Find research datasets worth reusing

opencc-by-4.0Dec 2017View details →

FIGURE 1 in Paleontologia Electronica is still number one for new open access fossil species

FIGURE 1. Eekaulostomus cuevasae, an extinct armored trumpetfish, and the top open access fossil taxon of 2017 (Farke, 2017). Reproduced from Cantalice and Alvarado-Ortega (2016). 2. Illustration of trumpetfish from Brian Engh, http://dontmesswithdinosaurs.com.

opencc-by-4.0Aug 2024View details →

The mOTUs online database provides web-accessible genomic context to taxonomic profiling of microbial communities - Supplementary Tables

Supplementary Table 1: A map between each of the genomes in mOTUs-db (3’747’151), the associated study and its metagenomic sample (in case of MAGs). Columns: <code>    GENOME             → Unique mOTUs-db name of the genome</code> <code>    STUDY              → Unique mOTUs-db name of the study</code> <code>    IS_MAG             → True if genome is a MAG, otherwise False </code> <code>    METAGENOMIC_SAMPLE → Unique name of the metagenomic sample or NA in case of non-MAG genome</code> Example: <code>    GENOME                                     STUDY        IS_MAG    METAGENOMIC_SAMPLE</code> <code>    ---------------------------------------------------------------------------------------------</code> <code>    ACIN21-1_SAMN05421555_MAG_00000001         ACIN21-1     True      ACIN21-1_SAMN05421555_METAG</code> <code>    RSGB23-1_GCA-006096615-V1_GENO_10000001    RSGB23-1     False     NA</code> Supplementary Table 2: A map between all non-MAG genomes (919’090) and their source (e.g. Refseq or JGI). Columns: <code>    GENOME             → Unique mOTUs-db name of the genome</code> <code>    SOURCE_SAMPLE_LINK → Link to the original location of this genome</code> Example: <code>    #GENOME                                     SOURCE_SAMPLE_LINK</code> <code>    --------------------------------------------------------------------------------------------------------</code> <code>    JGIG23-1_GA0055041_GENO_10000001            https://gold.jgi.doe.gov/analysis_project?id=Ga0055041</code> <code>    RSGB23-1_GCA-006717865-V1_GENO_10000001     https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_006717865.1</code> Supplementary Table 3: A list of all metagenomic studies processed for the mOTUs-db, their number of samples, the number of reconstructed MAGs and the associated publication. Columns: <code>    STUDY       --> Unique mOTUs-db study identifier</code> <code>    BIOPROJECT  --> Public identifier (NCBI/JGI) of metagenomic sequencing project</code> <code>    SAMPLES     --> Number of metagenomic samples</code> <code>    MAGs        --> Number of reconstructed MAGs</code> <code>    PUBLICATION --> Link to publication</code> Example: <code>    STUDY        BIOPROJECT    SAMPLES    MAGs     PUBLICATION</code> <code>    -------------------------------------------------------------------------------------------------</code> <code>    ACIN21-1     PRJEB44456    58         1,110    https://www.nature.com/articles/s42003-021-02112-2</code> Supplementary Table 4: Mapping between mOTUs-db sample identifier, the associated biosample and the environment. Columns: <code>    SAMPLE             --> Unique mOTUS-db sample identifier</code> <code>    BIOSAMPLE          --> Public identifier (NCBI/JGI) of metagenomic sample</code> <code>    STUDY              --> Unique mOTUs-db study identifier</code> <code>    ENVIRONMENT        --> Environment of metagenomic sample</code> <code>    SOURCE_SAMPLE_LINK --> Link to the original location of this sample</code> Example: <code>    #SAMPLE                      BIOSAMPLE     STUDY    ENVIRONMENT  SOURCE_SAMPLE_LINK</code> <code>    ---------------------------------------------------------------------------------------------------------------------</code> <code>    ACIN21-1_SAMN05421555_METAG  SAMN05421555  ACIN21-1 marine       https://www.ncbi.nlm.nih.gov/biosample/SAMN05421555/</code> Supplementary Table 5: A list of environments covered in the mOTUs-db mapped to the respective NCBI taxonomy (if possible) Columns: <code>    TERM             --> Unique environment name</code> <code>    NCBI TAXONOMY ID --> Link to the NCBI taxonomy</code> Example: <code>    TERM                          NCBI TAXONOMY ID</code> <code>    ----------------------------------------------</code> <code>    activated sludge metagenome   NCBI:txid942017</code> <code>    air metagenome                NCBI:txid655179</code>

opencc-by-4.0Aug 2024View details →

Publications of professors and teachers of the Ukrainian Free University (1921 - 1925), placed in the Open Access

The publications of scientists of the Ukrainian Free University for the period from 1921 to 1925 inclusive, placed in the Open Access with the indication of electronic libraries in which they were placed, are presented.

opencc-by-sa-4.0Sep 2024View details →

Data Access Made Easy: flexible, on the fly data standardization and processing (for research automatic weather stations)

<pre>Automatic Weather Stations (AWS) deployed in the context of research projects provide very valuable data thanks to the flexibility they offer in term of measured meteorological parameters, choice of sensors and quick deployment and redeployment. However this flexibility is a challenge in terms of metadata and data management. Traditional approaches based on networks of standard stations can not accommodate these needs and often no tools are available to manage these research AWS, leading to wasted data periods because of difficult data reuse, low reactivity in identifying potential measurement problems, and lack of metadata to document what happened. The Data Access Made Easy (DAME) effort is our answer to these challenges. At its core, it relies on the mature and flexible open source MeteoIO meteorological pre-processing library. It was originally developed as a flexible data processing engine for the needs of numerical models consuming meteorological data and further developed as a data standardization engine for the Global Cryosphere Watch (GCW) of the World Meteorological Organization (WMO). For each AWS, a single configuration file describes how to read and parse the data, defines a mapping between the available fields and a set of standardized names and provides relevant Attribute Conventions Dataset Discovery (ACDD) metadata fields, if necessary on a per input file basis. Low level data editing is also available, such as excluding a given sensor, swapping sensors or merging data from another AWS, for any given time period. Moreover an arbitrary number of filters can be applied on each meteorological parameter, restricted to specific time periods if required. This allows to describe the whole history of an AWS within a single configuration file and to deliver a single, consistent, standardized output file possibly spanning many years, many input data files and many changes both in format and available sensors. Finally, all configuration files are versionned in order to document their history. A web interface has been developed that allows data owners to manage the configuration files for their stations, refresh their data at regular intervals, inspect the data QA log files and allow on-demand data generation. The same interface allows other users to request data on-demand for any time period. This presentation and software has received funding from the World Meteorological Organization under grant agreement No. 29539/2022-1.9 as well as the European Union’s Horizon 2020 research and innovation program under grant agreement No. 101003472 (Arctic Passion). It has also been supported by the WSL/SLF over many years and projects.</pre>

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Extended 1.0 Dataset of "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary"

Introduction We are enclosing the database used in our research titled "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary", along with our statistical calculations. For the sake of reproducibility, further information can be found in the file Short_Description_of_Data_Analysis.pdf and Statistical_formulas.pdf  The sharing of data is part of our aim to strengthen the base of our scientific research. As of March 7, 2024, the detailed submission and analysis of our research findings to a scientific journal has not yet been completed. The dataset was expanded on 23rd September 2024 to include SPSS statistical analysis data, a heatmap, and buffer zone analysis around the Health Development Offices (HDOs) created in QGIS software. Short Description of Data Analysis and Attached Files (datasets): Our research utilised data from 2022, serving as the basis for statistical standardisation. The 2022 Hungarian census provided an objective basis for our analysis, with age group data available at the county level from the Hungarian Central Statistical Office (KSH) website. The 2022 demographic data provided an accurate picture compared to the data available from the 2023 microcensus. The used calculation is based on our standardisation of the 2022 data. For xlsx files, we used MS Excel 2019 (version: 1808, build: 10406.20006) with the SOLVER add-in. Hungarian Central Statistical Office served as the data source for population by age group, county, and regions: <a href="https://www.ksh.hu/stadat_files/nep/hu/nep0035.html">https://www.ksh.hu/stadat_files/nep/hu/nep0035.html</a>, (accessed 04 Jan. 2024.) with data recorded in MS Excel in the Data_of_demography.xlsx file. In 2022, 108 Health Development Offices (HDOs) were operational, and it's noteworthy that no developments have occurred in this area since 2022. The availability of these offices and the demographic data from the Central Statistical Office in Hungary are considered public interest data, freely usable for research purposes without requiring permission. The contact details for the Health Development Offices were sourced from the following page (Hungarian National Population Centre (NNK)): <a href="https://www.nnk.gov.hu/index.php/efi">https://www.nnk.gov.hu/index.php/efi</a> (n=107). The Semmelweis University Health Development Centre was not listed by NNK, hence it was separately recorded as the 108th HDO. More information about the office can be found here: <a href="https://semmelweis.hu/egeszsegfejlesztes/en/">https://semmelweis.hu/egeszsegfejlesztes/en/</a> (n=1). (accessed 05 Dec. 2023.) Geocoordinates were determined using Google Maps (N=108): <a href="https://www.google.com/maps">https://www.google.com/maps</a>. (accessed 02 Jan. 2024.) Recording of geocoordinates (latitude and longitude according to WGS 84 standard), address data (postal code, town name, street, and house number), and the name of each HDO was carried out in the: Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv file. The foundational software for geospatial modelling and display (QGIS 3.34), an open-source software, can be downloaded from: <a href="https://qgis.org/en/site/forusers/download.html">https://qgis.org/en/site/forusers/download.html</a>. (accessed 04 Jan. 2024.) The HDOs_GeoCoordinates.gpkg QGIS project file contains Hungary's administrative map and the recorded addresses of the HDOs from the Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv file, imported via .csv file. The OpenStreetMap tileset is directly accessible from <a href="http://www.openstreetmap.org">www.openstreetmap.org</a> in QGIS. (accessed 04 Jan. 2024.) The Hungarian county administrative boundaries were downloaded from the following website: <a href="https://data2.openstreetmap.hu/hatarok/index.php?admin=6" target="_new">https://data2.openstreetmap.hu/hatarok/index.php?admin=6</a> (accessed 04 Jan. 2024.) HDO_Buffers.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding buffer zones with a radius of 7.5 km. Heatmap.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding heatmap (Kernel Density Estimation). A brief description of the statistical formulas applied is included in the Statistical_formulas.pdf. Recording of our base data for statistical concentration and diversification measurement was done using MS Excel 2019 (version: 1808, build: 10406.20006) in .xlsx format. <ul> <li>Aggregated number of HDOs by county: Number_of_HDOs.xlsx</li> <li>Standardised data (Number of HDOs per 100,000 residents): Standardized_data.xlsx</li> <li>Calculation of the Lorenz curve: Lorenz_curve.xlsx</li> <li>Calculation of the Gini index: Gini_Index.xlsx</li> <li>Calculation of the LQ index: LQ_Index.xlsx</li> <li>Calculation of the Herfindahl-Hirschman Index: Herfindahl_Hirschman_Index.xlsx</li> <li>Calculation of the Entropy index: Entropy_Index.xlsx</li> <li>Regression and correlation analysis calculation: Regression_correlation.xlsx</li> </ul> Using the SPSS 29.0.1.0 program, we performed the following statistical calculations with the databases Data_HDOs_population_without_outliers.sav and Data_HDOs_population.sav: <ul> <li>Regression curve estimation with elderly population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_elderly_without_outlier.spv</li> <li>Pearson correlation table between the total population, elderly population, and number of HDOs per county, excluding outlier values such as Budapest and Pest County: Pearson_Correlation_populations_HDOs_number_without_outliers.spv.</li> <li>Dot diagram including total population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_total_population_without_outliers.spv.</li> <li>Dot diagram including elderly (64<) population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_elderly_population_without_outliers.spv</li> <li>Regression curve estimation with total population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_without_outlier.spv</li> <li>Dot diagram including elderly (64<) population and number of HDOs per county: Dot_HDO_elderly_population.spv</li> <li>Dot diagram including total population and number of HDOs per county: Dot_HDO_total_population.spv</li> <li>Pearson correlation table between the total population, elderly population, and number of HDOs per county: Pearson_Correlation_populations_HDOs_number.spv</li> <li>Regression curve estimation with total population and number of HDOs, (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_total_population.spv</li> </ul> For easier readability, the files have been provided in both SPV and PDF formats. The translation of these supplementary files into English was completed on 23rd Sept. 2024.   If you have any further questions regarding the dataset, please contact the corresponding author: <a target="_new">domjan.peter@phd.semmelweis.hu</a>

GEOLAB - Transnational Access project SHARP - Soil Heterogeneity for soil Amelioration in Road Projects

The SHARP - Soil Heterogeneity for soil Amelioration in Road Projects experiment proposes a novel approach to subgrade improvement: the strategic placement of gravel or other recycled aggregates in a layered configuration. This methodology aims to achieve a sufficient enhancement of the subgrade's mechanical properties, potentially eliminating the need for more resource-intensive and environmentally impactful techniques. The current research phase focuses on the evaluation of performance improvement in silt through the introduction of gravel layers via large-scale triaxial testing. Numerical simulations have already yielded promising results, highlighting the potential of this approach. However, the inherent size disparity between gravel and silt particles necessitates the use of large-scale testing equipment to accurately capture their composite behavior and avoid scale issues. Conventional triaxial equipment, typically limited to 10 cm diameter specimens, is inadequate for this purpose. The utilization of a large-scale triaxial apparatus facilitates the acquisition of reliable data directly applicable to real-world road and railway construction practices. The traditional Terzaghi method for analyzing consolidation (settling) in soils is not accurate for layered soils with different compressibility characteristics. This is a well-established fact (Schiffman & Stein, 1970; Lee et al., 1992). Previous research (Huang & Griffiths, 2010) has also shown that using the Terzaghi method for layered soils in finite element modeling can lead to inaccurate results due to issues with flow continuity at the interfaces between layers. This highlights the importance of studying consolidation in layered soils, both for practical reasons (ensuring the proposed solution is viable) and to gain a deeper theoretical understanding (the heterogeneity effect on consolidating silty soil). While numerical simulations have suggested that adding gravel layers to silty soil can significantly improve slope stability (Bossi et al., 2016), this needs experimental validation. Smaller lab specimens are not suitable due to the size difference between gravel and silt. The test performed within the SHARP project aimed to investigate the effectiveness of a "patchy" gravel-in-silt mix on volumetric changes and shear strength. This approach aims to be practical for construction crews by allowing the gravel to be spread in lenses within the existing subgrade. This would simplify construction and ensure a minimum level of performance for the improved subgrade. Ultimately, the goal is to use the data from these tests to assess the validity of using an "average friction angle" approach (Elkateb, 2003) for layered soils, considering the impact of the spatial distribution of the gravel layers. Numerical modeling can help answer these questions, but data for validation are necessary.

Daten zum Open Access in der deutschsprachigen Rechtswissenschaft (Update 2024)

Die vorliegende Version eines Datensatzes zum Open Access in der Rechtswissenschaft des deutschen Sprachraums (DE/CH/AU/LI) erweitert die <a href="https://doi.org/10.5281/zenodo.7796493">vorige Version</a> um fünf weitere Zeitschriften und berichtigt einzelne andere Einträge (bspw. media|lex). Damit liegt eine zum 1.10.2024 aktualisierte Liste der juristischen Internetzeitschriften im deutschen Sprachraum vor, auf die sich die zeitgleich aktualisierte Fassung des Überblicksbeitrags "<a href="https://open-access.network/informieren/open-access-in-fachdisziplinen/rechtswissenschaft">Open Access in der Rechtswissenschaft</a>" stützt.

METHODS FOR PREVENTING SQL INJECTION IN IDENTITY AND ACCESS MANAGEMENT (IAM) SYSTEMS

This paper discusses methods for preventing SQL (Structured Query Language) injections in identity and access control (IAM) systems. SQL injections represent one of the most serious threats to web security, allowing attackers to gain unauthorized access to and modify data. The main security methods include filtering input data, using prepared statements and parameterization, implementing stored procedures, restricting access rights, and regularly updating software. Effective privilege management and database activity monitoring also play a key role in preventing attacks. The introduction of these measures helps protect confidential information, ensures reliable authentication and authorization, and maintains data integrity. The paper highlights the importance of an integrated approach to database security in the face of growing cyber threats.

Dataset for Unauthorized Access Attacks on Digital Meter SICAM via TCP/IP Communication

This dataset captures network traffic involving unauthorized access attacks on a Digital Meter SICAM device within a controlled test environment. It includes both normal operations and simulated attacks over TCP/IP communication. The clean traffic records legitimate interactions between the Control Station and the SICAM meter, including successful logins, data retrievals, and routine logoffs at specified timestamps. The attack traffic documents an intruder's activities after infiltrating the network: conducting network scans with Nmap, executing dictionary and brute-force attacks using Hydra to discover passwords, and accessing measured values on the SICAM meter.

APPLICATION SECURITY AND LEAST PRIVILEGE ACCESS IN MODERN DEVOPS

In the context of modern DevOps, application security and the implementation of the principle of least privilege (PoLP) are becoming critical elements aimed at minimizing risks and improving the sustainability of IT systems. This article analyzes approaches to integrating security measures at all stages of the software development lifecycle, starting from the early phases, which reduces the likelihood of vulnerabilities. Special attention is paid to the principle of least privilege, which restricts access by users and system components to only the necessary rights, thereby increasing security and preventing unauthorized access. Strategies for minimizing permissions, ensuring infrastructure protection, and automating security checks in CI/CD pipelines are considered. The challenges associated with the implementation of these principles are also discussed, and ways to overcome them are proposed to improve the security and stability of software solutions.

opencc-zeroJan 2024View details →

Linked collectors and determiners for: Accessing cryptic diversity in Neotropical rattlesnakes (Serpentes: Viperidae: Crotalus) with the description of two new species.

Natural history specimen data linked to collectors and determiners held within, "Accessing cryptic diversity in Neotropical rattlesnakes (Serpentes: Viperidae: Crotalus) with the description of two new species". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/a0f62e9a-3360-4076-92aa-b7c85ca34ce5">https://bionomia.net/dataset/a0f62e9a-3360-4076-92aa-b7c85ca34ce5</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/a0f62e9a-3360-4076-92aa-b7c85ca34ce5">https://gbif.org/dataset/a0f62e9a-3360-4076-92aa-b7c85ca34ce5</a>. Formatted as a Frictionless Data package.

opencc-zeroJan 2024View details →

Linked collectors and determiners for: BoGART - Berlin Botanical Garden (B) Accessions Database.

Natural history specimen data linked to collectors and determiners held within, "BoGART - Berlin Botanical Garden (B) Accessions Database". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/f5db93fd-3e51-48a7-b97a-c5375a75a4ad">https://bionomia.net/dataset/f5db93fd-3e51-48a7-b97a-c5375a75a4ad</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/f5db93fd-3e51-48a7-b97a-c5375a75a4ad">https://gbif.org/dataset/f5db93fd-3e51-48a7-b97a-c5375a75a4ad</a>. Formatted as a Frictionless Data package.

opencc-zeroJan 2024View details →

Linked collectors and determiners for: INHS wet collections accession.

Natural history specimen data linked to collectors and determiners held within, "INHS wet collections accession". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/ceac3273-94ef-4793-9789-fb8154d43436">https://bionomia.net/dataset/ceac3273-94ef-4793-9789-fb8154d43436</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/ceac3273-94ef-4793-9789-fb8154d43436">https://gbif.org/dataset/ceac3273-94ef-4793-9789-fb8154d43436</a>. Formatted as a Frictionless Data package.

Genome Annotation Xff Temecula1 (NCBI accession GCF_000007245.1) with Bakta

The genome of Xff strain Temecula1 (NCBI accession GCF_000007245.1) was re-annotated using the Bakta pipeline (Schwengers et al., 2021).

opencc-by-4.0Jun 2021View details →

Wave Parameters - North Atlantic Ocean - Period 2091-2100 - RCP8.5 - MODEL: Wavewatch III - Global Driver: ACCESS

Wave Model: <ul> <li>WAVEWATCH_III - version number 5.16</li> </ul> Global driver:  ACCESS (Australian Community Climate and Earth System Simulator) Variables: <ul> <li>Significant Wave Height</li> <li>Mean period, peak frequency</li> <li>Mean wave direction</li> <li>0.25° x 0.25° horizontal resolution - 3h time resolution</li> </ul> Region:  <ul> <li>southernmost latitude = 10°</li> <li>northernmost latitude = 42°</li> <li>westernmost longitude = -70°</li> <li>easternmost longitude = -5°</li> </ul> 360-day calendar NetCDF format

opencc-by-4.0Jun 2021View details →

Wave Parameters - North Atlantic Ocean - Period 2036-2045 - RCP8.5 - MODEL: Wavewatch III - Global Driver: ACCESS