Pailler, Louis
hybrid · semantic + lexical · 4 datasets ranked · 7.75s
1 text
These datasets were collected using a total of 585 Armadillidium vulgare females. The objective of this study was to measure survival after immune priming with Salmonella enterica , using different doses and inactivation methods, and to characterise the underlying cellular mechanisms using flow cytometry. Survival analysis according to S. enterica method of inactivation and dosage Survival after injection of a lethal dose of live S. enterica was measured after immune priming using bacteria inactivated by heat (HK) or paraformaldehyde (PFA) and two different doses (10^3 and 10^6). This allowed to collect the Dataset_survival.csv, analysed using the Script_survival_analysis.R. Dataset_survival.csv: Ind: individual identification Treatment: priming treatment that females received. C : control females, no priming injection. PBS : females primed with sterile PBS. PFA3 / PFA6 : females primed with 10^3 or 10^6 PFA-inactivated S. enterica . HK3 / HK6 : females primed with 10^3 or 10^6 heat-inactivated S. enterica . Repl: experimental replicate Status: 1 = dead, 0 = live Survival : Time at death. 168 indicates living females at the end of the experiment Time: Time elapsed between the two injections. T24 : 24hours. T7 : 7 days Haemocyte sub-populations analysis according to priming treatment Following the survival experiment, and because the A. vulgare primed with PFA6 inactivated S. enterica exhibited the highest survival rates against LD50 infection, this inactivated method and dosage were used to examine the haemocyte sub-populations of A. vulgare mounting immune priming. Haemolymph was sampled from all females (C, PBS, PFA6) either 2 days (2D-PP) or 6 days (6D-PP) after the initial priming injection, or 2 days after the LD50 injection (2D-LD50). Distinct sets of females were used for each time point. This allowed to generate the Dataset_cytometry.csv, analysed using the Script_cytometry_pca_analysis.R. Principal Component Analysis (PCA) for each observation time allowed to extract projection values of the four PCs for each individual (Dataset_pca_ind_coord.csv). Dataset_cytometry.csv: Ind: individual identification Treatment: priming treatment that females received. C: control females, no priming injection. PBS: females primed with sterile PBS. PFA6: 10^6 PFA-inactivated S. enterica . Repl: experimental replicate Box: experimental box Exp: experimental day Time: Sampling time. 2D-PP: 2-days after priming. 6D-PP: 6-days after the priming injection. 2D-LD50: 2 days after the LD50 infection. P1_percent: Percentage of the first population. P1_FSCA: Cell diameter (size) of the first population. P1_SSCA: Internal complexity (internal granularity) of the first population. Viab_P1: Viability of the first population. P2_percent: Percentage of the second population. P2_FSCA: Cell diameter (size) of the second population. P2_SSCA: Internal complexity (internal granularity) of the second population. Viab_P2: Viability of the second population. Dataset_pca_ind_coord .csv: Ind: individual identification Treatment: priming treatment that females received. C: control females, no priming injection. PBS: females primed with sterile PBS. PFA6: 10^6 PFA-inactivated S. enterica . Repl: experimental replicate Box: experimental box Exp: experimental day Time: Sampling time. 2D-PP: 2-days after priming. 6D-PP: 6-days after the priming injection. 2D-LD50: 2 days after the lethal dose infection. Dim.1: projection values on the first principal component (PC1) for each individual. Dim.2: projection values on the second principal component (PC2) for each individual. Dim.3: projection values on the third principal component (PC3) for each individual. Dim.4: projection values on the fourth principal component (PC4) for each individual.
HUDA, SABIN
1 files · 729 MB · csvdeclared
AMLNet - Synthetic Anti-Money Laundering Benchmark Dataset, Version 2.0 Relation: Is new version of identifier: 10.5281/zenodo.16736515 AMLNet is a synthetic anti-money laundering benchmark dataset created for machine learning evaluation. This Version 2.0 release is associated with the paper: "AMLNet: A Knowledge-Guided Synthetic Benchmark for Machine Learning Evaluation in Anti-Money Laundering" The dataset contains a fixed synthetic benchmark instance generated using the AMLNet framework. It includes approximately 1.09 million transactions over a 195-day simulation period, from 13 October 2025 to 27 April 2026. The benchmark contains 1,411 suspicious transactions, corresponding to a suspicious rate of approximately 0.13%. The dataset is fully synthetic. The accounts, transactions, timestamps, locations, balances, metadata, labels, and customer activity patterns do not correspond to real customers, real institutions, or real banking activity. AMLNet was designed to support machine learning experiments under rare-event anti-money laundering conditions. The dataset includes ordinary transactions and suspicious transaction sequences representing structuring, layering, and integration patterns. Suspicious activity is embedded within ordinary account activity to make the detection task more realistic and non-trivial. CONTENTS This release includes: - the fixed AMLNet Version 2.0 synthetic transaction dataset; - transaction labels; - laundering typology labels; - transaction metadata; - dataset documentation and column descriptions. Evaluation scripts will be added to this Zenodo record after publication of the associated paper. The AMLNet generator source code is not included in this release due to security concerns. DATA FORMAT The main dataset is provided as a CSV file with the following columns: - step: Sequential simulation step or transaction index. - type: Transaction type, such as BPAY, CASH_OUT, DEBIT, EFTPOS, NPP, OSKO, PAYMENT, or TRANSFER. - amount: Transaction amount in Australian dollars. - category: Transaction category, such as housing, food, transport, recreation, healthcare, education, utilities, shell company, property investment, cryptocurrency, or other. - nameOrig: Originating account or customer identifier. - nameDest: Destination account, customer, or merchant identifier. - oldbalanceOrg: Originating account balance before the transaction. - newbalanceOrig: Originating account balance after the transaction. - isFraud: Binary label used for suspicious/fraudulent transaction detection. - isMoneyLaundering: Binary AML label, where 1 indicates suspicious money laundering activity and 0 indicates ordinary activity. - laundering_typology: Laundering typology label. Values include normal, structuring, layering, and integration. - metadata: JSON-style metadata containing timestamp, location, device information, payment method, risk indicators, and typology-specific details where applicable. - fraud_probability: Model-generated or risk-score field used in selected experiments. This field may be empty for some records. - hour: Hour of transaction. - day_of_week: Day of week. - day_of_month: Day of month. - month: Month number. LABELS The dataset includes two binary label fields: - isFraud: Binary suspicious/fraudulent transaction label. - isMoneyLaundering: Binary anti-money laundering label. The laundering_typology column provides the suspicious activity type for labeled suspicious transactions. Values include: - normal - structuring - layering - integration DATASET STATISTICS - Total transactions: approximately 1.09 million - Suspicious transactions: 1,411 - Suspicious transaction rate: approximately 0.13% - Simulation period: 195 days - Simulation dates: 13 October 2025 to 27 April 2026 - Generated customer accounts: 10,000 - Observed graph nodes: 11,000, including customer and merchant nodes - Transaction types: BPAY, CASH_OUT, DEBIT, EFTPOS, NPP, OSKO, PAYMENT, and TRANSFER - Payment identifiers: BSB_Account, CardNumber, and PayID - Laundering typologies: structuring, layering, and integration - Geographic setting: Australian synthetic banking context SPLIT AND EVALUATION PROTOCOL The associated manuscript describes the split protocol and evaluation protocol used in the reported experiments. For transaction-level experiments, transactions are ordered chronologically to reduce temporal leakage. For node-level graph experiments, the dataset is converted into an account/entity graph, and node features are computed from aggregated transaction statistics. The manuscript describes the chronological train, validation, and test protocol used for model evaluation. Evaluation scripts will be added to this Zenodo record after publication of the associated paper. SYNTHETIC DATA NOTICE This dataset is fully synthetic. It does not contain real customer records, real account information, real banking transactions, real IP addresses, or real financial institution data. All customer identifiers, merchant identifiers, balances, timestamps, locations, transaction patterns, labels, and metadata values were generated for research purposes. The dataset should not be interpreted as a sample of actual banking activity. GENERATOR SOURCE CODE The AMLNet generator source code is not included in this release due to security concerns. Requests for access to the generator source code may be considered for legitimate research purposes, subject to identity verification, institutional affiliation, and appropriate use conditions. The associated manuscript provides the generator pseudocode, main configuration details, demographic assumptions, regulatory constraints, laundering typologies, timing rules, routing rules, split protocol, and evaluation protocol. This public release provides the fixed generated dataset instance used in the associated paper. Evaluation scripts will be added to this Zenodo record after publication. USAGE This dataset can be used for: - anti-money laundering machine learning research; - rare-event classification experiments; - transaction-level suspicious activity detection; - account-level graph classification; - feature ablation studies; - explainability experiments; - synthetic-to-real transfer learning research; - educational and academic research on financial crime detection. LICENSE This dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International License. You may share and adapt the dataset for non-commercial purposes, provided that appropriate credit is given. Commercial use is not permitted without prior permission. For commercial licensing enquiries, please contact: s.huda@griffith.edu.au, hudasabin@gmail.com CITATION If you use this dataset, please cite the Zenodo dataset record and the associated paper: Huda, S., Foo, E., Newton, M. A. H., Jadidi, Z., Huda, S., Morgan, G., and Sattar, A. AMLNet: A Knowledge-Guided Synthetic Benchmark for Machine Learning Evaluation in Anti-Money Laundering. Under review. Dataset: Huda, S. et al. AMLNet Synthetic Anti-Money Laundering Benchmark Dataset, Version 2.0. Zenodo. 10.5281/zenodo.21237971 CONTACT For questions about the dataset, please contact: Sabin Huda School of Information and Communication Technology Faculty Member, Academy of Excellence in Financial Crime Investigation & Compliance Griffith University Email: s.huda@griffith.edu.au, hudasabin@gmail.com
Souza, Caio · Correia, João
54 files · 16 MB · csvdeclared
This repository contains the second version of the metamodel-based rescoring framework for protein-ligand binding affinity prediction. The first version introduced a linear metamodel combined with random train/test splits, but did not explicitly account for structural similarity or information leakage between training, validation, and test partitions. In this updated version, we included: Python scripts for data preprocessing, model training, and evaluation Pairwise ΔΔG evaluation code and associated data Visualization scripts for generating publication figures This dataset and codebase are intended to support reproducibility and further development of metamodel-based rescoring strategies in structural bioinformatics and computational drug design.
Costa, Carlos Fransley Scatambulo
3 files · 614 KB · csv, pdfdeclared
Dados anonimizados referentes ao estudo na tese: Formação Docente em Inteligência Artificial para o Ensino Superior: dos Fundamentos Computacionais ao Uso Crítico e Pedagógico da IA Generativa .