Comparison of results between NSA-GA, NSA, and random testing on all programs using integer data type and different range.

Introduction

This datasets have SQL injection attacks (SLQIA) as malicious Netflow data. The attacks carried out are SQL injection for Union Query and Blind SQL injection. To perform the attacks, the SQLMAP tool has been used.

NetFlow traffic has generated using DOROTHEA (DOcker-based fRamework fOr gaTHering nEtflow trAffic). NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device.

Datasets

The firts dataset was colleted to train the detection models (D1) and other collected using different attacks than those used in training to test the models and ensure their generalization (D2).

The datasets contain both benign and malicious traffic. All collected datasets are balanced.

The version of NetFlow used to build the datasets is 5.

    Dataset
    Aim
    Samples
    Benign-malicious
    traffic ratio




    D1
    Training
    400,003
    50%


    D2
    Test
    57,239
    50%

Infrastructure and implementation

Two sets of flow data were collected with DOROTHEA. DOROTHEA is a Docker-based framework for NetFlow data collection. It allows you to build interconnected virtual networks to generate and collect flow data using the NetFlow protocol. In DOROTHEA, network traffic packets are sent to a NetFlow generator that has a sensor ipt_netflow installed. The sensor consists of a module for the Linux kernel using Iptables, which processes the packets and converts them to NetFlow flows.

DOROTHEA is configured to use Netflow V5 and export the flow after it is inactive for 15 seconds or after the flow is active for 1800 seconds (30 minutes)

Benign traffic generation nodes simulate network traffic generated by real users, performing tasks such as searching in web browsers, sending emails, or establishing Secure Shell (SSH) connections. Such tasks run as Python scripts. Users may customize them or even incorporate their own. The network traffic is managed by a gateway that performs two main tasks. On the one hand, it routes packets to the Internet. On the other hand, it sends it to a NetFlow data generation node (this process is carried out similarly to packets received from the Internet).

The malicious traffic collected (SQLI attacks) was performed using SQLMAP. SQLMAP is a penetration tool used to automate the process of detecting and exploiting SQL injection vulnerabilities.

The attacks were executed on 16 nodes and launch SQLMAP with the parameters of the following table.

    Parameters
    Description




    '--banner','--current-user','--current-db','--hostname','--is-dba','--users','--passwords','--privileges','--roles','--dbs','--tables','--columns','--schema','--count','--dump','--comments', --schema'
    Enumerate users, password hashes, privileges, roles, databases, tables and columns


    --level=5
    Increase the probability of a false positive identification


    --risk=3
    Increase the probability of extracting data


    --random-agent
    Select the User-Agent randomly


    --batch
    Never ask for user input, use the default behavior


    --answers="follow=Y"
    Predefined answers to yes

Every node executed SQLIA on 200 victim nodes. The victim nodes had deployed a web form vulnerable to Union-type injection attacks, which was connected to the MYSQL or SQLServer database engines (50% of the victim nodes deployed MySQL and the other 50% deployed SQLServer).

The web service was accessible from ports 443 and 80, which are the ports typically used to deploy web services. The IP address space was 182.168.1.1/24 for the benign and malicious traffic-generating nodes. For victim nodes, the address space was 126.52.30.0/24. The malicious traffic in the test sets was collected under different conditions. For D1, SQLIA was performed using Union attacks on the MySQL and SQLServer databases.

However, for D2, BlindSQL SQLIAs were performed against the web form connected to a PostgreSQL database. The IP address spaces of the networks were also different from those of D1. In D2, the IP address space was 152.148.48.1/24 for benign and malicious traffic generating nodes and 140.30.20.1/24 for victim nodes.

To run the MySQL server we ran MariaDB version 10.4.12. Microsoft SQL Server 2017 Express and PostgreSQL version 13 were used.

Random numbers are an important resource for applications such as numerical simulation and secure communication. However, it is difficult to certify whether a physical random number generator is truly unpredictable. Here, we exploit the phenomenon of quantum nonlocality in a loophole-free photonic Bell test experiment for the generation of randomness that cannot be predicted within any physical theory that allows one to make independent measurement choices and prohibits superluminal signaling. To certify and quantify the randomness, we describe a new protocol that performs well in an experimental regime characterized by low violation of Bell inequalities. Applying an extractor function to our data, we obtained 256 new random bits, uniform to within 0.001. arXiv:1702.05178

A number of structural coverage criteria have been proposed to measure the adequacy of testing efforts. In the avionics and other critical systems domains, test suites satisfying structural coverage criteria are mandated by standards. With the advent of powerful automated test generation tools, it is tempting to simply generate test inputs to satisfy these structural coverage criteria. However, while techniques to produce coverage-providing tests are well established, the effectiveness of such approaches in terms of fault detection ability has not been adequately studied. In this work, we evaluate the effectiveness of test suites generated to satisfy four coverage criteria through counterexample-based test generation and a random generation approach-where tests are randomly generated until coverage is achieved-contrasted against purely random test suites of equal size. Our results yield three key conclusions. First, coverage criteria satisfaction alone can be a poor indication of fault finding effectiveness, with inconsistent results between the seven case examples (and random test suites of equal size often providing similar-or even higher-levels of fault finding). Second, the use of structural coverage as a supplement-rather than a target-for test generation can have a positive impact, with random test suites reduced to a coverage-providing subset detecting up to 13.5 percent more faults than test suites generated specifically to achieve coverage. Finally, Observable MC/DC, a criterion designed to account for program structure and the selection of the test oracle, can-in part-address the failings of traditional structural coverage criteria, allowing for the generation of test suites achieving higher levels of fault detection than random test suites of equal size. These observations point to risks inherent in the increase in test automation in critical systems, and the need for more research in how coverage criteria, test generation approaches, the test oracle use- , and system structure jointly influence test effectiveness.

SDC-Scissor tool for Cost-effective Simulation-based Test Selection in Self-driving Cars Software

This dataset provides test cases for self-driving cars with the BeamNG simulator. Check out the repository and demo video to get started.

GitHub: github.com/ChristianBirchler/sdc-scissor

This project extends the tool competition platform from the Cyber-Phisical Systems Testing Competition which was part of the SBST Workshop in 2021.

Usage

Demo

YouTube Link

Installation

The tool can either be run with Docker or locally using Poetry.

When running the simulations a working installation of BeamNG.research is required. Additionally, this simulation cannot be run in a Docker container but must run locally.

To install the application use one of the following approaches:

• Docker: docker build --tag sdc-scissor .
• Poetry: poetry install

Using the Tool

The tool can be used with the following two commands:

• Docker: docker run --volume "$(pwd)/results:/out" --rm sdc-scissor [COMMAND] [OPTIONS] (this will write all files written to /out to the local folder results)
• Poetry: poetry run python sdc-scissor.py [COMMAND] [OPTIONS]

There are multiple commands to use. For simplifying the documentation only the command and their options are described.

• Generation of tests:
  • generate-tests --out-path /path/to/store/tests
• Automated labeling of Tests:
  • label-tests --road-scenarios /path/to/tests --result-folder /path/to/store/labeled/tests
  • Note: This only works locally with BeamNG.research installed
• Model evaluation:
  • evaluate-models --dataset /path/to/train/set --save
• Split train and test data:
  • split-train-test-data --scenarios /path/to/scenarios --train-dir /path/for/train/data --test-dir /path/for/test/data --train-ratio 0.8
• Test outcome prediction:
  • predict-tests --scenarios /path/to/scenarios --classifier /path/to/model.joblib
• Evaluation based on random strategy:
  • evaluate --scenarios /path/to/test/scenarios --classifier /path/to/model.joblib

The possible parameters are always documented with --help.

Linting

The tool is verified the linters flake8 and pylint. These are automatically enabled in Visual Studio Code and can be run manually with the following commands:

poetry run flake8 .
poetry run pylint **/*.py

License

The software we developed is distributed under GNU GPL license. See the LICENSE.md file.

Contacts

Christian Birchler - Zurich University of Applied Science (ZHAW), Switzerland - birc@zhaw.ch

Nicolas Ganz - Zurich University of Applied Science (ZHAW), Switzerland - gann@zhaw.ch

Sajad Khatiri - Zurich University of Applied Science (ZHAW), Switzerland - mazr@zhaw.ch

Dr. Alessio Gambi - Passau University, Germany - alessio.gambi@uni-passau.de

Dr. Sebastiano Panichella - Zurich University of Applied Science (ZHAW), Switzerland - panc@zhaw.ch

References

• Christian Birchler, Nicolas Ganz, Sajad Khatiri, Alessio Gambi, and Sebastiano Panichella. 2022. Cost-effective Simulation-based Test Selection in Self-driving Cars Software with SDC-Scissor. In 2022 IEEE 29th International Conference on Software Analysis, Evolution and Reengineering (SANER), IEEE.

If you use this tool in your research, please cite the following papers:

@INPROCEEDINGS{Birchler2022,
 author={Birchler, Christian and Ganz, Nicolas and Khatiri, Sajad and Gambi, Alessio, and Panichella, Sebastiano},
 booktitle={2022 IEEE 29th International Conference on Software Analysis, Evolution and Reengineering (SANER), 
 title={Cost-effective Simulationbased Test Selection in Self-driving Cars Software with SDC-Scissor}, 
 year={2022},
}

NIST statistical test results for data A in Fig 7 (before learning).

Result of ENT test: Plaintext avalanche (Tp), Key avalanche (Tk), and Plaintext/Ciphertext correlation (Tc).

To evaluate land use and land cover (LULC) maps an independent and representative test dataset is required. Here, a test dataset was generated via stratified random sampling approach across all areas in Fiji not used to generate training data (i.e. all Tikinas which did not contain a training data point were valid for sampling to generate the test dataset). Following equation 13 in Olofsson et al. (2014), the sample size of the test dataset was 834. This was based on a desired standard error of the overall accuracy score of 0.01 and a user's accuracy of 0.75 for all classes. The strata for sampling test samples were the eight LULC classes: water, mangrove, bare soil, urban, agriculture, grassland, shrubland, and trees.

There are different strategies for allocating samples to strata for evaluating LULC maps, as discussed by Olofsson et al. (2014). Equal allocation of samples to strata ensures coverage of rarely occurring classes and minimise the standard error of estimators of user's accuracy. However, equal allocation does not optimise the standard error of the estimator of overall accuracy. Proportional allocation of samples to strata, based on the proportion of the strata in the overall dataset, can result in rarely occurring classes being underrepresented in the test dataset. Optimal allocation of samples to strata is challenging to implement when there are multiple evaluation objectives. Olofsson et al. (2014) recommend a "simple" allocation procedure where 50 to 100 samples are allocated to rare classes and proportional allocation is used to allocate samples to the remaining majority classes. The number of samples to allocate to rare classes can be determined by iterating over different allocations and computing estimated standard errors for performance metrics. Here, the 2021 all-Fiji LULC map, minus the Tikinas used for generating training samples, was used to estimate the proportional areal coverage of each LULC class. The LULC map from 2021 was used to permit comparison with other LULC products with a 2021 layer, notably the ESA WorldCover 10m v200 2021 product.

The 2021 LULC map was dominated by the tree class (74\% of the area classified) and the remaining classes had less than 10\% coverage each. Therefore, a "simple" allocation of 100 samples to the seven minority classes and an allocation of 133 samples to the tree class was used. This ensured all the minority classes had sufficient coverage in the test set while balancing the requirement to minimise standard errors for the estimate of overall accuracy. The allocated number of test dataset points were randomly sampled within each strata and were manually labelled using 2021 annual median RGB composites from Sentinel-2 and Planet NICFI and high-resolution Google Satellite Basemaps.

Data format

The Fiji LULC test data is available in GeoJSON format in the file fiji-lulc-test-data.geojson. Each point feature has two attributes: ref_class (the LULC class manually labelled and quality checked) and strata (the strata the sampled point belongs to derived from the 2021 all-Fiji LULC map). The following integers correspond to the ref_class and strata labels:

1. water
2. mangrove
3. bare earth / rock
4. urban / impervious
5. agriculture
6. grassland
7. shrubland
8. tree

Use

When evaluating LULC maps using test data derived from a stratified sample, the nature of the stratified sampling needs to be accounted for when estimating performance metrics such as overall accuracy, user's accuracy, and producer's accuracy. This is particulary so if the strata do not match the map classes (i.e. when comparing different LULC products). Stehman (2014) provide formulas for estimating performance metrics and their standard errors when using test data with a stratified sampling structure.

To support LULC accuracy assessment a Python package has been developed which provides implementations of Stehman's (2014) formulas. The package can be installed via:

pip install lulc-validation

with documentation and examples here.

In order to compute performance metrics accounting for the stratified nature of the sample the total number of points / pixels available to be sampled in each strata must be known. For this dataset that is:

1. 1779768,
2. 3549325,
3. 541204,
4. 687659,
5. 14279258,
6. 15115599,
7. 4972515,
8. 116131948

Acknowledgements

This dataset was generated with support from a Climate Change AI Innovation Grant.

Next-generation sequencing-based tests have advanced the field of medical diagnostics, but their novelty and cost can lead to uncertainty in clinical deployment. The Heme-STAMP is one such assay that tracks mutations in genes implicated in hematolymphoid neoplasms. Rather than limiting its clinical usage or imposing rule-based criteria, we propose leveraging machine learning to guide clinical decision-making on whether this test should be ordered. We trained a machine learning model to predict the outcome of Heme-STAMP testing using 3,472 orders placed between May 2018 and September 2021 from an academic medical center and demonstrated how to integrate a custom machine learning model into a live clinical environment to obtain real-time model and physician estimates. The model predicted the results of a complex next-generation sequencing test with discriminatory power comparable to expert hematologists (AUC score: 0.77 [0.66, 0.87], 0.78 [0.68, 0.86] respectively) and with capacity to im..., The feature data was pulled from the STAnford medicine Research data Repository (STARR) and further processed to meet the needs of this study and privacy guidelines. Labels were obtained through the Stanford Pathology Department. Ordering physician estimates were generated by participating physicians and model estimates were generated by the machine learning model used in the study., , # Machine learning feature data from EHR, labels, and estimates for next generation sequencing-based assay

https://doi.org/10.5061/dryad.nzs7h450b

Description of the data and file structure

These datasets were utilized to train and evaluate a machine learning model that predicts the outcome of the Heme-STAMP test, a next generation sequencing assay that tracks mutations in genes implicated in hematolymphoid neoplasms. The feature_data_anon.csv was used to train/test a Random Forest model and uses features such as demographics, lab results, medications, diagnoses, etc. Numerical values were binned by their distribution. For example, "Age0" would correspond to the 1st bucket of values while "Age_3" would correspond to the 4th bucket. The estimates.csv contains the estimations generated by the ordering physician and the machine learning model on the orders that were prospectively collected.Â

Files and variables

File: Feature_data...

We used the human genome reference sequence in its GRCh38.p13 version in order to have a reliable source of data in which to carry out our experiments. We chose this version because it is the most recent one available in Ensemble at the moment. However, the DNA sequence by itself is not enough, the specific TSS position of each transcript is needed. In this section, we explain the steps followed to generate the final dataset. These steps are: raw data gathering, positive instances processing, negative instances generation and data splitting by chromosomes.

First, we need an interface in order to download the raw data, which is composed by every transcript sequence in the human genome. We used Ensembl release 104 (Howe et al., 2020) and its utility BioMart (Smedley et al., 2009), which allows us to get large amounts of data easily. It also enables us to select a wide variety of interesting fields, including the transcription start and end sites. After filtering instances that present null values in any relevant field, this combination of the sequence and its flanks will form our raw dataset. Once the sequences are available, we find the TSS position (given by Ensembl) and the 2 following bases to treat it as a codon. After that, 700 bases before this codon and 300 bases after it are concatenated, getting the final sequence of 1003 nucleotides that is going to be used in our models. These specific window values have been used in (Bhandari et al., 2021) and we have kept them as we find it interesting for comparison purposes. One of the most sensitive parts of this dataset is the generation of negative instances. We cannot get this kind of data in a straightforward manner, so we need to generate it synthetically. In order to get examples of negative instances, i.e. sequences that do not represent a transcript start site, we select random DNA positions inside the transcripts that do not correspond to a TSS. Once we have selected the specific position, we get 700 bases ahead and 300 bases after it as we did with the positive instances.

Regarding the positive to negative ratio, in a similar problem, but studying TIS instead of TSS (Zhang135 et al., 2017), a ratio of 10 negative instances to each positive one was found optimal. Following this136 idea, we select 10 random positions from the transcript sequence of each positive codon and label them137 as negative instances. After this process, we end up with 1,122,113 instances: 102,488 positive and 1,019,625 negative sequences. In order to validate and test our models, we need to split this dataset into three parts: train, validation and test. We have decided to make this differentiation by chromosomes, as it is done in (Perez-Rodriguez et al., 2020). Thus, we use chromosome 16 as validation because it is a good example of a chromosome with average characteristics. Then we selected samples from chromosomes 1, 3, 13, 19 and 21 to be part of the test set and used the rest of them to train our models. Every step of this process can be replicated using the scripts available in https://github.com/JoseBarbero/EnsemblTSSPrediction.

2017 Part 1 Common statistical data.csv_ Bronchial Asthma (BA) is one of the most common chronic respiratory diseases among children. 70 children aged 6-17 years old were observed. The control group consisted of 20 virtually healthy children, randomized by age. Standard methods of examination in accordance with the unified clinical protocol “Bronchial Asthma in Children” (order of the Ministry of Healthcare of Ukraine on 08.10.2013, No 868) were applied. This file contains the results of the survey, which are grouped by factors. Each side corresponds to a specific patient, individual data (full name) which is encoded. The number before the factor corresponds to the category which name in accordance with the number is presented separately (see section classification of factors).

Factors 14.age, 5.Complete blood count, 6.Urinalysis Test, 11.Spirogram, 12.Immunological status, 13: TSLP are presented in quantitative terms.

Factors 1.SEVERE PERSISTENT, 1.MODERATE PERSISTENT are encoded with yes / no values ​​(1/0).

From the table, 12 factors are removed, which occur as a result of the survey once or twice.

Classification of factors id : name 0 : Description 1 : Course type 2 : Principal diagnosis (case taking) 3 : Anamnesis of disease 4 : Anamnesis of life 5 : Complete blood count 6 : Urinalysis Test 7 : Skin allergy test (pollen allergens) 8 : Skin allergy test(household allergens) 9 : Food allergy test 10 : Ig E 11 : Spirogram 12 : Immunological status 13 : TSLP 14 : Age 15 : Gender

2017 Part 2 Separeted statistical data.csv_ This paper presents selected data of clinical and paraclinical studies of 70 children in the Kharkiv region, suffering from bronchial asthma, and 20 children in the control group. Individual patient data coded. The study group of patients (Table #1 in the file "2017 Part 1 Common statistical data.csv") was divided into 2 parts: 1)a separated group (Table #1 in the file "2017 Part 2 Separated statistical data.csv") 2)a test group (Table #1 in the file "2017 Part 3 Test statistical data.csv").

In the formation of the test group used a random number generator and the algorithm Random random = new Random(31); ... nextNumber = random.nextInt(size-1); size = 90 - the number of patients who participated in clinical trials.

2017 Part 3 Test statistical data.csv_ This paper presents selected data of clinical and paraclinical studies of 70 children in the Kharkiv region, suffering from bronchial asthma, and 20 children in the control group. Individual patient data coded. The study group of patients (Table #1 in the file "2017 Part 1 Common statistical data.csv") was divided into 2 parts: 1)a separated group (Table #1 in the file "2017 Part 2 Separated statistical data.csv") 2)a test group (Table #1 in the file "2017 Part 3 Test statistical data.csv").

Data DescriptionThe DIPSER dataset is designed to assess student attention and emotion in in-person classroom settings, consisting of RGB camera data, smartwatch sensor data, and labeled attention and emotion metrics. It includes multiple camera angles per student to capture posture and facial expressions, complemented by smartwatch data for inertial and biometric metrics. Attention and emotion labels are derived from self-reports and expert evaluations. The dataset includes diverse demographic groups, with data collected in real-world classroom environments, facilitating the training of machine learning models for predicting attention and correlating it with emotional states.Data Collection and Generation ProceduresThe dataset was collected in a natural classroom environment at the University of Alicante, Spain. The recording setup consisted of six general cameras positioned to capture the overall classroom context and individual cameras placed at each student’s desk. Additionally, smartwatches were used to collect biometric data, such as heart rate, accelerometer, and gyroscope readings.Experimental SessionsNine distinct educational activities were designed to ensure a comprehensive range of engagement scenarios:News Reading – Students read projected or device-displayed news.Brainstorming Session – Idea generation for problem-solving.Lecture – Passive listening to an instructor-led session.Information Organization – Synthesizing information from different sources.Lecture Test – Assessment of lecture content via mobile devices.Individual Presentations – Students present their projects.Knowledge Test – Conducted using Kahoot.Robotics Experimentation – Hands-on session with robotics.MTINY Activity Design – Development of educational activities with computational thinking.Technical SpecificationsRGB Cameras: Individual cameras recorded at 640×480 pixels, while context cameras captured at 1280×720 pixels.Frame Rate: 9-10 FPS depending on the setup.Smartwatch Sensors: Collected heart rate, accelerometer, gyroscope, rotation vector, and light sensor data at a frequency of 1–100 Hz.Data Organization and FormatsThe dataset follows a structured directory format:/groupX/experimentY/subjectZ.zip Each subject-specific folder contains:images/ (individual facial images)watch_sensors/ (sensor readings in JSON format)labels/ (engagement & emotion annotations)metadata/ (subject demographics & session details)Annotations and LabelingEach data entry includes engagement levels (1-5) and emotional states (9 categories) based on both self-reported labels and evaluations by four independent experts. A custom annotation tool was developed to ensure consistency across evaluations.Missing Data and Data QualitySynchronization: A centralized server ensured time alignment across devices. Brightness changes were used to verify synchronization.Completeness: No major missing data, except for occasional random frame drops due to embedded device performance.Data Consistency: Uniform collection methodology across sessions, ensuring high reliability.Data Processing MethodsTo enhance usability, the dataset includes preprocessed bounding boxes for face, body, and hands, along with gaze estimation and head pose annotations. These were generated using YOLO, MediaPipe, and DeepFace.File Formats and AccessibilityImages: Stored in standard JPEG format.Sensor Data: Provided as structured JSON files.Labels: Available as CSV files with timestamps.The dataset is publicly available under the CC-BY license and can be accessed along with the necessary processing scripts via the DIPSER GitHub repository.Potential Errors and LimitationsDue to camera angles, some student movements may be out of frame in collaborative sessions.Lighting conditions vary slightly across experiments.Sensor latency variations are minimal but exist due to embedded device constraints.CitationIf you find this project helpful for your research, please cite our work using the following bibtex entry:@misc{marquezcarpintero2025dipserdatasetinpersonstudent1, title={DIPSER: A Dataset for In-Person Student1 Engagement Recognition in the Wild}, author={Luis Marquez-Carpintero and Sergio Suescun-Ferrandiz and Carolina Lorenzo Álvarez and Jorge Fernandez-Herrero and Diego Viejo and Rosabel Roig-Vila and Miguel Cazorla}, year={2025}, eprint={2502.20209}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2502.20209}, } Usage and ReproducibilityResearchers can utilize standard tools like OpenCV, TensorFlow, and PyTorch for analysis. The dataset supports research in machine learning, affective computing, and education analytics, offering a unique resource for engagement and attention studies in real-world classroom environments.

Abstract

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries.

The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language.

Geographic coverage

13 major metropolitan areas: Bogota, Medellin, Cali, Baranquilla, Bucaramanga, Cucuta, Cartagena, Pasto, Ibague, Pereira, Manizales, Monteira, and Villavicencio.

Analysis unit

The units of analysis are the individual respondents and households. A household roster is undertaken at the start of the survey and the individual respondent is randomly selected among all household members aged 15 to 64 included. The random selection process was designed by the STEP team and compliance with the procedure is carefully monitored during fieldwork.

Universe

The target population for the Colombia STEP survey is all non-institutionalized persons 15 to 64 years old (inclusive) living in private dwellings in urban areas of the country at the time of data collection. This includes all residents except foreign diplomats and non-nationals working for international organizations.

The following groups are excluded from the sample: - residents of institutions (prisons, hospitals, etc.) - residents of senior homes and hospices - residents of other group dwellings such as college dormitories, halfway homes, workers' quarters, etc. - persons living outside the country at the time of data collection.

Kind of data

Sample survey data [ssd]

Sampling procedure

Stratified 7-stage sample design was used in Colombia. The stratification variable is city-size category.

First Stage Sample The primary sample unit (PSU) is a metropolitan area. A sample of 9 metropolitan areas was selected from the 13 metropolitan areas on the sample frame. The metropolitan areas were grouped according to city-size; the five largest metropolitan areas are included in Stratum 1 and the remaining 8 metropolitan areas are included in Stratum 2. The five metropolitan areas in Stratum 1 were selected with certainty; in Stratum 2, four metropolitan areas were selected with probability proportional to size (PPS), where the measure of size was the number of persons aged 15 to 64 in a metropolitan area.

Second Stage Sample The second stage sample unit is a Section. At the second stage of sample selection, a PPS sample of 267 Sections was selected from the sampled metropolitan areas; the measure of size was the number of persons aged 15 to 64 in a Section. The sample of 267 Sections consisted of 243 initial Sections and 24 reserve Sections to be used in the event of complete non-response at the Section level.

Third Stage Sample The third stage sample unit is a Block. Within each selected Section, a PPS sample of 4 blocks was selected; the measure of size was the number of persons aged 15 to 64 in a Block. Two sample Blocks were initially activated while the remaining two sample Blocks were reserved for use in cases where there was a refusal to cooperate at the Block level or cases where the block did not belong to the target population (e.g., parks, and commercial and industrial areas).

Fourth Stage Sample The fourth stage sample unit is a Block Segment. Regarding the Block segmentation strategy, the Colombia document 'FINAL SAMPLING PLAN (ARD-397)' states "According to the 2005 population and housing census conducted by DANE, the average number of dwellings per block in the 13 large cities or metropolitan areas was approximately 42 dwellings. Based on this finding, the defined protocol was to report those cases in which 80 or more dwellings were present in a given block in order to partition block using a random selection algorithm." At the fourth stage of sample selection, 1 Block Segment was selected in each selected Block using a simple random sample (SRS) method.

Fifth Stage Sample The fifth stage sample unit is a dwelling. At the fifth stage of sample selection, 5582 dwellings were selected from the sampled Blocks/Block Segments using a simple random sample (SRS) method. According to the Colombia document 'FINAL SAMPLING PLAN (ARD-397)', the selection of dwellings within a participant Block "was performed differentially amongst the different socioeconomic strata that the Colombian government uses for the generation of cross-subsidies for public utilities (in this case, the socioeconomic stratum used for the electricity bill was used). Given that it is known from previous survey implementations that refusal rates are highest amongst households of higher socioeconomic status, the number of dwellings to be selected increased with the socioeconomic stratum (1 being the poorest and 6 being the richest) that was most prevalent in a given block".

Sixth Stage Sample The sixth stage sample unit is a household. At the sixth stage of sample selection, one household was selected in each selected dwelling using an SRS method.

Seventh Stage Sample The seventh stage sample unit was an individual aged 15-64 (inclusive). The sampling objective was to select one individual with equal probability from each selected household.

Sampling methodologies are described for each country in two documents and are provided as external resources: (i) the National Survey Design Planning Report (NSDPR) (ii) the weighting documentation (available for all countries)

Mode of data collection

Face-to-face [f2f]

Research instrument

The STEP survey instruments include:

• The background questionnaire developed by the World Bank (WB) STEP team
• Reading Literacy Assessment developed by Educational Testing Services (ETS).

All countries adapted and translated both instruments following the STEP technical standards: two independent translators adapted and translated the STEP background questionnaire and Reading Literacy Assessment, while reconciliation was carried out by a third translator.

The survey instruments were piloted as part of the survey pre-test.

The background questionnaire covers such topics as respondents' demographic characteristics, dwelling characteristics, education and training, health, employment, job skill requirements, personality, behavior and preferences, language and family background.

The background questionnaire, the structure of the Reading Literacy Assessment and Reading Literacy Data Codebook are provided in the document "Colombia STEP Skills Measurement Survey Instruments", available in external resources.

Cleaning operations

STEP data management process:

1) Raw data is sent by the survey firm 2) The World Bank (WB) STEP team runs data checks on the background questionnaire data. Educational Testing Services (ETS) runs data checks on the Reading Literacy Assessment data. Comments and questions are sent back to the survey firm. 3) The survey firm reviews comments and questions. When a data entry error is identified, the survey firm corrects the data. 4) The WB STEP team and ETS check if the data files are clean. This might require additional iterations with the survey firm. 5) Once the data has been checked and cleaned, the WB STEP team computes the weights. Weights are computed by the STEP team to ensure consistency across sampling methodologies. 6) ETS scales the Reading Literacy Assessment data. 7) The WB STEP team merges the background questionnaire data with the Reading Literacy Assessment data and computes derived variables.

Detailed information on data processing in STEP surveys is provided in "STEP Guidelines for Data Processing", available in external resources. The template do-file used by the STEP team to check raw background questionnaire data is provided as an external resource, too.`

Response rate

An overall response rate of 48% was achieved in the Colombia STEP Survey.

This dataset is a collection of undirected and unweighted LFR benchmark graphs as proposed by Lancichinetti et al. [1]. We generated the graphs using the code provided by Santo Fortunato on his personal website [2], embedded in our evaluation framework [3], with two different parameter sets. Let N denote the number of vertices in the network, then

Maximum community size: 0.2N (Set A); 0.1N (Set B) Minimum community size: 0.05N (Set A); 10 (Set B) Maximum node degree: 0.19N (Set A); 0.19N (Set B) Community size distribution exponent: 1.0 (Set A); 1.0 (Set B) Degree distribution exponent: 2.0 (Set A); 2.0 (Set B).

All other parameters assume default values. We provide graphs with different combinations of average degree, network size and mixing parameter for the given parameter sets:

Set A: For average degrees in {15, 25, 50} we provide network sizes in {300, 600, 1200}, each with 20 different mixing parameters linearly spaced in [0.2, 0.8]. For each configuration we provide 100 benchmark graphs. Set A: For average degrees in {15, 25, 50} we provide mixing parameters in {0.35, 0.45, 0.55}, each with network sizes in {300, 450, 600, 900, 1200, 1800, 2400, 3600, 4800, 6200, 9600, 19200}. For each configuration we provide 50 benchmark graphs. Set B: For average degrees in {20} we provide network sizes in {300, 600, 1200, 2400}, each with 20 different mixing parameters linearly spaced in [0.2, 0.8]. For each configuration we provide 100 benchmark graphs.

Benchmark graphs are given in edge list format. Further, for each benchmark graph we provide ground truth communities as membership list and as structured datatype (.json), its generating random seeds and basic network statistics.

[1] Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Physical Review E 78(4):046110,https://doi.org/10.1103/PhysRevE.78.046110

[2] https://www.santofortunato.net/resources, Accessed: 19 Jan 2021

[3] https://github.com/synwalk/synwalk-analysis, Accessed: 19 Jan 2021

Datasets based on the bacterial and viral simulated NGS datasets. Fastq files correspond to tests sets of those datasets. Basecall files were generated based on the fastq files with an 8nt simulated barcode between the mates of a read pair. The "rn" datasets containg random length subreads (25-250bp) of the original validation and training reads.

The Nanopore datasets were resimulated with DeepSimulator 1.5 (Li et al., 2020) based on the original datasets (i.e. using the same species composition as the original data). The test Nanopore dataset contains full reads (target average length: 8kb) and the training and validation datasets - 250bp subreads.