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| 2. |
- Grünig, Marc, et al.
(author)
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A harmonized database of European forest simulations under climate change
- 2024
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In: Data in Brief. - 2352-3409. ; 54
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Journal article (peer-reviewed)abstract
- Process-based forest models combine biological, physical, and chemical process understanding to simulate forest dynamics as an emergent property of the system. As such, they are valuable tools to investigate the effects of climate change on forest ecosystems. Specifically, they allow testing of hypotheses regarding long-term ecosystem dynamics and provide means to assess the impacts of climate scenarios on future forest development. As a consequence, numerous local-scale simulation studies have been conducted over the past decades to assess the impacts of climate change on forests. These studies apply the best available models tailored to local conditions, parameterized and evaluated by local experts. However, this treasure trove of knowledge on climate change responses remains underexplored to date, as a consistent and harmonized dataset of local model simulations is missing. Here, our objectives were (i) to compile existing local simulations on forest development under climate change in Europe in a common database, (ii) to harmonize them to a common suite of output variables, and (iii) to provide a standardized vector of auxiliary environmental variables for each simulated location to aid subsequent investigations. Our dataset of European stand- and landscape-level forest simulations contains over 1.1 million simulation runs representing 135 million simulation years for more than 13,000 unique locations spread across Europe. The data were harmonized to consistently describe forest development in terms of stand structure (dominant height), composition (dominant species, admixed species), and functioning (leaf area index). Auxiliary variables provided include consistent daily climate information (temperature, precipitation, radiation, vapor pressure deficit) as well as information on local site conditions (soil depth, soil physical properties, soil water holding capacity, plant-available nitrogen). The present dataset facilitates analyses across models and locations, with the aim to better harness the valuable information contained in local simulations for large-scale policy support, and for fostering a deeper understanding of the effects of climate change on forest ecosystems in Europe.
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| 3. |
- Mishra, Vaibhav, Doktorand, 1989-, et al.
(author)
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Data library of irradiated fuel salt and off-gas tank composition for a molten salt reactor concept produced with Serpent2 and SOURCES 4C codes
- 2024
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In: Data in Brief. - : Elsevier. - 2352-3409. ; 54
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Journal article (peer-reviewed)abstract
- This paper describes the methodology used to create a fuel data library comprising safeguards-relevant quantities that may be useful for verification of spent nuclear fuel (SNF) produced by simulating a concept Molten Salt Reactor (MSR). The Monte-Carlo particle transport code, Serpent2 and the calculation code SOURCES 4C were used to compile this fuel data library. The data library is based on the Compact Molten Salt Reactor (CMSR) concept being developed by Seaborg Technologies (based in Copenhagen, Denmark). The library includes data such as nuclide mass densities for a total of 1398 nuclides (in g/cm3), as well as total decay heat production (denoted by suffix the ‘TOT_DH’) in Watts, total gamma photon emission rates (denoted by the suffix ‘TOT_GS’) in photos per second, and the total activity (denoted by suffix ‘TOT_A’) in Becquerel. Lastly, the data also includes total neutron emission rates from 1) spontaneous fission (denoted by ‘SF’ and reported in neutrons per second per cm3), and 2) (ɑ, n) reactions (denoted by ‘AN’ and reported in neutrons per second per cm3) for the fuel salt. These quantities are reported for a range of burnup-initial enrichment-cooling time (or collectively known as, BIC) parameters. The resulting fuel data library is an extension of a previously published data library for the same reactor concept but with one significant change. The current library is based on a more realistic model of the CMSR involving movement of gaseous and volatile fission products (GFP and VFP) from the core via an Off-Gas System (OGS). The dataset is made available for public use in a compressed binary format as an HDF5 (or Hierarchical Data Format) file that can be parsed using data analysis tools such as Pandas.
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| 4. |
- Mishra, Vaibhav, Doktorand, 1989-, et al.
(author)
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Irradiated fuel salt data library for a molten salt reactor produced with Serpent2 and SOURCES 4C codes
- 2024
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In: Data in Brief. - : Elsevier. - 2352-3409. ; 52
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Journal article (peer-reviewed)abstract
- This paper describes the creation and description of a nuclear fuel isotopics dataset for irradiated fuel salt from a Molten Salt Reactor (MSR). The dataset has been created using the Monte-Carlo particle transport code, Serpent 2.1.32 (released February 24, 2021) and the calculation code SOURCES 4C (released October 09, 2002). The dataset comprises isotopic mass densities of 1362 isotopes (including fission products and major and minor actinides) and their corresponding contributions to decay heat, gamma activity, and spontaneous fission rates computed by Serpent 2.1.32 as well as overall neutron emission rates from spontaneous fission and (ɑ, n) reactions computed by SOURCES 4C. These quantities are computed for a model MSR core utilizing a full-core 3D model of the Seaborg Compact Molten Salt Reactor (CMSR) . The dataset spans a wide range of values of burnup (BU), initial enrichment (IE) and cooling time (CT) over which the above-mentioned quantities are reported. The structure of the dataset includes isotopic mass densities (in g/cm3), followed by isotope-wise contributions to decay heat (denoted by suffix ‘DH’ and reported in Watts), gamma photon emission rates (denoted by suffix ‘GS’ and reported photos per second), and spontaneous fission rates (denoted by suffix ‘SF’ and reported in fissions per second). In addition to these columns, the data also includes total neutron emission rates from 1) spontaneous fission (denoted by ‘SF’ and reported in neutrons per second per cm3), and 2) (ɑ, n) reactions (denoted by ‘AN’ and reported in neutrons per second per cm3). In total, the dataset has 310,575 rows of different combinations of fuel burnup, initial enrichment, and cooling time (BIC) values spanning the realistic possible range of these parameters. The dataset is made available for public use in a comma-separated value file that can be easily read using one of the numerous popular data analysis tools such as NumPy or Pandas.
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| 5. |
- Saha, Anwesha, et al.
(author)
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The first complete mitochondrial genome data of the Afghan pika Ochotona rufescens (Lagomorpha, Ochotonidae), near the type locality
- 2024
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In: Data in Brief. - 2352-3409. ; 53
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Journal article (peer-reviewed)abstract
- The Afghan pika Ochotona rufescens (Gray, 1842) is widely distributed across the mountains of Afghanistan, Iran, Pakistan, and southwestern Turkmenistan, most often at elevations between 2,000 and 3,000 m. Here we present, for the first time, the complete mitochondrial genomes of two specimens of the nominotypical subspecies Ochotona rufescens rufescens, de novo assembled from Illumina short reads of fragmented probe-enriched DNA. The lengths of the circular mitogenomes are 16,408 bp and 16,407 bp, respectively. Both mitogenomes contain 13 protein-coding genes (PCGs), two ribosomal RNAs (16S rRNA and 12S rRNA), 22 transfer RNA genes, and a control region. The gene NAD6 and the tRNA (Gln), tRNA (Ala), tRNA (Asn), tRNA (Cys), tRNA (Tyr), tRNA (Ser), tRNA (Glu), and tRNA (Pro) are encoded on the light strand while the rest are encoded on the heavy strand. The overall nucleotide composition was ∼30% for A, 25% for T, 15% for G, and 29% for C. The mitogenome data are available in the GenBank under the accession numbers ON859136 and ON859137.
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| 7. |
- van Ittersum, Martin
(author)
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Daily bias-corrected weather data and daily simulated growth data of maize, millet, sorghum, and wheat in the changing climate of sub-Saharan Africa
- 2024
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In: Data in Brief. - 2352-3409. ; 54
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Journal article (peer-reviewed)abstract
- Crop models are the primary means by which agricultural scientists assess climate change impacts on crop production. Site-based and high-quality weather and climate data is essential for agronomically and physiologically sound crop simulations under historical and future climate scenarios. Here, we describe a bias-corrected dataset of daily agro-meteorological data for 109 reference weather stations distributed across key production areas of maize, millet, sorghum, and wheat in ten sub-Saharan African countries. The dataset leverages extensive ground observations from the Global Yield Gap Atlas (GYGA), an existing climate change projections dataset from the Inter-Sectoral Model Intercomparison Project (ISIMIP), and a calibrated crop simulation model (the WOrld FOod Studies -WOFOST). The weather data were bias-corrected using the delta method, which is widely used in climate change impact studies. The bias -corrected dataset encompasses daily values of maximum and minimum temperature, precipitation rate, and global radiation obtained from five models participating in the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), as well as simulated daily growth variables for the four crops. The data covers three periods: historical (1995-2014), 2030 (2020- 2039), and 2050 (2040-2059). The simulation of daily growth dynamics for maize, millet, sorghum, and wheat growth was performed using the daily weather data and the WOFOST crop model, under potential and water -limited potential conditions. The crop simulation outputs were evaluated using national agronomic expertise. The presented datasets, including the weather dataset and daily simulated crop growth outputs, hold substantial potential for further use in the investigation of future climate change impacts in sub-Saharan Africa. The daily weather data can be used as an input into other modelling frameworks for crops or other sectors (e.g., hydrology). The weather and crop growth data can provide key insights about agro-meteorological conditions and water -limited crop output to inform adaptation priorities and benchmark (gridded) crop simulations. Finally, the weather and simulated growth data can also be used for training machine learning techniques for extrapolation purposes. (c) 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY -NC -ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
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| 8. |
- Xiao, Yinshuang, et al.
(author)
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Survey data on customer two-stage decision-making process in household vacuum cleaner market
- 2024
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In: Data in Brief. - 2352-3409. ; 54
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Journal article (peer-reviewed)abstract
- This paper presents the data collection method and introduces the dataset about consumers’ consider-then-choose behaviors in the household vacuum cleaner market. First, we designed a questionnaire that collected participants’ consideration and choice data, social network data, demographic information, and preferences for product features. In addition, we obtained data on vacuum cleaner product features through web scraping from online shopping websites. After data cleaning and processing, the resulting dataset enables investigation into customer preferences in two stages, namely the consideration and choice stages and the impact of social influence on the two-stage decision-making process. This dataset is unique as it is the first of its kind to collect both customers’ revealed preferences in a two-stage decision-making process and their ego social networks. This enables the modeling of customer preferences while accounting for social influence. The published survey questionnaire can be used as a template to collect data on other products in support of customer preferences modeling and the design for market systems.
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| 9. |
- Yasin, A., et al.
(author)
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Behind the Bait : Delving into PhishTank's hidden data
- 2024
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In: Data in Brief. - : Elsevier Inc.. - 2352-3409. ; 52
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Journal article (peer-reviewed)abstract
- Phishing constitutes a form of social engineering that aims to deceive individuals through email communication. Extensive prior research has underscored phishing as one of the most commonly employed attack vectors for infiltrating organizational networks. A prevalent method involves misleading the target by employing phishing URLs concealed through hyperlink strategies. PhishTank, a website employing the concept of crowd-sourcing, aggregates phishing URLs and subsequently verifies their authenticity. In the course of this study, we leveraged a Python script to extract data from the PhishTank website, amassing a comprehensive dataset comprising over 190,0000 phishing URLs. This dataset is a valuable resource that can be harnessed by both researchers and practitioners for enhancing phish- ing filters, fortifying firewalls, security education, and refining training and testing models, among other applications.
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| 10. |
- Yasin, Affan, et al.
(author)
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Python Data Odyssey : Mining User feedback from Google Play store
- 2024
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In: Data in Brief. - : Elsevier. - 2352-3409. ; 54
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Journal article (peer-reviewed)abstract
- ContextThe Google Play Store is widely recognized as one of the largest platforms for downloading applications, both free and paid1. On a daily basis, millions of users avail themselves of this marketplace, sharing their thoughts through various means such as star ratings, user comments, suggestions, and feedback. These insights, in the form of comments and feedback, constitute a valuable resource for organizations, competitors, and emerging companies seeking to expand their market presence. These comments provide insights into app deficiencies, suggestions for new features, identified issues, and potential enhancements. Unlocking the potential of this repository of suggestions holds significant value.ObjectiveThis study sought to gather and analyze user reviews from the Google Play store for leading game apps. The primary aim was to construct a dataset for subsequent analysis utilizing requirements engineering, machine learning, and competitive assessment.MethodologyThe authors employed a Python-based web scraping method to extract a comprehensive set of over 429,000+ reviews from the Google Play pages of selected apps. The scraped data encompassed reviewer names (removed due to privacy), ratings, and the textual content of the reviews.ResultsThe outcome was a dataset comprising the extracted user reviews, ratings, and associated metadata. A total of 429,000+ reviews were acquired through the scraping process for popular apps like Subway Surfers, Candy Crush Saga, PUBG Mobile, among others. This dataset not only serves as a valuable educational resource for instructors, aiding in the training of students in data analysis, but also offers practitioners the opportunity for in-depth examination and insights (in the past data of top apps).
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