“cebulka_adverts.zip” and “cebulka_community.zip”) containing all messages. These files are organized into individual directories that mirror the folder structure found on Cebulka.</li> <li>Two .csv files that list all the messages, including file names and the content of each post. The first .csv lists messages from “cebulka_adverts.zip,” and the second .csv lists messages from “cebulka_community.zip.”</li> </ul> <h3><strong>Ethical Considerations</strong></h3> <p>9. <strong>Ethics Statement</strong></p> <p>A set of data handling policies aimed at ensuring safety and ethics has been outlined in the following paper:</p> <p>Harviainen, J.T., Haasio, A., Ruokolainen, T., Hassan, L., Siuda, P., Hamari, J. (2021). Information Protection in Dark Web Drug Markets Research [in:] Proceedings of the 54th Hawaii International Conference on System Sciences, HICSS 2021, Grand Hyatt Kauai, Hawaii, USA, 4-8 January 2021, Maui, Hawaii, (ed.) Tung X. Bui, Honolulu, HI, pp. 4673-4680.</p> <p>The primary safeguard was the early-stage hashing of usernames and identifiers from the messages, utilizing automated systems for irreversible hashing. Recognizing that automatic name removal might not catch all identifiers, the data underwent manual review to ensure compliance with research ethics and thorough anonymization.</p>
Dopek.eu (Polish clear web and dark web message board) messages data
<h3><strong>General Information</strong></h3> <p>1. <strong>Title of Dataset</strong></p> <p>Dopek.eu (Polish clear web and dark web message board) messages data.</p> <p>2. <strong>Data Collectors</strong></p> <p>Haitao Shi (The University of Edinburgh, UK); Leszek Świeca (Kazimierz Wielki University in Bydgoszcz, Poland).</p> <p>3. <strong>Funding Information</strong></p> <p>The dataset is part of the research supported by the Polish National Science Centre (Narodowe Centrum Nauki) grant 2021/43/B/HS6/00710.</p> <p>Project title: “Rhizomatic networks, circulation of meanings and contents, and offline contexts of online drug trade” (2022-2025; PLN 956 620; funding institution: Polish National Science Centre [NCN], call: OPUS 22; Principal Investigator: Piotr Siuda [Kazimierz Wielki University in Bydgoszcz, Poland]).</p> <h3><strong>Data Collection Context</strong></h3> <p>4. <strong>Data Source</strong></p> <p>Clear web and dark web message board called dopek.eu (<a href="https://dopek.eu/">https://dopek.eu/</a>). </p> <p>5.<strong> Purpose</strong></p> <p>This dataset was developed within the abovementioned project. The project delves into internet dynamics within disruptive activities, specifically focusing on the online drug trade in Poland. It aims to (1) examine the utilization of the open internet, including social media, in the drug trade; (2) delineate the role of darknet environments in narcotics distribution; and (3) uncover the intricate flow of drug trade-related content and its meanings between the open web and the darknet, and how these meanings are shaped within the so-called drug subculture.</p> <p>The dopek.eu forum emerges as a pivotal online space on the Polish internet, serving as a hub for trading, discussions, and the exchange of knowledge and experiences concerning the use of the so-called new psychoactive substances (designer drugs). The dataset has been instrumental in conducting analyses pertinent to the earlier project goals.</p> <p>6.<strong> Collection Method</strong></p> <p>The dataset was compiled using the Scrapy framework, a web crawling and scraping library for Python. This tool facilitated systematic content extraction from the targeted message board.</p> <p>7.<strong> Collection Date</strong></p> <p>The data was collected in October 2023.</p> <h3><strong>Data Content</strong></h3> <p>8.<strong> Data Description</strong></p> <p>The dataset comprises all messages posted on dopek.eu from its inception until October 2023. These messages include the initial posts that start each thread and the subsequent posts (replies) within those threads. A .txt file has been prepared detailing the structure of the message board folders from which the posts were extracted. The dataset includes 171,121 posts.</p> <p>9.<strong> Data Cleaning, Processing, and Anonymization</strong></p> <p>The data has been cleaned and processed using regular expressions in Python. Additionally, all personal information was removed through regular expressions. The data has been hashed to exclude all identifiers related to instant messaging apps and email addresses. Furthermore, all usernames appearing in messages have been eliminated.</p> <p>10. <strong>File Formats and Variables/Fields</strong></p> <p>The dataset consists of the following types of files:</p> <ul> <li>Zipped .txt files (dopek.zip) containing all messages (posts).</li> <li>A .csv file that lists all the messages, including file names and the content of each post.</li> </ul> <h3><strong>Accessibility and Usage</strong></h3> <p><strong>11. Access Conditions</strong></p> <p>The data can be accessed without any restrictions.</p> <p><strong>12. Related Documentation</strong></p> <p>Attached are .txt files detailing the tree of folders for “dopek.zip”.</p> <h3><strong>Ethical Considerations</strong></h3> <p><strong>13. Ethics Statement</strong></p> <p>A set of data handling policies aimed at ensuring safety and ethics has been outlined in the following paper:</p> <p>Harviainen, J.T., Haasio, A., Ruokolainen, T., Hassan, L., Siuda, P., Hamari, J. (2021). Information Protection in Dark Web Drug Markets Research [in:] Proceedings of the 54th Hawaii International Conference on System Sciences, HICSS 2021, Grand Hyatt Kauai, Hawaii, USA, 4-8 January 2021, Maui, Hawaii, (ed.) Tung X. Bui, Honolulu, HI, pp. 4673-4680.</p> <p>The primary safeguard was the early-stage hashing of usernames and identifiers from the posts, utilizing automated systems for irreversible hashing. Recognizing that scraping and automatic name removal might not catch all identifiers, the data underwent manual review to ensure compliance with research ethics and thorough anonymization.</p>
Light-saturated photosynthetic rate, dark respiration, stomatal conductance and ratio of internal to external carbon dioxide concentration from the 1980-82 Eriophorum vaginatum reciprocal transplant plots from Eagle Creek to Prudhoe Bay, Alaska, 2010
In 1980-1982, six transplant gardens were established along a latitudinal gradient in interior Alaska from Eagle Creek, AK, in the south to Prudhoe Bay, AK, in the north (Shaver et al. 1986) .Three sites, Toolik Lake (TL), Sagwon (SAG), and Prudhoe Bay (PB) are north of the continental divide and the remaining three, Eagle Creek (EC), No Name Creek (NN), and Coldfoot (CF), are south of the continental divide. Each garden consisted of 10 individual tussocks transplanted back to their home-site, as well as 10 individuals from each of the other transplant sites. Data were collected in July 2010 for tussocks transplanted in 1980-82 in a reciprocal transplant experiment and then harvested in 2011. Important variables are garden name, source population, light-saturated photosynthetic rate, dark respiration, stomatal conductance and ratio of internal to external carbon dioxide concentration.
Supplementary Material for A Global Analysis of Dark Matter Signals from 27 Dwarf Spheroidal Galaxies using 11 Years of Fermi-LAT Observations
<p><strong>Description of the Supplementary Data</strong></p> <p>This record contains tabulated Bayesian and frequentist exclusion limits, profile likelihood maps and posterior probability maps for the publication S. Hoof, A. Geringer-Sameth, and R. Trotta, “<i>A Global Analysis of Dark Matter Signals from 27 Dwarf Spheroidal Galaxies using 11 Years of Fermi-LAT Observations</i>,” <a href="https://doi.org/10.1088/1475-7516/2020/02/012">JCAP 02 (2020) 012</a> (also available on the <a href="https://arxiv.org/abs/1812.06986">arXiv</a>). The dwarf spheroidal galaxies considered in this work are (in alphabetical order): Aquarius II, Boötes I, Canes Venatici I, Canes Venatici II, Carina, Carina II, Coma Berenices, Draco, Draco II, Fornax, Grus I, Hercules, Horologium I, Leo I, Leo II, Leo IV, Leo V, Pegasus III, Pisces II, Reticulum II, Sculptor, Segue 1, Sextans, Tucana II, Ursa Major I, Ursa Major II, and Ursa Minor.</p> <p>This record consists of the following files, which correspond to the limits presented Figures 9 and 10 of the paper. The files can be downloaded individually or obtained by downloading and unpacking the <code>record_2612268.zip</code>. In what follows,<code><strong>[CHANNEL]</strong></code> refers to the annihilation channel used, i.e. <i>e<sup>+</sup> e<sup>-</sup></i>, <i>μ<sup>+</sup> μ<sup>-</sup></i>, <i>τ<sup>+</sup> τ<sup>-</sup></i>, <i>b b̄</i>, <i>c c̄</i>, <i>t t̄</i>, <i>g g</i>, <i>W<sup>+</sup> W<sup>-</sup></i>, and <i>Z Z</i>. We also provide a simple plotting script for <code>Python</code>, named <code>plotting_script.py</code>, which provides basic plotting routines for all files.</p> <ul> <li>One-dimensional limits on <i><σ v></i>. The files <code>oneD_frequentist_limits_<strong>[CHANNEL]</strong>_channel.txt</code> contain the frequentist limits (at 95% confidence level, 1 degree of freedom) given the value of the WIMP mass <i>m<sub>χ</sub></i> tabulated there. The files <code>oneD_Bayesian_limits_<strong>[CHANNEL]</strong>_channel.txt</code> contain the Bayesian limit (95% credibility conditioned on the mass <i>m<sub>χ</sub></i> tabulated there).</li> <li>Two-dimensional grid of profile likelihood values. The files <code>twoD_profile_likelihood_map_<strong>[CHANNEL]</strong>_channel.txt</code> contain the natural logarithm of the profile likelihood w.r.t. the global best-fit likelihood value for that channel together with the corresponding values of <i>m<sub>χ</sub></i> and <i><σ v></i>. Note that for obtaining the limits in Fig. 10, which are conditioned on the WIMP mass, one needs to rescale the profile likelihood values with the maximum profile likelihood for a given WIMP mass.</li> <li>Two-dimensional grid of posterior probabilities for each combination of <i>m<sub>χ</sub></i> and <i><σ v></i>. The files <code>twoD_posterior_probability_map_<strong>[CHANNEL]</strong>_channel.txt</code> contain probabilities (obtained using a log-uniform prior on <i><σ v></i>) together with the corresponding values of <i>m<sub>χ</sub></i> and <i><σ v></i>. The tabulated values of <i>m<sub>χ</sub></i> and <i><σ v></i> correspond to the centres of the respective bins in <i>m<sub>χ</sub></i> and <i><σ v></i> and the posterior probability contained in them (the total posterior probability sums to 1).</li> </ul> <p>Please contact the authors if you require different data or have any questions regarding this data set.</p>
TIDMAD: Time Series Dataset for Discovering Dark Matter with AI Denoising
<p>TIDMAD is the first dataset and benchmark from a dark matter physics experiment, providing ultra-long time series data and comprehensive tools that enable machine learning models to directly advance the fundamental physics search for dark matter.</p> <p>This data is availble for download via <code>download_data.py</code>. Metadata for this dataset is specified in <code>TIDMAD_croissant.json</code>. The file names are listed in <code>filelist.dat</code>. For furhter information and publically available code, please see the associated <a href="https://github.com/jessicafry/TIDMAD" target="_blank" rel="noopener">GitHub repository</a>. For more information on this dataset and benchmark, please reference our TIDMAD paper.</p>
Dark matter flow dataset Part I: Halo-based statistics from cosmological N-body simulation
<p>Dark matter (DM), if exists, is believed to be cold, collisionless, dissipationless, non-baryonic, barely interacting with baryonic matter except through gravity, and sufficiently smooth on large scales with a fluid-like behavior. The flow of dark matter can be best described by a self-gravitating collisionless fluid dynamics (SG-CFD). The statistics of dark matter density, velocity, acceleration, energy, momentum, and their redshift evolution play essential roles for structure formation and evolution. These information can be systematically extracted from cosmological N-body simulations by either i) a structural (halo-based) or ii) a statistical (correlation-based) approach. In this halo-based statistical dataset, i) all halos in N-body system are identified with all particles divided into halo and out-of-halo particles; ii) halos are grouped into halo groups including all halos of the same mass (m<sub>h</sub>); iii) the redshift (z) and mass scale (m<sub>h</sub>) dependence of all halo properties (momentum, energy, size, shape, velocity, acceleration, etc.) are presented . </p> <p>Applications of cascade and statistical theory for dark matter and bulge-SMBH evolution:</p> <ol> <li>Dark matter particle mass ,size, and properties from energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.07240">arxiv</a> 2) <a href="https://zenodo.org/record/6640353">zenodo slides</a></li> <li>Origin of MOND acceleration & deep-MOND from acceleration fluctuation & energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2203.05606">arxiv</a> 2) <a href="https://zenodo.org/record/6640386">zenodo slides</a></li> <li>The baryonic-to-halo mass relation from mass and energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2203.06899">arxiv</a> 2) <a href="https://zenodo.org/record/6640355">zenodo slides</a></li> <li>Universal scaling laws and density slope for dark matter haloes: 1) <a href="http://doi.org/10.48550/arXiv.2209.03313">arxiv</a> 2) <a href="https://zenodo.org/record/7059193">zenodo slides</a> 3) <a href="http://doi.org/10.1038/s41598-023-31083-z">paper</a></li> <li>Dark matter halo mass functions and density profiles from mass/energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2210.01200">arxiv</a> 2) <a href="https://zenodo.org/record/7146473">zenodo slides</a> 3) <a href="https://doi.org/10.1038/s41598-023-42958-6">paper</a></li> <li>Energy cascade for distribution and evolution of supermassive black holes (SMBHs): 2) <a href="http://doi.org/10.5281/zenodo.7490502">zenodo slides</a></li> </ol> <p>Condensed slides for all applications "<a href="http://doi.org/10.5281/zenodo.7508310">Cascade Theory for Turbulence, Dark Matter, and bulge-SMBH evolution </a>"</p> <p>The two relevant datasets and accompanying presentation can be found at: </p> <ol> <li><a href="https://doi.org/10.5281/zenodo.6541230">Dark matter flow dataset Part I: Halo-based statistics from cosmological N-body simulation</a> </li> <li><a href="https://doi.org/10.5281/zenodo.6569898">Dark matter flow dataset Part II: Correlation-based statistics from cosmological N-body simulation</a>.</li> <li><a href="https://doi.org/10.5281/zenodo.6569901">A comparative study of Dark matter flow & hydrodynamic turbulence and its applications</a></li> </ol> <p>The same dataset also available on Github at: <a href="https://github.com/ZhijieXu2022/dark_matter_flow_dataset/">Github: dark_matter_flow_dataset</a> and zenodo at: <a href="http://doi.org/10.5281/zenodo.6586212">Dark matter flow dataset from cosmological N-body simulation</a>.</p> <p>Cascade and statistical theory developed by these datasets:</p> <ol> <li>Inverse mass cascade in dark matter flow and effects on halo mass functions: 1) <a href="http://doi.org/10.48550/arXiv.2109.09985">arxiv</a> 2) <a href="https://zenodo.org/record/6639536">zenodo slides</a> </li> <li>Inverse mass cascade and effects on halo deformation, energy, size, and density profiles: 1) <a href="http://doi.org/10.48550/arXiv.2109.12244">arxiv</a> 2) <a href="https://zenodo.org/record/6640337">zenodo slides</a></li> <li>Inverse energy cascade in dark matter flow and effects of halo shape: 1) <a href="http://doi.org/10.48550/arXiv.2110.13885">arxiv</a> 2) <a href="https://zenodo.org/record/6640331">zenodo slides</a></li> <li>The mean flow, velocity dispersion, energy transfer and evolution of dark matter halos: 1) <a href="http://doi.org/10.48550/arXiv.2201.12665">arxiv</a> 2) <a href="https://zenodo.org/record/6640380">zenodo slides</a></li> <li>Two-body collapse model and generalized stable clustering hypothesis for pairwise velocity 1) <a href="http://doi.org/10.48550/arXiv.2110.05784">arxiv</a> 2) <a href="https://zenodo.org/record/6640306">zenodo slides</a></li> <li>Energy, momentum, spin parameter in dark matter flow and integral constants of motion: 1) <a href="http://doi.org/10.48550/arXiv.2202.04054">arxiv</a> 2) <a href="https://zenodo.org/record/6640322">zenodo slides</a></li> <li>Maximum entropy distributions of dark matter in ΛCDM cosmology: 1) <a href="http://doi.org/10.48550/arXiv.2110.03126">arxiv</a> 2) <a href="https://zenodo.org/record/6640373">zenodo slides</a> 3) <a href="http://doi.org/10.1051/0004-6361/202346429">paper</a></li> <li>Halo mass functions from maximum entropy distributions in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2110.09676">arxiv</a> 2) <a href="https://zenodo.org/record/6640325">zenodo slides</a></li> <li>On the statistical theory of self-gravitating collisionless dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.00910">arxiv</a> 2) <a href="https://zenodo.org/record/6640705">zenodo slides</a> 3) <a href="http://doi.org/10.1063/5.0151129">paper</a></li> <li>High order kinematic and dynamic relations for velocity correlations in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.02991">arxiv</a> 2) <a href="https://zenodo.org/record/6640684">zenodo slides</a></li> <li>Evolution of density and velocity distributions and two-thirds law for pairwise velocity: 1) <a href="http://doi.org/10.48550/arXiv.2202.06515">arxiv</a> 2) <a href="https://zenodo.org/record/6640676">zenodo slides</a></li> </ol>
Dark matter flow dataset Part II: Correlation-based statistics from cosmological N-body simulation
<p>Dark matter (DM), if exists, is believed to be cold, collisionless, dissipationless, non-baryonic, barely interacting with baryonic matter except through gravity, and sufficiently smooth on large scales with a fluid-like behavior. The flow of dark matter can be best described by a self-gravitating collisionless fluid dynamics (SG-CFD). The statistics of dark matter density, velocity, acceleration, energy, momentum, and their redshift evolution play essential roles for structure formation and evolution. These information can be systematically extracted from cosmological N-body simulations by either i) a structural (halo-based) or ii) a statistical (correlation-based) approach. In this correlation-based statistical dataset, i) all particle pairs with any given separation r in a N-body system are identified; ii) statistical measures are calculated over all particle pairs with the same separation r (pairwise average); iii) the redshift (z) and scale (r) dependence of all statistical measures (correlation/moment/structure/dispersion/spectrum functions for density, velocity and potential etc.) are presented. </p> <p>Applications of cascade and statistical theory for dark matter and bulge-SMBH evolution:</p> <ol> <li>Dark matter particle mass ,size, and properties from energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.07240">arxiv</a> 2) <a href="https://zenodo.org/record/6640353">zenodo slides</a></li> <li>Origin of MOND acceleration & deep-MOND from acceleration fluctuation & energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2203.05606">arxiv</a> 2) <a href="https://zenodo.org/record/6640386">zenodo slides</a></li> <li>The baryonic-to-halo mass relation from mass and energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2203.06899">arxiv</a> 2) <a href="https://zenodo.org/record/6640355">zenodo slides</a></li> <li>Universal scaling laws and density slope for dark matter haloes: 1) <a href="http://doi.org/10.48550/arXiv.2209.03313">arxiv</a> 2) <a href="https://zenodo.org/record/7059193">zenodo slides</a> 3) <a href="http://doi.org/10.1038/s41598-023-31083-z">paper</a></li> <li>Dark matter halo mass functions and density profiles from mass/energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2210.01200">arxiv</a> 2) <a href="https://zenodo.org/record/7146473">zenodo slides</a> 3) <a href="https://doi.org/10.1038/s41598-023-42958-6">paper</a></li> <li>Energy cascade for distribution and evolution of supermassive black holes (SMBHs): 2) <a href="http://doi.org/10.5281/zenodo.7490502">zenodo slides</a></li> </ol> <p>Condensed slides for all applications "<a href="http://doi.org/10.5281/zenodo.7508310">Cascade Theory for Turbulence, Dark Matter, and bulge-SMBH evolution </a>"</p> <p>The two relevant datasets and accompanying presentation can be found at: </p> <ol> <li><a href="https://doi.org/10.5281/zenodo.6541230">Dark matter flow dataset Part I: Halo-based statistics from cosmological N-body simulation</a> </li> <li><a href="https://doi.org/10.5281/zenodo.6569898">Dark matter flow dataset Part II: Correlation-based statistics from cosmological N-body simulation</a>.</li> <li><a href="https://doi.org/10.5281/zenodo.6569901">A comparative study of Dark matter flow & hydrodynamic turbulence and its applications</a></li> </ol> <p>The same dataset also available on Github at: <a href="https://github.com/ZhijieXu2022/dark_matter_flow_dataset/">Github: dark_matter_flow_dataset</a> and zenodo at: <a href="http://doi.org/10.5281/zenodo.6586212">Dark matter flow dataset from cosmological N-body simulation</a>.</p> <p>Cascade and statistical theory developed by these datasets:</p> <ol> <li>Inverse mass cascade in dark matter flow and effects on halo mass functions: 1) <a href="http://doi.org/10.48550/arXiv.2109.09985">arxiv</a> 2) <a href="https://zenodo.org/record/6639536">zenodo slides</a> </li> <li>Inverse mass cascade and effects on halo deformation, energy, size, and density profiles: 1) <a href="http://doi.org/10.48550/arXiv.2109.12244">arxiv</a> 2) <a href="https://zenodo.org/record/6640337">zenodo slides</a></li> <li>Inverse energy cascade in dark matter flow and effects of halo shape: 1) <a href="http://doi.org/10.48550/arXiv.2110.13885">arxiv</a> 2) <a href="https://zenodo.org/record/6640331">zenodo slides</a></li> <li>The mean flow, velocity dispersion, energy transfer and evolution of dark matter halos: 1) <a href="http://doi.org/10.48550/arXiv.2201.12665">arxiv</a> 2) <a href="https://zenodo.org/record/6640380">zenodo slides</a></li> <li>Two-body collapse model and generalized stable clustering hypothesis for pairwise velocity 1) <a href="http://doi.org/10.48550/arXiv.2110.05784">arxiv</a> 2) <a href="https://zenodo.org/record/6640306">zenodo slides</a></li> <li>Energy, momentum, spin parameter in dark matter flow and integral constants of motion: 1) <a href="http://doi.org/10.48550/arXiv.2202.04054">arxiv</a> 2) <a href="https://zenodo.org/record/6640322">zenodo slides</a></li> <li>Maximum entropy distributions of dark matter in ΛCDM cosmology: 1) <a href="http://doi.org/10.48550/arXiv.2110.03126">arxiv</a> 2) <a href="https://zenodo.org/record/6640373">zenodo slides</a> 3) <a href="http://doi.org/10.1051/0004-6361/202346429">paper</a></li> <li>Halo mass functions from maximum entropy distributions in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2110.09676">arxiv</a> 2) <a href="https://zenodo.org/record/6640325">zenodo slides</a></li> <li>On the statistical theory of self-gravitating collisionless dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.00910">arxiv</a> 2) <a href="https://zenodo.org/record/6640705">zenodo slides</a> 3) <a href="http://doi.org/10.1063/5.0151129">paper</a></li> <li>High order kinematic and dynamic relations for velocity correlations in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.02991">arxiv</a> 2) <a href="https://zenodo.org/record/6640684">zenodo slides</a></li> <li>Evolution of density and velocity distributions and two-thirds law for pairwise velocity: 1) <a href="http://doi.org/10.48550/arXiv.2202.06515">arxiv</a> 2) <a href="https://zenodo.org/record/6640676">zenodo slides</a></li> </ol>
Beyond the Four-Level Model: Dark and Hot States in Quantum Dots Degrade Photonic Entanglement
<p><strong>Dataset for "Beyond the four-level model: Dark and hot states in quantum dots degrade photonic entanglement"</strong></p> <p><em>Nano Lett.</em> 2023, 23, 4, 1409–1415<br> Publication Date: February 6, 2023<br> <a href="https://doi.org/10.1021/acs.nanolett.2c04734">https://doi.org/10.1021/acs.nanolett.2c04734</a></p> <p>A description of the dataset is found in the <strong>readme.md</strong> file (markdown markup language).</p> <p><strong>Data reuse</strong><br> Please cite B.U. Lehner et al., <em>Nano Lett.</em> 2023, 23, 4, 1409–1415 (2023) in publications that reuse this data and if possible inform the corresponding authors.</p>
Carbon dioxide response curve, dark respiration, specific leaf area, and leaf nitrogen data for the 2014 Eriophorum vaginatum reciprocal transplant gardens at Toolik Lake and Sagwon, AK, collected in 2016.
Transplant gardens at Toolik Lake and Sagwon were established in 2014. At each location, 60 tussocks each from ecotypes of Eriophorum vaginatum from Coldfoot (CF, 67°15′32″N, 150°10′12″W), Toolik Lake (TL, 68°37′44″N, 149°35′0″W), and Sagwon (SAG, 69°25′26″N, 148°42′49″W) were transplanted. Half the transplanted tussocks were grown under ambient conditions, while the other half were exposed to passive warming supplied by open-top chambers (OTC). Data were collected in late June through July 2016 include carbon dioxide response curve data, dark respiration, specific leaf area, and leaf nitrogen content.
Dark respiration and photosynthesis data from Dry Heath Nitrogen & Phosphorus addition plots, Arctic LTER, Toolik Field Station, Alaska, summers 2023-2024.
To determine the effects of weather variability on Arctic plant functioning, we conducted this study looking at the response of plant dark respiration and photosynthesis to short-term, high-frequency, temperature and light variability. We measured Betula nana, Chamaenerion angustifolium, and Calamagrostis stricta from the dry heath tundra N&P fertilized plots. We took measurements through two summer seasons. The first summer we obtained data regarding responses to variable temperature and light, and in the second summer we obtained the dark respiration to temperature response and photosynthesis to light response curves.
Arbuscular mycorrhizal fungi and dark septate endophytes root colonization in Upper Green Lakes Valley, 2007-2016
Arbuscular mycorrhizal fungi (AMF) and dark septate endophytes (DSE) are two fungal groups that colonize plant roots and can benefit plant growth, but little is known about their landscape distributions. We performed sequencing and microscopy on a variety of plants across a high-elevation landscape featuring plant density, snowpack, and nutrient gradients. Percent colonization by both AMF and DSE varied significantly among plant species, and DSE colonized forbs and grasses more than sedges. AMF were more abundant in roots at lower elevation areas with lower snowpack and lower phosphorus and nitrogen levels, suggesting increased hyphal recruitment by plants to aid in nutrient uptake. DSE colonization was highest in areas with less snowpack and higher inorganic nitrogen levels, suggesting an important role for these fungi in mineralizing organic nitrogen. Both of these groups of fungi are likely to be important for plant fitness and establishment in areas limited by phosphorus and nitrogen.
Data bundle for "Spherical-angular dark field imaging and sensitive microstructural phase clustering with unsupervised machine learning"
<p>Prepared by Tom McAuliffe (t.mcauliffe17@imperial.ac.uk)</p> <p>This repository is a release of the raw data and analysis results for: 'Spherical-angular dark field imaging and sensitive microstructural phase clustering with unsupervised machine learning' </p> <p>The raw data is given as 'yprime.h5' - this contains patterns and metadata in the Bruker-exported format.</p> <p>Scripts for dataset decomposition into latent factors are given in 'Scripts'.</p> <p>Our spherical analysis code is included in 'SphericalAngleDF'.</p> <p>Outputs of our analysis code are contained in 'Analysis'.</p> <p>Figures for the paper are included in 'Figures'.</p> <p> </p>
The Effects of Asymmetric Dark Matter on Stellar Evolution I: Spin-Dependent Scattering - Supporting Data
<p>Supporting code and data for the paper: </p> <p><em>The Effects of Asymmetric Dark Matter on Stellar Evolution I: Spin-Dependent Scattering</em></p> <p>Raen (2020)</p> <p><strong>Supporting code</strong> includes `run_star_extras.f`, inlist templates, and our dark matter module (to be used in conjunction with MESA: <a href="http://mesa.sourceforge.net/index.html">Modules for Experiments in Stellar Astrophysics</a>). The full source code used in the production of this paper is available at <a href="https://github.com/troyraen/DM-in-Stars/">github.com/troyraen/DM-in-Stars</a> in the Raen2020 branch. (The master branch is intended for use by those wishing to use our module to explore DM effects beyond the scope of this paper.) We used MESA version 12115, and MESA SDK version 20190830.</p> <p><strong>Model data</strong> includes MESA history and profile data for the models highlighted in the paper (<span class="math-tex">\(1.0\ \mathrm{M}_\odot\)</span> and <span class="math-tex">\(3.5\ \mathrm{M}_\odot\)</span> models with <span class="math-tex">\(\Gamma_B = 0\)</span> (no dark matter), <span class="math-tex">\(\Gamma_B = 10^4\)</span>, and <span class="math-tex">\(\Gamma_B = 10^6\)</span>). The specific inlists used to generate the models are also included. Additional data will be shared on reasonable request to the paper's corresponding author.</p>
Supplementary Data: Status of the scalar singlet dark matter model (arXiv:1705.07931)
<p>Supplementary Data</p> <p><em>Status of the scalar singlet dark matter model</em><br> <em>arXiv:1705.07931</em></p> <p>The files in this record contain data for the scalar singlet dark matter model considered in the GAMBIT "Round 1" scalar singlet paper.</p> <p>The files consist of</p> <ul> <li>Three YAML files, each corresponding to a different parameter range</li> <li>StandardModel_SLHA2_SingletDM_scan_15.yaml, a universal YAML fragment included from the other three YAML files</li> <li>Three hdf5 files. SingletDM.hdf5 contains the combined results of all sampling runs, and is the basis for the profile likelihood plots in the paper. SingletDM_TW_full.hdf5 and SingletDM_TW_lowmass.hdf5 contain the results from T-Walk scans over the full and low-mass parameter ranges, respectively. These are the bases for the marginalised posterior plots in the paper.</li> <li>An example pip file corresponding to each hdf5 file, for producing plots using pippi</li> <li>A tarball best_fits_yaml.tar.gz containing YAML files of the best-fit point in each subregion of the fit.</li> </ul> <p>The YAML files corresponding to different parameter ranges follow the naming scheme SingletDM_[slice].yaml, where slice may be full, lowmass or neck. Each of these YAML files contains entries in the Scanners node for running Diver, MultiNest, TWalk and GreAT.</p> <p>A few caveats to keep in mind:</p> <ol> <li> <p>The YAML files that we give here are updated compared to the ones that we used when generating the hdf5 file, in order to match the set of available options in the release version of GAMBIT 1.0.0. The included physics and numerics are however identical.</p> </li> <li> <p>The YAML files are designed to work with the tagged release of GAMBIT 1.0.0, and the pip file is tested with pippi 2.0, commit 2ab061a8. They may or may not work with later versions of either software (but you can of course always obtain the version that they do work with via the git history).</p> </li> <li> <p>The pip file is an example only. Users wishing to reproduce the more advanced plots in any of the GAMBIT papers should contact us for tips or scripts, or experiment for themselves. Many of these scripts are in multiple parts and require undocumented manual interventions and steps in order to implement various plot-specific customisations, so please don't expect the same level of polish as for files provided here or in the GAMBIT repo.</p> </li> </ol>
Supplementary Data for Inhomogeneous Energy Injection in the 21-cm Power Spectrum: Sensitivity to Dark Matter Decay
<p>This dataset contains the interpolation tables for use with the DM21cm code release as part of "Inhomogeneous Energy Injection in the 21-cm Power Spectrum: Sensitivity to Dark Matter Decay." For details on usage, see the public github repository at: https://github.com/yitiansun/DM21cm. </p>
Supplementary data for "Resonant or asymmetric: The status of sub-GeV dark matter"
<p>The files in this record contain supplementary data for the study, "Resonant or asymmetric:<br>The status of sub-GeV dark matter". Samples have been created using <a href="https://gambitbsm.org/" target="_blank" rel="noopener">GAMBIT</a> and figures can be reproduced with <a href="https://github.com/tegonzalo/pippi" target="_blank" rel="noopener">pippi</a>.</p>
Supplementary Data: Impact of vacuum stability, perturbativity and XENON1T on global fits of Z2 and Z3 scalar singlet dark matter (arXiv:1806.11281)
<p> </p> <p><strong>Supplementary Data</strong></p> <p> </p> <p><em>Impact of vacuum stability, perturbativity and XENON1T on global fits of Z<sub>2</sub> and Z<sub>3</sub> scalar singlet dark matter</em> <a href="https://arxiv.org/abs/1806.xxxxx"><em>arXiv:</em></a><em><a href="https://arxiv.org/abs/1806.11281">1806.11281</a></em></p> <p>The files in this record contain data for the scalar singlet dark matter models considered in the <a href="http://gambit.hepforge.org">GAMBIT</a> "Scalar singlet Mark II" paper.</p> <p>The files consist of</p> <ul> <li>30 regular YAML files</li> <li><code>StandardModel_SLHA2_scan.yaml</code>, a universal YAML fragment included from the other YAML files</li> <li>14 hdf5 files. 8 of these correspond to the complete set of combined samples for each fit. These 8 fits are generated from all binary permutations of three run properties: Z2 or Z3 model, with or without absolute vacuum stability demanded, and with constraints from the 2017 or 2018 XENON1T data. These 8 hdf5 files are used to generate the profile likelihood plots in the paper. The other 6 hdf5 files are the results of T-Walk runs, and are used to generate the posterior pdfs in the paper.</li> <li>Some example pip files for producing plots from the hdf5 files using <a href="github.com/patscott/pippi">pippi</a></li> <li>A tarball <code>best_fits_yaml.tar.gz</code> containing YAML files of the best-fit point in each of the 8 fits.</li> </ul> <p>The files follow the naming scheme <code>SingletDM_[model]_[slice]_[vacuum]_[xenon]_[prior]_[scanner].yaml</code>.</p> <ul> <li>model: <code>Z2</code> or <code>Z3</code></li> <li>slice: <code>full</code>, <code>lowmass</code>, <code>neck</code> or absent (for hdf5 files)</li> <li>vacuum: <code>ms</code> (metastable) or <code>vs</code> (absolute vacuum stability)</li> <li>prior: <code>logmu3</code>, <code>flatmu3</code> or absent (for Z<sub>2</sub> scans)</li> <li>scanner: <code>TWalk</code> or absent (implies Diver scans in the case of YAML files, and indicates merged samples potentially from both Diver and T-Walk in the case of hdf5 files)</li> </ul> <p>A few caveats to keep in mind:</p> <ol> <li> <p>The YAML files are designed to work with GAMBIT 1.2.0, commit e4d3f739, and the pip files are tested with pippi 2.1, commit c094b8c8. They may or may not work with later versions of either software (but you can of course always obtain the version that they do work with via the git history).</p> </li> <li> <p>The pip files are examples only. Users wishing to reproduce the more advanced plots in any of the GAMBIT papers should contact us for tips or scripts, or experiment for themselves. Many of these scripts are in multiple parts and require undocumented manual interventions and steps in order to implement various plot-specific customisations, so please don't expect the same level of polish as for files provided here or in the GAMBIT repo. </p> </li> </ol> <p> </p>
New limit on dark photon dark matter from searches using LIGO O1 data.
<p>The three files contain the three limits in Fig.4 of the published paper.</p> <p>For each file, there are 2 columns of data, with the first column being the frequency in Hz and the second column the</p> <p>corresponding upper limit on the dimensionless dark photon squared coupling.</p> <p>limit_red.txt: the red line, O1 95% CL limits (1800s SFTs) </p> <p>limit_blue.txt: the blue line, average optimal O1 limits (893 hours)</p> <p>limit_yellow.txt: the yellow line, Nominal SNR O1 95% CL limits</p>
MERFISH data of the developing mouse visual cortex under normal- and dark-rearing
<p>MERFISH data for the manuscript "Spatial profiling of the interplay between cell type- and vision-dependent transcriptomic programs in the visual cortex"</p>
Data from: "DarkSide-20k sensitivity to light dark matter particles"
<p>These files provide expected limits from the preprint of arXiv:2407.05813, "DarkSide-20k sensitivity to light dark matter particles" and are made available by the DarkSide-20k Collaboration.</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.