Find research datasets worth reusing

NETTAG+ Data set on adsorption of inorganic (Cu and Pb) and organic (PAHs) pollutants in fishing nets

This data set includes the raw data associated with the article in Marine Pollution Bulletin "Potential of fishing nets for adsorption of inorganic (Cu and Pb) and organic (PAHs) pollutants"

StopWords dataset: Integration of a set of stopwords in English and Portuguese - rev. 1

StopWords dataset: Integration of a set of stopwords in English and Portuguese - rev. 1 ================================ StopWords dataset - rev. 1 (two MS-Excel files) ------------- StopWords Integrated Basic integration of a set of stopwords (English and Portuguese) for use in Text Mining tasks. File name 1: StopWords_Integrated_Favaretto.xlsx Tab 1 of MS-Excel: pt_accent (215 words) Column Label: stopwords_pt Tab 2 of MS-Excel: pt_noaccent (208 words) Column Label: stopwords_pt_na Tab 3 of MS-Excel: en (213 words) Column Label: stopwords_en ------------- StopWords Extended Extension of a set of stopwords (English and Portuguese) for use in Text Mining tasks. File name 2: StopWords_Extended_Favaretto.xlsx Tab 1 of MS-Excel: pt_extend (614 words) Column Label: stopwords_pt_extend Tab 2 of MS-Excel: en_extended (483 words) Column Label: stopwords_en_extend ================================ Warning: Some words in this set of stopwords may even be misspelled intentionally, as they may occur in practice in texts that are not written correctly. Aviso: Algumas palavras deste conjunto de stopwords podem até mesmo ter grafia errada de forma intencional, pois podem ocorrer na prática em textos não escritos corretamente. ================================ Source: elaborated by Prof. Dr. José Eduardo Ricciardi Favaretto based on a mix of several different sources https://orcid.org/0000-0002-0143-0809 https://lattes.cnpq.br/3790103269421610 https://linkedin.com/in/favaretto ================================

Hydrographic gridded data set for the South Brazil Bight and Southern Brazilian Shelf

This dataset includes climatological and seasonal maps, spanning data from 1972 to 2024, across 8 different depth levels: 5, 10, 25, 50, 100, 200, 500, and 1000 dBar, with a spatial resolution of 10 km. The maps were generated using the griddata function with triangulation-based natural neighbor interpolation. The variables included in this dataset are conservative temperature (°C), absolute salinity (g kg⁻¹), neutral density (kg m⁻³), dissolved oxygen (mL L⁻¹), total alkalinity (µmol kg⁻¹), total dissolved inorganic carbon (µmol kg⁻¹), pH (total scale), partial pressure of carbon dioxide (µatm), nitrate (µmol kg⁻¹), and phosphate (µmol kg⁻¹). The name description of each variable is provided in the readme_DatasetATLAS.txt. The dataset can be directly accessed using Ocean Data View (ODV) software.

opencc-by-4.0Sep 2024View details →

Sets of period sets for words of length n.

We consider finite words of length n. Each word has a set of periods, but many words can have the same set of periods. For a definition of a period of a word, see [1]. A set of periods is a subset of the set {0, 1, ..., n-1}, but not all subsets of {0, 1, ..., n-1} are period sets. The set denoted Gamma(n) contains all possible period sets corresponding to at least one word of length n. For a definition of Gamma(n), see references [1] or [2]. For more details see reference [4]. The series of text files provide the list of period set, one per line, for Gamma(n), for n = 1, 2, ..., 100. Each line contains a list of integers sorted by increasing value: this list constitutes one period set. The  separator symbol is a space. Hence, the number of non-empty lines in a file gives the cardinality of Gamma(n). The period set are sorted by their basic period.  For a definition of the notion of basic period, see [1] or [2]. The files for n=61, ..., 100, where computed with the incremental algorithm described in [5]. The sequence of the cardinalities of the set Gamma(n), is also called, the Number of distinct autocorrelations of binary words of length n, and corresponds to the sequence A005434 in the Encyclopedia of Integer Sequences (EOIS) link [3]. The files have generic name formatted as follows: gamma.n.bps where n is the word length, for n = 1, 2, ..., 100. References: 1. Eric Rivals, Sven Rahmann.    Combinatorics of Periods in Strings.    Proc. 28th International Colloquium on Automata, Languages, and Programming, Lecture Notes in Computer Science vol. 2076, p. 615-26., P. Orejas, P. G. Spirakis, J. van Leuween editors, Springer Verlag, Berlin, 2001.    doi: <a title="Publication 1" href="https://doi.org/10.1007/3-540-48224-5_51" target="_blank" rel="noopener">https://doi.org/10.1007/3-540-48224-5_51</a> 2. Eric Rivals, Sven Rahmann.    Combinatorics of Periods in Strings.    Journal of Combinatorial Theory - Series A, 104(1), p. 95-113, October 2003    doi: <a title="Publication 2" href="https://doi.org/10.1016/s0097-3165(03)00123-7" target="_blank" rel="noopener">https://doi.org/10.1016/s0097-3165(03)00123-7</a> 3. Entry A005434 from The On-Line Encyclopedia of Integer Sequences.    URL: <a title="Sequence entry A005434" href="https://oeis.org/A005434" target="_blank" rel="noopener">https://oeis.org/A005434</a> 4. Autocorrelation of Strings. A comment on entries A005434 and A045690 of the Encyclopedia of Integer Sequences.    URL: <a title="Introduction to period sets (webpage)" href="https://www.lirmm.fr/~rivals/RESEARCH/PERIOD/" target="_blank" rel="noopener">https://www.lirmm.fr/~rivals/RESEARCH/PERIOD/</a>  5. Eric Rivals. Incremental computation of the set of period sets. arXiv:2410.12077,  2024. <a title="Publication 5" href="https://doi.org/10.48550/arXiv.2410.12077" target="_blank" rel="noopener">https://doi.org/10.48550/arXiv.2410.12077</a>

opencc-zeroNov 2024View details →

Data set-PID2020-118478RB-I00

Datos de reconstrucción de tomografías junto con las porosidades detectadas y sus características.

Data set for letter "Floquet-Driven Crossover from Density-Assisted Tunneling to Enhanced Pair Tunneling"

The files contain the data depicted in the figures of the article "Floquet-Driven Crossover from Density-Assisted Tunneling to Enhanced Pair Tunneling", arXiv 2404.08482. The format of the data and to which figure it corresponds is described in the file "README.txt".

opencc-by-4.0Jan 2024View details →

A data set from an extensive experimental benchmark study of the Hell Bridge Test Arena subject to imposed damage

A data set from an extensive experimental benchmark study of the Hell Bridge Test Arena (HBTA), a full-scale steel bridge subject to imposed damage, has been established. The data set includes organized dynamic response and load measurement data of the bridge under different structural state conditions, where the structural state conditions range from an undamaged (reference) state to known damage states. Furthermore, the data set includes acceleration and strain data from the response monitoring and acceleration data from the load monitoring, where a modal vibration shaker is used as an excitation source. The data is collected in one h5-file (hierarchical data format version 5) with a sampling rate of 100 Hz. Signal processing and resampling of the data has been performed according to the description provided in the references below. The data set is now published in this open-access data repository and can be accessed and downloaded freely. As such, the data set provides an important benchmark to the scientific community within bridge damage detection and SHM.

opencc-by-4.0Jan 2024View details →

Data set associated to the manuscript entitled Carbon emissions from inland waters may be underestimated: evidence from European river networks fragmented by drying by López-Rojo et. al

CO2 and CH4 emissions and several associated environmental variables  were taken in 6 European drying river networks, in 20 river reaches per river network. The field work was carried across 3 sampling campaigns in 2021, coinciding with 3 hydrological seasons (pre-dry, dry and post-rewetting) to encompass most of the hydrological variability. Each time, measures were taken in the habitats available (flowing water, dry riverbeds, isolated pools).

opencc-by-4.0Jul 2021View details →

A stakeholder-centered determination of High-Value Data sets: the use-case of Latvia

The data in this dataset were collected in the result of the survey of Latvian society (2021) aimed at identifying high-value data set for Latvia, i.e. data sets that, in the view of Latvian society, could create the value for the Latvian economy and society. The survey is created for both individuals and businesses. It being made public both to act as supplementary data for "Towards enrichment of the open government data: a stakeholder-centered determination of High-Value Data sets for Latvia" paper (author: Anastasija Nikiforova, University of Latvia) and in order for other researchers to use these data in their own work. The survey was distributed among Latvian citizens and organisations. The structure of the survey is available in the supplementary file available (see Survey_HighValueDataSets.odt) ***Description of the data in this data set: structure of the survey and pre-defined answers (if any)***     1. Have you ever used open (government) data? - {(1) yes, once; (2) yes, there has been a little experience; (3) yes, continuously, (4) no, it wasn’t needed for me; (5) no, have tried but  has failed}     2. How would you assess the value of open govenment data that are currently available for your personal use or your business? - 5-point Likert scale, where 1 – any to 5 – very high     3. If you ever used the open (government) data,  what was the purpose of using them? - {(1) Have not had to use; (2) to identify the situation for an object or ab event (e.g. Covid-19 current state); (3) data-driven decision-making; (4) for the enrichment of my data, i.e. by supplementing them; (5) for better understanding of decisions of the government; (6) awareness of governments’ actions (increasing transparency); (7) forecasting (e.g. trendings etc.); (8) for developing data-driven solutions that use only the open data; (9) for developing data-driven solutions, using open data as a supplement to existing data; (10) for training and education purposes; (11) for entertainment; (12) other (open-ended question)     4. What category(ies) of “high value datasets” is, in you opinion, able to create added value for society or  the economy? {(1)Geospatial data;  (2) Earth observation and environment; (3) Meteorological; (4) Statistics; (5) Companies and company ownership; (6) Mobility}     5. To what extent do you think the current data catalogue of Latvia’s Open data portal corresponds to the needs of data users/ consumers? - 10-point Likert scale,  where 1 – no data are useful, but 10 – fully correspond, i.e. all potentially valuable datasets are available     6. Which of the current data categories in Latvia’s open data portals, in you opinion, most corresponds to the “high value dataset”? - {(1)Foreign affairs;  (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}     7. Which of them form your TOP-3? - {(1)Foreign affairs;  (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}     8. How would you assess the value of the following data categories?     8.1. sensor data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable     8.2. real-time data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable     8.3. geospatial data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable     9. What would be these datasets? I.e. what (sub)topic could these data be associated with? - open-ended question     10. Which of the data sets currently available could be valauble and useful for society and businesses? - open-ended question     11. Which of the data sets currently NOT available in Latvia’s open data portal could, in your opinion, be valauble and useful for society and businesses? - open-ended question     12. How did you define them? - {(1)Subjective opinion; (2) experience with data; (3) filtering out the most popular datasets, i.e. basing the on public opinion; (4) other (open-ended question)}     13. How high could be the value of these data sets value for you or your business? - 5-point Likert scale, where 1 – not valuable, 5 – highly valuable     14. Do you represent any company/ organization (are you working anywhere)? (if “yes”, please, fill out the survey twice, i.e. as an individual user AND a company  representative) - {yes; no; I am an individual data user; other (open-ended)}     15. What industry/ sector does your company/ organization belong to? (if you do not work at the moment, please, choose the last option) - {Information and communication services; Financial and ansurance activities; Accommodation and catering services; Education; Real estate operations; Wholesale and retail trade; repair of motor vehicles and motorcycles; transport and storage; construction; water supply; waste water; waste management and recovery; electricity, gas supple, heating and air conditioning; manufacturing industry; mining and quarrying; agriculture, forestry and fisheries professional, scientific and technical services; operation of administrative and service services; public administration and defence; compulsory social insurance; health and social care; art, entertainment and recreation; activities of households as employers;; CSO/NGO; Iam not a representative of any company     16. To which category does your company/ organization belong to in terms of its size? - {small; medium; large; self-employeed; I am not a representative of any company}     17. What is the age group that  you belong to? (if you are an individual user, not a company representative) - {11..15, 16..20, 21..25, 26..30, 31..35, 36..40, 41..45, 46+, “do not want to reveal”}     18. Please, indicate your education or a scientific degree that corresponds most to you? (if you are an individual user, not a company representative) - {master degree; bachelor’s degree; Dr. and/ or PhD; student (bachelor level); student (master level); doctoral candidate; pupil; do not want to reveal these data} ***Format of the file*** .xls, .csv (for the first spreadsheet only), .odt ***Licenses or restrictions*** CC-BY

opencc-by-4.0Aug 2021View details →

Data set for the integrated Climate, Land, Energy and Water systems modelling exercise RCLEWs in OSeMOSYS

This dataset refers to the modelling exercise (version01_210616RCLEWs). The dataset contains the OSeMOSYS code used to run the modelling exercise, the model input data, the scenarios model data files, and the results. The code for the results visualization is available at https://github.com/KTH-dESA/teaching-CLEWs_visualization. This is an update of version 01_210827 available at: https://doi.org/10.5281/zenodo.5293834

opencc-by-sa-4.0Jun 2021View details →

WaveFake: A data set to facilitate audio DeepFake detection

The main purpose of this data set is to facilitate research into audio DeepFakes. We hope that this work helps in finding new detection methods to prevent such attempts. These generated media files have been increasingly used to commit <a href="https://www.vice.com/en/article/pkyqvb/deepfake-audio-impersonating-ceo-fraud-attempt">impersonation attempts</a> or <a href="https://www.wired.com/story/telegram-still-hasnt-removed-an-ai-bot-thats-abusing-women/">online harassment</a>. You can find the accompanying code repository on <a href="https://github.com/RUB-SysSec/WaveFake">GitHub</a>. The data set consists of  104,885 generated audio clips (16-bit PCM wav).  We examine multiple networks trained on two reference data sets. First, the <a href="https://keithito.com/LJ-Speech-Dataset/">LJSpeech</a> data set consisting of 13,100 short audio clips (on average 6 seconds each; roughly 24 hours total) read by a female speaker. It features passages from 7 non-fiction books and the audio was recorded on a MacBook Pro microphone. Second, we include samples based on the <a href="https://sites.google.com/site/shinnosuketakamichi/publication/jsut">JSUT</a> data set, specifically, basic5000 corpus. This corpus consists of 5,000 sentences covering all basic kanji of the Japanese language (4.8 seconds on average; roughly 6.7 hours total). The recordings were performed by a female native Japanese speaker in an anechoic room. Finally, we include samples from a full text-to-speech pipeline (16,283 phrases; 3.8s on average; roughly 17.5 hours total). Thus, our data set consists of approximately 175 hours of generated audio files in total. Note that we do not redistribute the reference data. We included a range of architectures in our data set: <ul> <li><a href="https://arxiv.org/abs/1910.06711">MelGAN</a></li> <li><a href="https://arxiv.org/abs/1910.11480">Parallel WaveGAN</a></li> <li><a href="https://arxiv.org/abs/2005.05106">Multi-Band MelGAN</a></li> <li><a href="http://arxiv.org/abs/2005.05106">Full-Band MelGAN</a></li> <li><a href="https://arxiv.org/abs/2010.05646">HiFi-GAN</a></li> <li><a href="https://arxiv.org/abs/1811.00002">WaveGlow</a></li> </ul> Additionally, we examined a bigger version of MelGAN and include samples from a full TTS-pipeline consisting of a conformer and parallel WaveGAN model. Collection Process For WaveGlow, we utilize the <a href="https://github.com/NVIDIA/waveglow">official implementation</a> (commit 8afb643) in conjunction with the official pre-trained network on <a href="https://pytorch.org/hub/nvidia_deeplearningexamples_waveglow/">PyTorch Hub</a>. We use a popular implementation available on <a href="https://github.com/kan-bayashi/ParallelWaveGAN">GitHub</a> (commit 12c677e) for the remaining networks. The repository also offers pre-trained models. We used the pre-trained networks to generate samples that are similar to their respective training distributions, <a href="https://keithito.com/LJ-Speech-Dataset/">LJ Speech</a> and <a href="https://sites.google.com/site/shinnosuketakamichi/publication/jsut">JSUT</a>. When sampling the data set, we first extract Mel spectrograms from the original audio files, using the pre-processing scripts of the corresponding repositories. We then feed these Mel spectrograms to the respective models to obtain the data set. For sampling the full TTS results, we use the <a href="https://github.com/espnet/espnet">ESPnet</a> project. To make sure the generated phrases do not overlap with the training set, we downloaded the <a href="https://commonvoice.mozilla.org/en/datasets">common voices data set</a> and extracted 16.285 phrases from it. This data set is licensed with a CC-BY-SA 4.0 license. This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -- EXC-2092 CaSa -- 390781972.

Data set of 'Adiabatic temperature profile in the mantle, revised'

P-V-T data of the four major mantle minerals, olivine, wadselyite, ringwoodite, and bridgmanite The original data are as follows: Olivine: https://doi.org/10.1016/j.pepi.2008.08.002 Wadsleyite: https://doi.org/10.1029/2009GL038107 Ringwoodite: https://doi.org/10.1029/2004JB003094 Bridgmanite; https://doi.org/10.1029/2009GL039318 https://doi.org/10.1029/2011JB008988 The temperatures were recalculated using https://doi.org/10.1016/j.pepi.2019.106348 The pressures were recalculated using https://doi.org/10.1029/2011JB008988

GeoERA RESOURCE H3O-PLUS data set which contains hydraulic properties of prime aquifers and aquitards in the Dutch-Flemish-German cross-border area

Dataset which contains information about hydraulic properties of harmonized hydrogeological units in the Dutch-Flemish-German cross-border region which was compiled in the GeoERA RESOURCE project under WP3 H3O-PLUS. The harmonization of the 3D geometry of the cross-border hydrogeological units in the H3O projects constituted a major step towards a common hydrogeological dataset of the Roer Valley Graben and thus the harmonization of groundwater flow models. The database that was compiled provides the characterization of these hydrogeological units with respect to their hydraulic properties, primarily their hydraulic conductivity. The associated report and appendices describe the database of hydraulic properties of aquifers and aquitards based on common criteria. Attention is also given to the characterization of hydraulic properties of faults.

Numerical Data Set Belonging to: 'Numerical Study of Phase-Change Phenomena: A Conservative Linearized Enthalpy Approach'

This is the numerical data set belonging to the Nureth conference paper entitled:  'Numerical Study of Phase-Change Phenomena: A Conservative Linearized Enthalpy Approach'.  The files 'Stefan_singlePhase_Tfield.dat' and 'Stefan_singlePhase_interface.dat' represent the raw data belonging to figure 1 in the paper and contain the solution to the one-phase Stefan problem for the temperature field and interface position (section 3.1 in the paper). The files 'Stefan_singlePhase_error.dat' and 'Stefan_twoPhase_error.dat'  represent the raw data belonging to figure 2 in the paper and contain the L2 norm of the relative difference between the numerical and analytical solution for the single and two phase Stefan problem respectively.  The files 'Gau_1140s_lf_50x50_3D', 'Gau_1140s_lf_100x100_3D', 'Gau_1140s_lf_200x200_3D' feature the raw OpenFOAM(v7) data containing the numerical solution to the liquid fraction of the Gallium melting in a rectangular enclosure problem (Gau, 1986) at 1140s of simulation time. These data were used for the mesh convergence study (figure 3, section 3.2).  The files 'Gau_120s_U_200x200_3D', 'Gau_360s_U_200x200_3D', 'Gau_750s_U_200x200_3D', 'Gau_1140s_U_200x200_3D' feature the raw OpenFOAM(v7) data containing the 3-dimensional numerical solution to the velocity of the Gallium melting in a rectangular enclosure problem (Gau, 1986) at respectively 120s, 360s, 750s and 1140s of simulation time. These data underly the velocity colours shown in figure 4 and figure 6 (section 3.2). Likewise, the  files 'Gau_120s_U_200x200_2D' and 'Gau_360s_U_200x200_2D' feature the raw OpenFOAM(v7) data containing the 2-dimensional numerical solution to the velocity of the Gallium melting in a rectangular enclosure problem. These data underly the velocity colours shown in figure 5 (section 3.2).

GeoERA RESOURCE CHAKA data set which contains time series of precipitation and discharge of springs in the CHAKA pilot areas (D5.5)

Dataset which contains time series of precipitation and discharge of springs in the pilot areas of the CHAKA work package of the GeoERA RESOURCE project. The file contains precipitation and spring discharge data of 16 pilot areas in the Karst & Chalk work package. A description of the application of the dataset for the characterisation of the typology of karst systems in given in the D5.3 deliverable of GeoERA RESOURCE of which the pdf is provided. Further information about the CHAKA results can be assessed though the webservices of the European Geological Data Infrastructure (EGDI).

Data to reproduce the results presented in Lake et al. 2021. Journal of Soils and Sediments, https://doi.org/10.1007/s11368-021-03107-6 ("High frequency un-mixing of soil samples using a submerged spectrophotometer in a laboratory setting – implications for sediment fingerprinting")

This repository contains data on (1) the absorbance data and (2) the measured concentrations, to reproduce computational results as presented in: "High frequency un-mixing of soil samples using a submerged spectrophotometer in a laboratory setting – implications for sediment fingerprinting".    1. Absorbance data (200-730 nm wavelengths):     * Average absorbance compensated for measured concentrations (average absorbance value per concentration)     * Average absorbance compensated for theoretical concentrations (average absorbance value per concentration)     * Average raw absorbance measured (average absorbance value per concentration)     * Raw absorbance measured (all absorbance values for all concentrations)     Data in all 3 files is indicated per soil sample / mixture, with corresponding fraction(s) of soil sample(s) and corresponding (theoretical) input concentration.      2. Measured concentration data:     * Measured concentration (average concentrations, tested for all experiments and for all theoretical input concentrations)

Wflow SBM streamflow estimates for CAMELS data set

The dataset contains 3 simulated timeseries at the basin outlet for the CAMELS dataset created with the wflow_sbm model at various spatial resolutions (3km, 1km, 200m). The data set includes the calculated objective functions NSE, KGE 2009, KGE 2012, and KGE NP.

opencc-by-4.0Dec 2021View details →

Data set of article entitled: "Impact of the interfacial Dzyaloshinskii-Moriya interaction on the band structure of one-dimensional artificial magnonic crystals: A micromagnetic study"

Data set of the immagies showed in figure 3 of the article entiteled "Impact of the interfacial Dzyaloshinskii-Moriya interaction on the band structure of one-dimensional artificial magnonic crystals: A micromagnetic study". All files contain the matrix of the dispersion relations of the two analysed Magnonic Crystals: the SAMPLE A and the SAMPLE B for different value of the interfacial Dzyaloshinskii-Moriya interaction (constant D). The first row is the set of values of k-vector, while the first column is the set of value of the frequencies. The other elements of the matrix are the values of the pixel related to the first row and first column. These elements are been obtanied by the Fast Fourier Transform in time and space of the micromagnetic simulations .

opencc-by-4.0Dec 2015View details →

Basis sets for the 1s2 ground states of two-electron rare gas ions

This file contains the exponents of Gaussian basis functions optimized in the Dirac-Coulomb approximation for the two-electron ions of the rare gases, He, Ne, Ar, Kr, Rn, and Og. The basis sets are of double-zeta (2z) to septuple-zeta (7z) quality, and include correlating sets:1p (2z), 2p1d (3z), 3p2d1f (4z), 4p3d2f1g (5g), 5p4d3f2g1h (6z), and 6p5d4f3g2h1i (7z). The basis sets are included with the DIRAC program.