FooDrugs database is a development done by the Computational Biology Group at IMDEA Food Institute (Madrid, Spain), in the context of the Food Nutrition Security Cloud (FNS-Cloud) project. Food Nutrition Security Cloud (FNS-Cloud) has received funding from the European Union's Horizon 2020 Research and Innovation programme (H2020-EU.3.2.2.3. – A sustainable and competitive agri-food industry) under Grant Agreement No. 863059 – www.fns-cloud.eu (See more details about FNS-Cloud below)

FooDrugs stores information extracted from transcriptomics and text documents for foo-drug interactiosn and it is part of a demonstrator to be done in the FNS-Cloud project. The database was built using MySQL, an open source relational database management system. FooDrugs host information for a total of 161 transcriptomics GEO series with 585 conditions for food or bioactive compounds. Each condition is defined as a food/biocomponent per time point, per concentration, per cell line, primary culture or biopsy per study. FooDrugs includes information about a bipartite network with 510 nodes and their similarity scores (tau score; https://clue.io/connectopedia/connectivity_scores) related with possible drug interactions with drugs assayed in conectivity map (https://www.broadinstitute.org/connectivity-map-cmap). The information is stored in eight tables:

Table “study” : This table contains basic information about study identifiers from GEO, pubmed or platform, study type, title and abstract

Table “sample”: This table contains basic information about the different experiments in a study, like the identifier of the sample, treatment, origin type, time point or concentration.

Table “misc_study”: This table contains additional information about different attributes of the study.

Table “misc_sample”: This table contains additional information about different attributes of the sample.

Table “cmap”: This table contains information about 70895 nodes, compromising drugs, foods or bioactives, overexpressed and knockdown genes (see section 3.4). The information includes cell line, compound and perturbation type.

Table “cmap_foodrugs”: This table contains information about the tau score (see section 3.4) that relates food with drugs or genes and the node identifier in the FooDrugs network.

Table “topTable”: This table contains information about 150 over and underexpressed genes from each GEO study condition, used to calculate the tau score (see section 3.4). The information stored is the logarithmic fold change, average expression, t-statistic, p-value, adjusted p-value and if the gene is up or downregulated.

Table “nodes”: This table stores the information about the identification of the sample and the node in the bipartite network connecting the tables “sample”, “cmap_foodrugs” and “topTable”.

In addition, FooDrugs database stores a total of 6422 food/drug interactions from 2849 text documents, obtained from three different sources: 2312 documents from PubMed, 285 from DrugBank, and 252 from drugs.com. These documents describe potential interactions between 1464 food/bioactive compounds and 3009 drugs. The information is stored in two tables:

Table “texts”: This table contains all the documents with its identifiers where interactions have been identified with strategy described in section 4.

Table “TM_interactions”: This table contains information about interaction identifiers, the food and drug entities, and the start and the end positions of the context for the interaction in the document.

FNS-Cloud will overcome fragmentation problems by integrating existing FNS data, which is essential for high-end, pan-European FNS research, addressing FNS, diet, health, and consumer behaviours as well as on sustainable agriculture and the bio-economy. Current fragmented FNS resources not only result in knowledge gaps that inhibit public health and agricultural policy, and the food industry from developing effective solutions, making production sustainable and consumption healthier, but also do not enable exploitation of FNS knowledge for the benefit of European citizens. FNS-Cloud will, through three Demonstrators; Agri-Food, Nutrition & Lifestyle and NCDs & the Microbiome to facilitate: (1) Analyses of regional and country-specific differences in diet including nutrition, (epi)genetics, microbiota, consumer behaviours, culture and lifestyle and their effects on health (obesity, NCDs, ethnic and traditional foods), which are essential for public health and agri-food and health policies; (2) Improved understanding agricultural differences within Europe and what these means in terms of creating a sustainable, resilient food systems for healthy diets; and (3) Clear definitions of boundaries and how these affect the compositions of foods and consumer choices and, ultimately, personal and public health in the future. Long-term sustainability of the FNS-Cloud will be based on Services that have the capacity to link with new resources and enable cross-talk amongst them; access to FNS-Cloud data will be open access, underpinned by FAIR principles (findable, accessible, interoperable and re-useable). FNS-Cloud will work closely with the proposed Food, Nutrition and Health Research Infrastructure (FNHRI) as well as METROFOOD-RI and other existing ESFRI RIs (e.g. ELIXIR, ECRIN) in which several FNS-Cloud Beneficiaries are involved directly. (https://cordis.europa.eu/project/id/863059)

***** changes between version FooDrugs_v2 and FooDrugs_V3 (31st January 2023) are:

Increased the amount of text documents by 85.675 from PubMed and ClinicalTrials.gov, and the amount of Text Mining interactions by 168.826.

Increased the amount of transcriptomic studies by 32 GEO series.

Removed all rows in table cmap_foodrugs representing interactions with values of tau=0

Removed 43 GEO series that after manually checking didn't correspond to food compounds.

Added a new column to the table texts: citation to hold the citation of the text.

Added these columns to the table study: contributor to contain the authors of the study, publication_date to store the date of publication of the study in GEO and pubmed_id to reference the publication associated with the study if any.

Added a new column to topTable to hold the top 150 up-regulated and 150 down-regulated genes.

We present three defect rediscovery datasets mined from Bugzilla. The datasets capture data for three groups of open source software projects: Apache, Eclipse, and KDE. The datasets contain information about approximately 914 thousands of defect reports over a period of 18 years (1999-2017) to capture the inter-relationships among duplicate defects.

File Descriptions

apache.csv - Apache Defect Rediscovery dataset

eclipse.csv - Eclipse Defect Rediscovery dataset

kde.csv - KDE Defect Rediscovery dataset

apache.relations.csv - Inter-relations of rediscovered defects of Apache

eclipse.relations.csv - Inter-relations of rediscovered defects of Eclipse

kde.relations.csv - Inter-relations of rediscovered defects of KDE

create_and_populate_neo4j_objects.cypher - Populates Neo4j graphDB by importing all the data from the CSV files. Note that you have to set dbms.import.csv.legacy_quote_escaping configuration setting to false to load the CSV files as per https://neo4j.com/docs/operations-manual/current/reference/configuration-settings/#config_dbms.import.csv.legacy_quote_escaping

create_and_populate_mysql_objects.sql - Populates MySQL RDBMS by importing all the data from the CSV files

rediscovery_db_mysql.zip - For your convenience, we also provide full backup of the MySQL database

neo4j_examples.txt - Sample Neo4j queries

mysql_examples.txt - Sample MySQL queries

rediscovery_eclipse_6325.png - Output of Neo4j example #1

distinct_attrs.csv - Distinct values of bug_status, resolution, priority, severity for each project

This mysql database contains list of submitted Java programs based on series of online lab exercises from year 2013 to 2015. The programs were submitted by first year computer science students from Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Malaysia who undertaking Introductory Computer Programming subject. There were 67, 18 and 47 of participated students in 2013, 2014 and 2015 respectively. The submitted programs were all of their solution attempts in answering a computational programming question. The question was as the following:

Write a program that will read string. Then your program should show all the string character using * except for character 2, output its real character. sample input. Apology sample output. p****

The global Data Base Management Systems market was valued at USD 50.5 billion in 2022 and is projected to reach USD 120.6 Billion by 2030, registering a CAGR of 11.5 % for the forecast period 2023-2030. Factors Affecting Data Base Management Systems Market Growth

Growing inclination of organizations towards adoption of advanced technologies like cloud-based technology favours the growth of global DBMS market

The cloud-based data base management system solutions offer the organizations with an ability to scale their database infrastructure up or down as per requirement. In a crucial business environment data volume can vary over time. Here, the cloud allows organizations to allocate resources in a dynamic and systematic manner, thereby, ensuring optimal performance without underutilization. In addition, these cloud-based solutions are cost-efficient. As, these cloud-based DBMS solutions eliminate the need for companies to maintain and invest in physical infrastructure and hardware. It helps in reducing ongoing operational costs and upfront capital expenditures. Organizations can choose pay-as-you-go pricing models, where they need to pay only for the resources they consume. Therefore, it has been a cost-efficient option for both smaller businesses and large-enterprises. Moreover, the cloud-based data base management system platforms usually come with management tools which streamline administrative tasks such as backup, provisioning, recovery, and monitoring. It allows IT teams to concentrate on more of strategic tasks rather than routine maintenance activities, thereby, enhancing operational efficiency. Whereas, these cloud-based data base management systems allow users to remote access and collaboration among teams, irrespective of their physical locations. Thus, in regards with today's work environment, which focuses on distributed and remote workforces. These cloud-based DBMS solution enables to access data and update in real-time through authorized personnel, allowing collaboration and better decision-making. Thus, owing to all the above factors, the rising adoption of advanced technologies like cloud-based DBMS is favouring the market growth.

Availability of open-source solutions is likely to restrain the global data base management systems market growth

Open-source data base management system solutions such as PostgreSQL, MongoDB, and MySQL, offer strong functionality at minimal or no licensing costs. It makes open-source solutions an attractive option for companies, especially start-ups or smaller businesses with limited budgets. As these open-source solutions offer similar capabilities to various commercial DBMS offerings, various organizations may opt for this solutions in order to save costs. The open-source solutions may benefit from active developer communities which contribute to their development, enhancement, and maintenance. This type of collaborative environment supports continuous innovation and improvement, which results into solutions that are slightly competitive with commercial offerings in terms of performance and features. Thus, the open-source solutions create competition for commercial DBMS market, they thrive in the market by offering unique value propositions, addressing needs of organizations which prioritize professional support, seamless integration into complex IT ecosystems, and advanced features. Introduction of Data Base Management Systems

A Database Management System (DBMS) is a software which is specifically designed to organize and manage data in a structured manner. This system allows users to create, modify, and query a database, and also manage the security and access controls for that particular database. The DBMS offers tools for creating and modifying data models, that define the structure and relationships of data in a database. This system is also responsible for storing and retrieving data from the database, and also provide several methods for searching and querying the data. The data base management system also offers mechanisms to control concurrent access to the database, in order to ensure that number of users may access the data. The DBMS provides tools to enforce security constraints and data integrity, such as the constraints on the value of data and access controls that restricts who can access the data. The data base management system also provides mechanisms for recovering and backing up the data when a system failure occurs....

This repository contains the Experiment Results and Collection scripts for our ESEC/FSE 2023 submission, "When Function Inlining Meets WebAssembly: A Counterintuitive Effect on Runtime Performance"

Our runtime experiment data is located in the Experiment Results directory. This directory contains two subdirectories, All Experiment Results and Counterintuitive Results Only. In All Experiment Results, we present the runtime results from our Experiments 1-5 and the Libsodium.js case study as CSV files. For the Chromium and Firefox results, each CSV file list the sample names and multiple columns for each of the four optimization levels, O0-O3. Under each optimization level, we list the runtime (in milliseconds) with inlining enabled (from the Baseline experiment), the runtime with inlining enabled (from one of Experiment #1-5), and percent change in runtime after disabling inlining.

The Counterintuitive Results Only directory contains CSV files presenting only the samples from each Experiment #1-5 that meet our threshold of at least a 5% decrease in runtime after disabling inlining.

The Excel file, Wasm Function Inlining Experiment Data.xlsx, contains all of these results in a single workbook, as well as formatting applied to highlight the counterintuitive runtime values presented in our paper.

The raw data collected from our experiments, including the generated WebAssembly, HTML, and JS files to run the samples, is found under the RawCollectedData directory. This folder contains a zipped file that, when extracted, contains subdirectories for each sample's collected data.

The CollectionScripts directory contains the scripts necessary to run our experiments. The PatchFiles directory contains the files from the Binaryen and LLVM infrastructures with the changes that we introduced to enable and disable select optimization passes through environment variables. The Scripts directory contains the scripts we used to run our experiments. The main file within this directory that serves as the entry point is opt_level_inlining.py. This file uses the other scripts to run Experiments 1-5 in our study. The script libsodiumjs_script.py is used to run our Libsodium.js case study.

The scripts are written in Python and Node.js, and the dependencies to run the scripts are Node.js, Python, and MySQL. To download the necessary dependencies for the scripts, run the command `pip install -r requirements.txt` in Scripts folder, and run `npm install` in the Scripts/performance-measurement-tool folder. Then, import the included MySQL schemas under the Scripts folder into a MySQL database. Update the MySQL connection details in the Scripts\_db_utils.py and Scripts\performance-measurement-tool\src\MySQLConnector.ts files, and then run `npm run build` in the Scripts\performance-measurement-tool folder.

Malaria is the leading cause of death in the African region. Data mining can help extract valuable knowledge from available data in the healthcare sector. This makes it possible to train models to predict patient health faster than in clinical trials. Implementations of various machine learning algorithms such as K-Nearest Neighbors, Bayes Theorem, Logistic Regression, Support Vector Machines, and Multinomial Naïve Bayes (MNB), etc., has been applied to malaria datasets in public hospitals, but there are still limitations in modeling using the Naive Bayes multinomial algorithm. This study applies the MNB model to explore the relationship between 15 relevant attributes of public hospitals data. The goal is to examine how the dependency between attributes affects the performance of the classifier. MNB creates transparent and reliable graphical representation between attributes with the ability to predict new situations. The model (MNB) has 97% accuracy. It is concluded that this model outperforms the GNB classifier which has 100% accuracy and the RF which also has 100% accuracy. Methods Prior to collection of data, the researcher was be guided by all ethical training certification on data collection, right to confidentiality and privacy reserved called Institutional Review Board (IRB). Data was be collected from the manual archive of the Hospitals purposively selected using stratified sampling technique, transform the data to electronic form and store in MYSQL database called malaria. Each patient file was extracted and review for signs and symptoms of malaria then check for laboratory confirmation result from diagnosis. The data was be divided into two tables: the first table was called data1 which contain data for use in phase 1 of the classification, while the second table data2 which contains data for use in phase 2 of the classification. Data Source Collection Malaria incidence data set is obtained from Public hospitals from 2017 to 2021. These are the data used for modeling and analysis. Also, putting in mind the geographical location and socio-economic factors inclusive which are available for patients inhabiting those areas. Naive Bayes (Multinomial) is the model used to analyze the collected data for malaria disease prediction and grading accordingly. Data Preprocessing: Data preprocessing shall be done to remove noise and outlier. Transformation: The data shall be transformed from analog to electronic record. Data Partitioning The data which shall be collected will be divided into two portions; one portion of the data shall be extracted as a training set, while the other portion will be used for testing. The training portion shall be taken from a table stored in a database and will be called data which is training set1, while the training portion taking from another table store in a database is shall be called data which is training set2. The dataset was split into two parts: a sample containing 70% of the training data and 30% for the purpose of this research. Then, using MNB classification algorithms implemented in Python, the models were trained on the training sample. On the 30% remaining data, the resulting models were tested, and the results were compared with the other Machine Learning models using the standard metrics. Classification and prediction: Base on the nature of variable in the dataset, this study will use Naïve Bayes (Multinomial) classification techniques; Classification phase 1 and Classification phase 2. The operation of the framework is illustrated as follows: i. Data collection and preprocessing shall be done. ii. Preprocess data shall be stored in a training set 1 and training set 2. These datasets shall be used during classification. iii. Test data set is shall be stored in database test data set. iv. Part of the test data set must be compared for classification using classifier 1 and the remaining part must be classified with classifier 2 as follows: Classifier phase 1: It classify into positive or negative classes. If the patient is having malaria, then the patient is classified as positive (P), while a patient is classified as negative (N) if the patient does not have malaria.
Classifier phase 2: It classify only data set that has been classified as positive by classifier 1, and then further classify them into complicated and uncomplicated class label. The classifier will also capture data on environmental factors, genetics, gender and age, cultural and socio-economic variables. The system will be designed such that the core parameters as a determining factor should supply their value.

ChEssBase is a dynamic relational database for all deep-water species from chemosynthetic ecosystems (hydrothermal vents, cold seeps and other reducing environments such as whale carcasses, sunken wood or OMZs) being constructed from the ChEss project (Biogeography of Deep-Water Chemosynthetic Ecosystems) within the Census of Marine Life initiative. AccConID=21 AccConstrDescription=This license lets others distribute, remix, tweak, and build upon your work, even commercially, as long as they credit you for the original creation. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials. AccConstrDisplay=This dataset is licensed under a Creative Commons Attribution 4.0 International License. AccConstrEN=Attribution (CC BY) AccessConstraint=Attribution (CC BY) AccessConstraints=None Acronym=None added_date=2013-06-12 15:21:34.517000 BrackishFlag=0 CDate=2004-06-24 cdm_data_type=Other CheckedFlag=0 Citation=Ramirez-Llodra, E., Blanco, 2005. ChEssBase: an online information system on biodiversity and biogeography of deep-sea fauna from chemosynthetic ecosystems. Version 2. World Wide Web electronic publications, http://www.noc.soton.ac.uk/chess/database/db_home.php Comments=None ContactEmail=None Conventions=COARDS, CF-1.6, ACDD-1.3 CurrencyDate=None DasID=212 DasOrigin=Literature research DasType=Data DasTypeID=1 DateLastModified={'date': '2025-04-25 01:33:51.812809', 'timezone_type': 1, 'timezone': '+02:00'} DescrCompFlag=0 DescrTransFlag=0 Easternmost_Easting=179.8 EmbargoDate=None EngAbstract=ChEssBase is a dynamic relational database for all deep-water species from chemosynthetic ecosystems (hydrothermal vents, cold seeps and other reducing environments such as whale carcasses, sunken wood or OMZs) being constructed from the ChEss project (Biogeography of Deep-Water Chemosynthetic Ecosystems) within the Census of Marine Life initiative. EngDescr=The aim of ChEssBase is to provide taxonomical, biological, ecological and distributional data for all species described from deep-water chemosynthetic ecosystems, as well as information on available samples, images, bibliography and information on the habitats.These habitats include hydrothermal vents, cold seeps, whale falls, sunken wood and areas of minimum oxygen that intersect with the continental margin or seamounts. Since the discovery of hydrothermal vents in 1977 and of cold seep communities in 1984, over 590 species from vents and over 230 species from seeps have been described. Chemosynthetically fueled communities have now also been found on large organic falls to the deep-sea floor such as whale falls and sunken wood, as well as on benthic zones of oxygen minimum.The data gathered in the last 30 years has shown that some species are shared amongst these ecosystems and our knowledge of their phylogeography improves with every new discovery. New species are continuously being discovered and described from research programmes around the globe and therefore ChEssBase is in active development and new data are being entered regularly. At present, ChEssBase includes data on 1740 species from 193 chemosynthetic sites around the globe. These data contain information (when available) on the taxonomy, morphology, trophic level, reproduction, endemicity, habitat type and distribution. There are now 1880 papers in our reference database.The first version of ChEssBase was available online in December 2004. In summer 2005, ChEssBase and the InterRidge biological database (www.interridge.org) were fused into a single source of information for biological data from chemosynthetic ecosystems. This second version of ChEssBase is available online since August 2005, with new records as well as new search and download options. Since December 2005, ChEssBase is integrated in the Ocean Biogeographic Information System (OBIS, www.iobis.org).ChEssBase is supported by a species-based relational database in MySQL. The database includes 3 major components:Taxonomy (from kingdom to subspecies)Distribution (from site to major geographic area)Samples (including sample, cruise and institution information)ChEssBase is regularly updated with new information available in the literature. In order to quickly obtain accurate new data and help maintain the database up to date, we would be very grateful if you could send us any new publications with data relevant to ChEssBase, which we would add to the database, together with the relevant references. FreshFlag=0 geospatial_lat_max=72.0 geospatial_lat_min=-55.1 geospatial_lat_units=degrees_north geospatial_lon_max=179.8 geospatial_lon_min=-158.1 geospatial_lon_units=degrees_east infoUrl=None InputNotes=None institution=SOTON-SOES, COML, SOTON-NOC License=https://creativecommons.org/licenses/by/4.0/ Lineage=Prior to publication data undergo quality control checked which are described in https://github.com/EMODnet/EMODnetBiocheck?tab=readme-ov-file#understanding-the-output MarineFlag=1 modified_sync=2021-02-05 00:00:00 Northernmost_Northing=72.0 OrigAbstract=None OrigDescr=None OrigDescrLang=None OrigDescrLangNL=None OrigLangCode=None OrigLangCodeExtended=None OrigLangID=None OrigTitle=None OrigTitleLang=None OrigTitleLangCode=None OrigTitleLangID=None OrigTitleLangNL=None Progress=In Progress PublicFlag=1 ReleaseDate=Jun 12 2013 12:00AM ReleaseDate0=2013-06-12 RevisionDate=None SizeReference=1740 species from 193 sites sourceUrl=(local files) Southernmost_Northing=-55.1 standard_name_vocabulary=CF Standard Name Table v70 StandardTitle=ChEssBase StatusID=1 subsetVariables=ScientificName,BasisOfRecord,aphia_id TerrestrialFlag=0 UDate=2025-03-26 VersionDate=Jun 3 2004 12:00AM VersionDay=23 VersionMonth=10 VersionName=2 VersionYear=2007 VlizCoreFlag=1 Westernmost_Easting=-158.1

This data set contains the measurement data of 20 large-scale triaxial (permeability included) tests carried out. Synthetic sand-lime bricks (KS XL) from the Silka company and the natural Nievelstein sandstone were sampled. The samples have a size of about 25 cm in diameter and a height of 45 cm. The experiments where carried out at the Chair of Geotechnical Engeneering at the RWTH Aachen University in Germany.

The following data were recorded for the rock samples:

geomechanical probe properties

size and weight

density (dry and wet)

porosity

permeability coefficient

uniaxial strength

During the tests the following data were recorded:

axial strain

confining and axial pressure

fluid pressure

fluid flow through specimen

temperatures

Data sets are stored in a MySQL-Database. Data sets can be interpreted with the devloped Triaxial Test Evaluation Tool.

The Radiocarbon dating laboratory of IRPA/KIK was founded in the 1960s. Initially dates were reported at more or less regular intervals in the journal Radiocarbon (Schreurs 1968). Since the advent of radiocarbon dating in the 1950s it had been a common practice amongst radiocarbon laboratories to publish their dates in so-called ‘date-lists’ that were arranged per laboratory. This was first done in the Radiocarbon Supplement of the American Journal of Science and later in the specialised journal Radiocarbon. In the course of time the latter, with the added subtitle An International Journal of Cosmogenic Isotope Research, became a regular scientific journal shifting focus from date-lists to articles. Furthermore the world-wide exponential increase of radiocarbon dates made it almost impossible to publish them all in the same journal, even more so because of the broad range of applications that use radiocarbon analysis, ranging from archaeology and art history to geology and oceanography and recently also biomedical studies.The IRPA/KIK database

From 1995 onwards IRPA/KIK’s Radiocarbon laboratory started to publish its dates in small publications, continuing the numbering of the preceding lists in Radiocarbon. The first booklet in this series was “Royal Institute for Cultural Heritage Radiocarbon dates XV” (Van Strydonck et al. 1995), followed by three more volumes (XVI, XVII, XVIII). The next list (XIX, 2005) was no longer printed but instead handed out as a PDF file on CD-rom.

The ever increasing number of dates and the difficulties in handling all the data, however, made us look for a more permanent and easier solution. In order to improve data management and consulting, it was thus decided to gather all our dates in a web-based database. List XIX was in fact already a Microsoft Access database that was converted into a reader friendly style and could also be printed as a PDF file. However a Microsoft Access database is not the most practical solution to make information publicly available. Hence the structure of the database was recreated in Mysql and the existing content was transferred into the corresponding fields. To display the records, a web-based front-end was programmed in PHP/Apache. It features a full-text search function that allows for partial word-matching. In addition the records can be consulted in PDF format.

Old records from the printed date-lists as well as new records are now added using the same Microsoft Acces back-end, which is now connected directly to the Mysql database. The main problem with introducing the old data was that not all the current criteria were available in the past (e.g. stable isotope measurements). Furthermore since all the sample information is given by the submitter, its quality largely depends on the persons willingness to contribute as well as on the accuracy and correctness of the information he provides. Sometimes problems arrive from the fact that a certain investigation (like an excavation) is carried out over a relatively long period (sometimes even more than ten years) and is directed by different people or even institutions. This can lead to differences in the labeling procedure of the samples, but also in the interpretation of structures and artifacts and in the orthography of the site’s name. Finally the submitter might change address, while the names of institutions or even regions and countries might change as well (e.g.Zaire - Congo)

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. A database which contains the information of heparin-binding proteins of E. coli K-12 MG1655 cells. Heparin affinity columns were applied to enrich and fractionate proteins. Identification of proteins was done via the collaboration with David Russell''s lab. Because heparin is negatively charged sulfated glucosaminoglycan, polyamion binding proteins, which contain nucleic acid-binding proteins, are expected to bind to heparin columns. Study of the expression pattern of heparin-binding proteins will help to study the nucleic acid-binding proteins, most of which are related to regulation. Moreover, heparin affinity columns will also erich low abundance proteins. Heparome database is constructed using MySQL. Website interface is built using HTML and PHP. Queries between MySQL database and website interface are executed using PHP. Besides including information of identified proteins, such as swiss accession number, gene name, molecular weight, isoelectric point, condon adaptation index (CAI), functional classification, et. al. , it also includes information of experiments, such as sample preparation, heparin-HPLC chromatography, SDS-PAGE gel separation and MALDI-MS.

Bei dem aufbereiteten Längsschnitt-Datensatzes 2014 bis 2016 handelt es sich um „Big-Data“, weshalb der Gesamtdatensatz nur in Form einer Datenbank (MySQL) verfügbar sein wird. In dieser Datenbank liegt die Information verschiedener Variablen eines Befragten untereinander. Die vorliegende Publikation umfasst eine SQL-Datenbank mit den Meta-Daten des Sample des Gesamtdatensatzes, das einen Ausschnitt der verfügbaren Variablen des Gesamtdatensatzes darstellt und die Struktur der aufbereiteten Daten darlegen soll, und eine Datendokumentation des Samples. Für diesen Zweck beinhaltet das Sample alle Variablen der Soziodemographie, dem Freizeitverhalten, der Zusatzinformation zu einem Befragten und dessen Haushalt sowie den interviewspezifischen Variablen und Gewichte. Lediglich bei den Variablen bezüglich der Mediennutzung des Befragten, handelt es sich um eine kleine Auswahl: Für die Onlinemediennutzung wurden die Variablen aller Gesamtangebote sowie der Einzelangebote der Genre Politik und Digital aufgenommen. Die Mediennutzung von Radio, Print und TV wurde im Sample nicht berücksichtigt, da deren Struktur anhand der veröffentlichten Längsschnittdaten der Media-Analyse MA Radio, MA Pressemedien und MA Intermedia nachvollzogen werden kann.
Die Datenbank mit den tatsächlichen Befragungsdaten wäre auf Grund der Größe des Datenmaterials bereits im kritischen Bereich der Dateigröße für den normalen Up- und Download. Die tatsächlichen Befragungsergebnisse, die zur Analyse nötig sind, werden dann 2021 in Form des Gesamtdatensatzes der Media-Analyse-Daten: IntermediaPlus (2014-2016) im DBK bei GESIS veröffentlicht werden.

Die Daten sowie deren Datenaufbereitung sind ein Vorschlag eines Best-Practice Cases für Big-Data Management bzw. den Umgang mit Big-Data in den Sozialwissenschaften und mit sozialwissenschaftlichen Daten. Unter Verwendung der GESIS Software CharmStats, die im Rahmen dieses Projektes um Big-Data Features erweitert wurde, erfolgt die Dokumentation und Herstellung der Transparenz der Harmonisierungsarbeit. Durch ein Python-Skript sowie ein html-Template wurde der Arbeitsprozess um und mit CharmStats zudem stärker automatisiert.

Der aufbereitete Längsschnitt des Gesamtdatensatzes der MA IntermediaPlus für 2014 bis 2016 wird 2021 in Kooperation mit GESIS herausgegeben werden und den FAIR-Prinzipien (Wilkinson et al. 2016) entsprechend verfügbar gemacht werden. Ziel ist es durch die Harmonisierung der einzelnen Querschnitte die Datenquelle der Media-Analyse, die im Rahmen des Dissertationsprojektes „Angebots- und Publikumsfragmentierung online“ durch Inga Brentel und Céline Fabienne Kampes erfolgt, für Forschung zum sozialen und medialen Wandel in der Bundesrepublik Deutschland zugänglich zu machen.

Künftige Studiennummer des Gesamtdatensatzes der IndermediaPlus im DBK der GESIS: ZA5769 (Version 1-0-0) und der doi: https://dx.doi.org/10.4232/1.13530

****************English Version****************

The prepared Longitudinal IntermediaPlus dataset 2014 to 2016 is a "big data", which is why the entire dataset will only be available in the form of a database (MySQL). In this database, the information of different variables of a respondent is organized in one column, one below the other. The present publication includes a SQL-Database with the meta data of a sample of the full database, which represents a section of the available variables of the total data set and is intended to show the structure of the prepared data and the data-documentation (codebook) of the sample. For this purpose, the sample contains all variables of sociodemography, free-time activities, additional information on a respondent and his household as well as the interview-specific variables and weights. Only the variables concerning the respondent's media use are a small selection: For online media use, the variables of all overall offerings as well as the individual offerings of the genres politics and digital were included. The media use of radio, print and TV was not included in the sample because its structure can be traced using the published longitudinal data of the media analysis MA Radio, MA Pressemedien and MA Intermedia.
Due to the size of the datafile, the database with the actual survey data would already be in the critical range of the file size for the common upload and download. The actual survey result...

Flatback mud crab (Eurypanopeus depressus) transcriptome was assembled from 15 individuals sequenced in 2 Illumina HiSeq2000 lanes PE100 using Trinity2.0.3 and annotated with Trinotate2.0.1 on a custom MySQL database from The Broad Institute. We exposed 3 flatback mud crabs to one of 4 treatments (total = 12 individuals): non-aerated control, aerated control, oil only (Marlin platform Dorado 1g/l ), and oil-dispersant (Marlin platform Dorado 1g/l, COREXIT 9500 0.1g/l) for 72 hours to assess the up and down regulation of genes in muscle tissues. To account for stress caused by laboratory treatments, muscle tissue from three reference individuals that were sacrificed and not exposed to any lab treatments were analyzed. This dataset reports upregulated and downregulated gene expression. NCBI accession numbers are provided for each sample.

The River Ouse Project was started by Dr Margaret Pilkington and colleagues in the Centre for Continuing Education, University of Sussex. Margaret is now retired with emeritus status and continues to run the project with a team of volunteers, in association with the University of Sussex. The team does botanical surveys of streamside grassland and steep wooded valleys (gills) in the upper reaches of the Sussex Ouse, a short flashy river arising on the southern slopes of the High Weald AONB (Area of Outstanding Natural Beauty). Survey sites are chosen on the basis of species richness, potential for restoration and contribution to flood control, and surveyed using the sampling methods outlined in Rodwell, J S (1992. British Plant Communities, Volume 3, Grasslands and Montane Communities). Survey data are transferred from the paper record taken in the field to Excel spreadsheets, and from there after validation and cleaning into two MySQL (MariaDB) databases, meadows and gills.

The file is an extract from the meadows database. It contains binary data of the site occupancy for most of the plants encountered in meadow sites (stands, assemblies) sampled using five 2m x 2m quadrats. Details of the database are available here: River Ouse Project databases.

For further details and access to the full database contact the author.

DACOS - DAtaset of COde Smells

The dataset offers annotated code snippets for three code smells— multifaceted abstraction, complex method, and long parameter list.

In addition to a manually annotated dataset on potentially subjective snippets, we offer a larger set of snippets containing the snippets that are either definitely benign or smelly.

The upload contains three files :

1. DACOSMain.sql - This is the SQL file containing the main DACOS dataset.
2. DACOSExtended.sql - This is the SQL file containing the Extended DACOS dataset.
3. Files.zip - The zip file containing all the source code files.

Required Software

The dataset is created in MySQL. Hence a local or remote installation of MySQL is needed with privileges to create and modify schemas.

Importing the Dataset

The dataset is a self-contained SQL file. To import the dataset, run the following command:

mysql -u username -p database_name < DACOSMain.sql
mysql -u username -p database_name < DACOSExtended.sql

Understanding the Datasets

Both the datasets differ in architecture. The main dataset contains a table named annotations that contains every annotation collected from users. The sample table contains the samples presented to the user for annotation. The class_metrics and method_metrics contain the tables for class and method metrics respectively. These were used to filter samples that are likely to contain smells and hence can be shown to users.

The extended dataset is created by selecting samples that are below or above the selected metric range for each smell. Hence, these samples are definitely smelly or benign. The extended version of the dataset does not contain a table for annotation since they were not presented to user. It instead has an 'entry' table where each sample is classified according to the smell it contains. The codes for identifying smells are as below: