Find research datasets worth reusing

CR2MET: A high-resolution precipitation and temperature dataset for the period 1960-2021 in continental Chile.

The Center for Climate and Resilience Research Meteorological dataset (CR2MET) includes two spatially-distributed products of daily precipitation and maximum/minimum near surface temperatures. The dataset covers the domain of continental Chile over a regular 0.05 degree latitude-longitude grid, and spans the period 1960-2021. Both a products are built on statistical models of the corresponding variables, calibrated against quality-controlled observational records. The CR2MET models are nurtured with a combination of data that includes different variables from ECMWF reanalysis ERA5, topographic parameters and land-surface temperature estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor.

Catalog of Cool Host Stars with Established Rotation Periods

Catalog of 249 late K- and M-type exoplanet host stars with rotation periods obtained from the literature or new analysis of space- or ground-based time-series photometry as of August 2022.  Model-based, metallicity-dependent corrections are provided but not included in gyrochronological age estimates.   Please cite the reference paper if any information from this table is used.   Table 1 in Gaidos et al. 2023, in press, in CDS format.

YOPP Southern Hemisphere 2022 Special Observing Period

This short video shows some of the radiosonde launches during the Year Of Polar Prediction Special Observing Period in the Antarctic   Year of Polar Prediction (YOPP) is an international program to Improve environmental prediction for the polar regions. The Southern Hemisphere effort dealing with Antarctica and the Southern Ocean, that is a key region for the global climate system, is called YOPP-SH. 24 stations released extra radiosondes during the 2022 winter special observing period (SOP). Countries Participating in YOPP-SH: Argentina, Australia, Brazil, Chile, China, France, Germany, Italy, Japan, Korea, New Zealand, Portugal, South Africa, Spain, Ukraine, United Kingdom, United States. The Ohio State University, SCAR: Scientific Committee on Antarctic Research, NCAR, National Science Foundation (NSF), WWRP: World Weather Research Programme, World Meteorological Organization Executive Produced by Byrd Polar and Climate Research Center Produced by: Jason Cervenec, David Bromwich, Pamela I Theodotou Direction and Editing: Pamela I Theodotou Video footage provided by individual station groups. Details and maps provided by Mariana Fontolan Litell.

openother-openFeb 2023View details →

Yiddish Periodicals in Germany

This is version 1 of a dataset of known historical Yiddish newspapers and periodicals published in Germany. The list is based upon cataloguing and holding information from the libraries and book listed in the readme file and Github repository. The list is provided as an Excel sheet and is a work in progress. Currently the list contains 227 items, 21 of which are available in digitised form. URLs for the digitised periodicals are provided; the list also provides holding locations for the other periodicals but no direct links to their online catalog records. Any comments and additions are most welcome, please get in touch via: <a href="mailto:gerben.zaagsma@uni.lu">gerben.zaagsma@uni.lu</a>.

FIG. 5. — A in A dog's life: interpreting Migration Period dog burials from Hungary

FIG. 5. — A, Scheme of Roman Period morphotypes drafted by Bennet et al. (2016); B, C, position of Langobard dogs in relation to this visual key (females are distinguished by square symbols). Abbreviations: Bp, proximal breadth (von den Driesch 1976); SD, smallest diameter (von den Driesch 1976).

FIG. 4 in A dog's life: interpreting Migration Period dog burials from Hungary

FIG. 4. — Comparison of size distributions of Roman Period dogs in Germania (A; Peters 1997) and Pannonia (B; BÖkÖnyi 1984; Bartosiewicz 1996) with those of Langobard dogs (C).

FIG. 3. — A in A dog's life: interpreting Migration Period dog burials from Hungary

FIG. 3. — A, estimating the withers heights of Langobard dogs. The relationship between first principal component (PC1) scores and skeletal withers heights in the reference material and estimates for the Langobard individuals; B, size distribution enlarged. The withers height (WH) distribution of Langobard dogs summarized in a box plot and histogram, including the Hegykő individuals. Abbreviations: ORL, Osteoarchaeological Research Laboratory, Stockholm; w, wolf.

FIG. 2 in A dog's life: interpreting Migration Period dog burials from Hungary

FIG. 2. — The grouping of extant and Langobard dogs (highlighted in yellow) based on the greatest lengths of four long bones. The Euclidean distance of 30 is marked by a dashed line. Abbreviations: F, female; M, male; Non-id, non identified; ORL, Osteoarchaeological Research Laboratory, Stockholm.

FIG. 1 in A dog's life: interpreting Migration Period dog burials from Hungary

FIG. 1. — Sites in western Hungary mentioned in the study in relation to central Europe (insert): 1, Zamárdi – KútvÖlgyi-dűlő II; 2, Keszthely – Általános iskola; 3, Dombóvár – TESCO; 4, Ménfőcsanak – BevásárlókÖzpont; 5, Hegykő – Mező. Abbreviations: AT, Austria; CH, Switzerland; CZ, Czech Republic; DE, Germany; HU, Hungary; PL, Poland; SI, Slovenia; SK, Slovakia.

CD5 index of works published in the period 1945-2016

The gzip-compressed data file contains two tab-separated fields: <ol> <li>Publication DOI</li> <li>CD₅ index (when available)</li> </ol> The data set was derived through open data using <a href="https://github.com/dspinellis/alexandria3k">Alexandria3k</a> and <a href="https://github.com/dspinellis/fast-cdindex">fast-cdindex</a>. More information about the process can be found in the following paper. Diomidis Spinellis. Open reproducible scientometric research with Alexandria3k. PLoS ONE, 18(11):e0294946, November 2023. <a href="https://dx.doi.org/10.1371/journal.pone.0294946">doi:10.1371/journal.pone.0294946</a> This version improves previous versions by incorporating into the calculation works lacking a reference list, but not calculating a CD₅ index for them. This version also excludes works published after 2016, as they lack five years of citations to them.

Measurements of diurnal variations of subsurface water temperature in Lake Kinneret during the period (Sept. 6 – 9, 2015)

The datasets include in-situ 10-minute measurements of subsurface water temperature taken at a depth of 20 cm at a site A (32.82 oN; 35.60 oE) located near the center of Lake Kinneret, during the period (Sept. 6 – 9, 2015). Lake Kinneret is located in Israel. The Campbell 107-L temperature probe was used (specifications are available online at <a href="https://www.campbellsci.asia/107-l">https://www.campbellsci.asia/107-l</a> ). The datasets also include meteorological measurements taken at the same site, such as air temperature, relative humidity, wind speed, upwelling and downwelling longwave (4.5 to 42 µm) radiation. The above meteorological measurements were taken at a height of 2 - 3 m above the lake surface. Measurements at the site A are associated with the Kinneret Limnological Laboratory, Israel Oceanographic and Limnological Research (https://www.ocean.org.il ). Data format: xlsx file. The file includes water temperature (WT, oC), wind speed (WS, m/s), air temperature (Tair, oC), relative humidity (RH, %), upwelling longwave radiation (Upwelling LW, W/m2) and downwelling longwave radiation (Downwelling LW, W/m2).

Fig. 3 in A New Model Of Stink Bug Traps: Heated Trap For Capturing Halyomorpha Halys During The Autumn Dispersal Period

Fig. 3. Mean number of stink bugs observed in the trapping site (statistics: repeated measure ANOVA, Durbin-Conover pairwise comparison test, P ≤ 0.05)

Fig. 2 in A New Model Of Stink Bug Traps: Heated Trap For Capturing Halyomorpha Halys During The Autumn Dispersal Period

Fig. 2. Arrangement of the traps. The edges of slots were marked with white lines on the photo, because of the better visualization

openDec 2022View details →

Regional climate model simulations (CCLM 15km) of profiles for the MOSAiC period

The ship-based experiment MOSAiC 2019/2020 was carried out during a full year in the Arctic. The data set includes simulation data of profiles and derived data for the MOSAiC period (Oct. 2019-Sept.2020). The regional climate model CCLM was used in a forecast mode (nested in ERA5) for the whole Arctic with 15 km resolution and is run with different configurations of sea ice data. These include the standard sea ice concentration taken from passive microwave data (AMSR2) with around 6 km resolution, and sea ice concentration from Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data and MODIS sea ice lead data with 1 km resolution for the winter period (Nov. 2019-April 2020). Model output is available every 1h. In the vertical, the model extends up to 22 km with 60 vertical levels. On data below 10km are used. In addition to profiles, integrated water vapour and temperature for the lowest 2km were calculated. Values are grid-box averages at the ship position. Geostrophic wind was computed from the pressure gradient of the four surrounding grid points. Reference: Heinemann, G., Schefczyk, L., Willmes, S., Shupe, M., 2022: Evaluation of simulations of near-surface variables using the regional climate model CCLM for the MOSAiC winter period. Elem. Sci. Anth., 10 (1). DOI: 10.1525/elementa.2022.00033. Project:  Modelling the impact of sea-ice leads on the atmospheric boundary layer during MOSAiC (MISLAM) Funding: Federal Ministry of Education and Research (BMBF), grant 03F0887A

opencc-by-4.0Mar 2023View details →

Supplemental Data for Architector for high-throughput cross-periodic table 3D complex building

This repository contains all of the data presented in either the main text or the SI for the manuscript "Architector for high-throughput cross-periodic table 3D complex building".

opencc-by-4.0Aug 2022View details →

Differential algebra mappings of CR3BP periodic solutions

This dataset is made of twenty-three items containing polynomial mappings of families of periodic solutions of the circular restricted 3-body problem (CR3BP) [1], here the Earth-Moon and the Sun-(Earth-Moon) systems. Each file stores a list of differential algebra (DA) [2] maps representing these families with their associated domains of definition. The DA maps were computed using DA solving of two-point boundary value problem [3] and allow one to access initial conditions of solutions of the CR3BP by polynomial evaluation instead of heavy computations, provided the files containing the DA polynomials [4]. Each family is available with three different accuracies, also called tolerances. They are, with increasing accuracy, 1e-5, 1e-8, and 1e-10. The most accurate ones have higher loading and evaluation times and use more disk space but give better approximations of the families. The code DAHALOa_reader allows to read these files and can on GitHub: <a href="https://github.com/ThomasClb/DAHALOa_reader">ThomasClb/DAHALOa_reader (github.com)</a> References [1] V. Szebehely, ‘Theory of Orbit', Academic Press, 1967, pp. 7-41. DOI: <a href="https://doi.org/10.1016/B978-0-12-395732-0.50007-6">10.1016/B978-0-12-395732-0.50007-6</a> [2] R. Armellin, P. Di Lizia, et al., ’Asteroid close encounters characterization using differential algebra: the case of Apophis', Celestial Mechanics and Dynamical Astronomy, Vol. 107, No. 4, 2010, pp. 451-470. DOI: <a href="https://doi.org/10.1007/s10569-010-9283-5">10.1007/s10569-010-9283-5</a> [3] P. Di Lizia, R. Armellin, and M. Lavagna, 'Application of high order expansions of two-point boundary value problems to astrodynamics', Celestial Mechanics and Dynamical Astronomy, Vol. 102, No. 4, 2008, pp. 355-375. DOI: <a href="https://doi.org/10.1007/s10569-008-9170-5">10.1007/s10569-008-9170-5</a> [4] T. Caleb, M. Losacco, A. Fossà, R. Armellin, and S. Lizy-Destrez, 'Differential algebra methods applied to continuous  abacus generation and bifurcation detection: application to periodic families of the Earth–Moon system', Nonlinear dynamics, Vol. 111, 2023, pp. 9721-9740. DOI: <a href="https://doi.org/10.1007/s11071-023-08375-0">10.1007/s11071-023-08375-0</a>

opencc-by-4.0Dec 2021View details →

RUSSIAN ORTHODOX CHURCH DURING THE PERIOD 1917 REVOLUTION

Theoretical and practical aspects of the problem of state-church relations are now increasingly attracting public attention. In this regard, one of the most important tasks is to study the historical experience of the relationship between the state, the Church and believers, which allows us to use the experience of the past in the formation of a modern model of church-state relations. The study of the experience of self-determination of the Orthodox Church on the issue of relations with the state during the revolution of 1917 is currently of considerable scientific and practical interest.

opencc-by-4.0Jun 2023View details →

CESNET-MINER22-TS: Periodic Behavior Features of Cryptomining Communication

CESNET-MINER22-TS: Periodic Behavior Features of Cryptomining CommunicationDatasets were created for the paper: Enhancing DeCrypto: Finding Cryptocurrency Miners Based on Periodic Behavior -- Josef Koumar, Richard Plný, Tomáš Čejka -- which was published at The 19th International Conference on Network and Service Management (CNSM) 2023. Please cite usage of our datasets as:  <blockquote>J. Koumar, R. Plný and T. Čejka, "Enhancing DeCrypto: Finding Cryptocurrency Miners Based on Periodic Behavior," 2023 19th International Conference on Network and Service Management (CNSM), Niagara Falls, ON, Canada, 2023, pp. 1-7, doi: 10.23919/CNSM59352.2023.10327904.</blockquote> The files cesnet_miner22_design_with_FTS_proba.zip and cesnet_miner22_evaluation_with_FTS_proba.zip contain one .csv file with IP flows. The IP flows were taken from the CESNET-MINER22 dataset [1], which was created by monitoring national research and educational network CESNET2. Furthermore, we add two features ID_DEPENDENCY (string) and PERIODICITY_PROBA (double). ID_DEPENDENCY is an ID of a network dependency (see the article [2]) and the PERIODICITY_PROBA is the predicted probability by FTS analysis. The files from periodicity_features.zip contain periodic behavior features for Machine Learning. The files names are in format "{evaluation/design}.periodicity_features.{TIME_INTERVAL}.{SIG_SPACE}.{PER_LEVEL}.csv" and have the following format of columns:<ul><li>id_dependency -- Identification of a network dependency observed as a Flow time series (FTS).</li><li>label -- The labels ("Miner" or "Other") of periodic FTS.</li><li>packet_value -- Value of Clear periodic behavior of the metric packet.</li><li>packet_value_x -- Value of the interval's lower value of Sinusoidal periodic behavior of the metric packets.</li><li>packet_value_y -- Value of the interval's upper value of Sinusoidal periodic behavior of the metric packets.</li><li>packet_mean -- Mean value of the metric packet.</li><li>packet_std -- Standard deviation value of the metric packet.</li><li>packet_skewness -- Skewness value of the metric packet.</li><li>packet_kurtosis -- Kurtosis value of the metric packet.</li><li>bytes_value -- Value of Clear periodic behavior of the metric bytes.</li><li>bytes_value_x -- Value of the interval's lower value of Sinusoidal periodic behavior of the metric bytes.</li><li>bytes_value_y -- Value of the interval's upper value of Sinusoidal periodic behavior of the metric bytes.</li><li>bytes_mean -- Mean value of the metric bytes.</li><li>bytes_std -- Standard deviation value of the metric bytes.</li><li>bytes_skewness -- Skewness value of the metric bytes.</li><li>bytes_kurtosis -- Kurtosis value of the metric bytes.</li><li>duration_value -- Value of Clear periodic behavior of the metric duration.</li><li>duration_value_x -- Value of the interval's lower value of Sinusoidal periodic behavior of the metric duration.</li><li>duration_value_y -- Value of the interval's upper value of Sinusoidal periodic behavior of the metric duration.</li><li>duration_mean -- Mean value of the metric duration.</li><li>duration_std -- Standard deviation value of the metric duration.</li><li>duration_skewness -- Skewness value of the metric duration.</li><li>duration_kurtosis -- Kurtosis value of the metric duration.</li><li>difftimes_value -- Value of Clear periodic behavior of the metric difftimes.</li><li>difftimes_value_x -- Value of the interval's lower value of Sinusoidal periodic behavior of the metric difftimes.</li><li>difftimes_value_y -- Value of the interval's upper value of Sinusoidal periodic behavior of the metric difftimes.</li><li>difftimes_mean -- Mean value of the metric difftimes.</li><li>difftimes_std -- Standard deviation value of the metric difftimes.</li><li>difftimes_skewness -- Skewness value of the metric difftimes.</li><li>difftimes_kurtosis -- Kurtosis value of the metric difftimes.</li><li>max_power -- Represent the maximum power of the LS periodogram.</li><li>max_frequency -- Describe the frequency of the maximum power of the LS periodogram.</li><li>min_power -- Represent the minimum power of the LS periodogram.</li><li>min_frequency -- Describe the frequency of the minimum power of the LS periodogram.</li><li>spectral_energy -- Represents the total energy present at all frequencies in LS periodogram.</li><li>spectral_entropy -- The degree of randomness or disorder in the LS periodogram.</li><li>spectral_kurtosis -- Indicates a nonstationary or non-Gaussian behavior in the power spectrum.</li><li>spectral_skewness -- The measure of peakedness or flatness of power spectrum.</li><li>spectral_rolloff -- It is defined as frequency below 85% of the distribution power.</li><li>spectral_cetroid -- Indicates at which frequency the energy of a spectrum is centered upon.</li><li>spectral_spread -- It is the difference between the highest and lowest frequency in the power spectrum.</li><li>spectral_slope -- The slope of the power spectrum trend in a given frequency range.</li><li>spectral_crest -- Refers to the rate of shift of the sign of a wave, which is the rate of change from negative to positive or the reverse.</li><li>spectral_flux -- The rate of change of periodogram power with increasing frequency.</li><li>spectral_bandwidth -- Describes the difference between upper and lower frequencies at which spectral energy is half its maximum value.</li></ul> The files from time_series.zip contain FTS of used time interval. The file names are in format "{evaluation/design}.time_series.{TIME_INTERVAL}.csv" and have the following format of columns:<ul><li>ID_DEPENDENCY -- Identification of a network dependency observed as a FTS.</li><li>N_FLOWS -- Number of flows in time series, i.e., number of data points.</li><li>N_PACKETS -- Number of packets in time series, i.e., the sum of metric PACKETS.</li><li>N_BYTES -- Number of bytes in time series, i.e., the sum of metric PACKETS.</li><li>PACKETS -- The array containing the time series metric number of packets in the IP flow.</li><li>BYTES -- The array containing the time series metric number of bytes in the IP flow.</li><li>START_TIMES -- The array containing the time series time axis of the flows starts.</li><li>END_TIMES -- The array containing the time series time axis of the flows ends.</li><li>LABELS -- The array of labels ("Miner" of "Other") of each datapoint.</li></ul> [1] Richard Plný et al. CESNET-MINER22: Datasets of Cryptomining Communication. Zenodo, October 2022.[2] Koumar, Josef, and Tomáš Čejka. "Network traffic classification based on periodic behavior detection." 2022 18th International Conference on Network and Service Management (CNSM). IEEE, 2022.

opencc-by-4.0Aug 2022View details →

500-year periodic vegetation and monsoonal climate oscillations during the last deglaciation in East Asia

1 Core Xiaolongwan description 2 Age-depth models 3 Pollen counts 4 Pollen concentration 5 Pollen percentage 6 HHT Betula 7 HHT Boreal Conifer with Herb 8 HHT Artemisia 9 HHT Broadleaved Tree 10 Long chain n-alkanes δ13C