The aim of this survey was to collect feedback about existing training programmes in statistical analysis for postgraduate researchers at the University of Edinburgh, as well as respondents' preferred methods for training, and their requirements for new courses. The survey was circulated via e-mail to research staff and postgraduate researchers across three colleges of the University of Edinburgh: the College of Arts, Humanities and Social Sciences; the College of Science and Engineering; and the College of Medicine and Veterinary Medicine. The survey was conducted on-line using the Bristol Online Survey tool, March through July 2017. 90 responses were received. The Scoping Statistical Analysis Support project, funded by Information Services Innovation Fund, aims to increase visibility and raise the profile of the Research Data Service by: understanding how statistical analysis support is conducted across University of Edinburgh Schools; scoping existing support mechanisms and models for students, researchers and teachers; identifying services and support that would satisfy existing or future demand.

Introductory statistical inference texts and courses treat the point estimation, hypothesis testing, and interval estimation problems separately, with primary emphasis on large-sample approximations. Here, I present an alternative approach to teaching this course, built around p-values, emphasizing provably valid inference for all sample sizes. Details about computation and marginalization are also provided, with several illustrative examples, along with a course outline. Supplementary materials for this article are available online.

The statistical information of the training and testing data set.

The mapping of metabolite-specific data to pathways within cellular metabolism is a major data analysis step needed for biochemical interpretation. A variety of machine learning approaches, particularly deep learning approaches, have been used to predict these metabolite-to-pathway mappings, utilizing a training dataset of known metabolite-to-pathway mappings. A few such training datasets have been derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG). However, several prior published machine learning approaches utilized an erroneous KEGG-derived training dataset that used SMILES molecular representations strings (KEGG-SMILES dataset) and contained a sizable proportion (~26%) duplicate entries. The presence of so many duplicates taint the training and testing sets generated from k-fold cross-validation of the KEGG-SMILES dataset. Therefore, the k-fold cross-validation performance of the resulting machine learning models was grossly inflated by the erroneous presence of these duplicate entries. Here we describe and evaluate the KEGG-SMILES dataset so that others may avoid using it. We also identify the prior publications that utilized this erroneous KEGG-SMILES dataset so their machine learning results can be properly and critically evaluated. In addition, we demonstrate the reduction of model k-fold cross-validation (CV) performance after de-duplicating the KEGG-SMILES dataset. This is a cautionary tale about properly vetting prior published benchmark datasets before using them in machine learning approaches. We hope others will avoid similar mistakes.

This collection contains a snapshot of the learning resource metadata from ESIP's Data management Training Clearinghouse (DMTC) associated with the closeout (March 30, 2023) of the Institute of Museum and Library Services funded (Award Number: LG-70-18-0092-18) Development of an Enhanced and Expanded Data Management Training Clearinghouse project. The shared metadata are a snapshot associated with the final reporting date for the project, and the associated data report is also based upon the same data snapshot on the same date.

The materials included in the collection consist of the following:

esip-dev-02.edacnm.org.json.zip - a zip archive containing the metadata for 587 published learning resources as of March 30, 2023. These metadata include all publicly available metadata elements for the published learning resources with the exception of the metadata elements containing individual email addresses (submitter and contact) to reduce the exposure of these data.

statistics.pdf - an automatically generated report summarizing information about the collection of materials in the DMTC Clearinghouse, including both published and unpublished learning resources. This report includes the numbers of published and unpublished resources through time; the number of learning resources within subject categories and detailed subject categories, the dates items assigned to each category were first added to the Clearinghouse, and the most recent data that items were added to that category; the distribution of learning resources across target audiences; and the frequency of keywords within the learning resource collection. This report is based on the metadata for published resourced included in this collection, and preliminary metadata for unpublished learning resources that are not included in the shared dataset.

The metadata fields consist of the following:

    Fieldname
    Description




    abstract_data
    A brief synopsis or abstract about the learning resource


    abstract_format
    Declaration for how the abstract description will be represented.


    access_conditions
    Conditions upon which the resource can be accessed beyond cost, e.g., login required.


    access_cost
    Yes or No choice stating whether othere is a fee for access to or use of the resource.


    accessibililty_features_name
    Content features of the resource, such as accessible media, alternatives and supported enhancements for accessibility.


    accessibililty_summary
    A human-readable summary of specific accessibility features or deficiencies.


    author_names
    List of authors for a resource derived from the given/first and family/last names of the personal author fields by the system


    author_org
    - name
    - name_identifier
    - name_identifier_type



    - Name of organization authoring the learning resource.
    - The unique identifier for the organization authoring the resource.
    - The identifier scheme associated with the unique identifier for the organization authoring the resource.

authors - givenName - familyName - name_identifier - name_identifier_type

    - Given or first name of person(s) authoring the resource.
    - Last or family name of person(s) authoring the resource.
    - The unique identifier for the person(s) authoring the resource.
    - The identifier scheme associated with the unique identifier for the person(s) authoring the resource, e.g., ORCID.



    citation
    Preferred Form of Citation.


    completion_time
    Intended Time to Complete

contact - name - org - email

    - Name of person(s) who has/have been asserted as the contact(s) for the resource in case of questions or follow-up by resource user.
    - Name of organization that has/have been asserted as the contact(s) for the resource in case of questions or follow-up by resource user.
    - (excluded) Contact email address.



    contributor_orgs
    - name
    - name_identifier
    - name_identifier_type
    - type
    - Name of organization that is a secondary contributor to the learningresource. A contributor can also be an individual person.
    - The unique identifier for the organization contributing to the resource.
    - The identifier scheme associated with the unique identifier for the organization contributing to the resource.
    - Type of contribution to the resource made by an organization.


    contributors
    - familyName
    - givenName
    - name_identifier
    - name_identifier_type

• Last or family name of person(s) contributing to the resource. - Given or first name of person(s) contributing to the resource. - The unique identifier for the person(s) contributing to the resource. - The identifier scheme associated with the unique identifier for the person(s) contributing to the resource, e.g., ORCID.

contributors.type

Type of contribution to the resource made by a person.

    created
    The date on which the metadata record was first saved as part of the input workflow.


    creator
    The name of the person creating the MD record for a resource.


    credential_status
    Declaration of whether a credential is offered for comopletion of the resource.

ed_frameworks - name - description - nodes.name

    - The name of the educational framework to which the resource is aligned, if any. An educational framework is a structured description of educational concepts such as a shared curriculum, syllabus or set of learning objectives, or a vocabulary for describing some other aspect of education such as educational levels or reading ability.
    - A description of one or more subcategories of an educational framework to which a resource is associated.
    - The name of a subcategory of an educational framework to which a resource is associated.


    expertise_level
    The skill level targeted for the topic being taught.


    id
    Unique identifier for the MD record generated by the system in UUID format.


    keywords
    Important phrases or words used to describe the resource.


    language_primary
    Original language in which the learning resource being described is published or made available.


    languages_secondary
    Additional languages in which the resource is tranlated or made available, if any.


    license
    A license for use of that applies to the resource, typically indicated by URL.


    locator_data
    The identifier for the learning resource used as part of a citation, if available.


    locator_type
    Designation of citation locatorr type, e.g., DOI, ARK, Handle.


    lr_outcomes
    Descriptions of what knowledge, skills or abilities students should learn from the resource.


    lr_type
    A characteristic that describes the predominant type or kind of learning resource.


    media_type
    Media type of resource.


    modification_date
    System generated date and time when MD record is modified.


    notes
    MD Record Input Notes


    pub_status
    Status of metadata record within the system, i.e., in-process, in-review, pre-pub-review, deprecate-request, deprecated or published.


    published
    Date of first broadcast / publication.


    publisher
    The organization credited with publishing or broadcasting the resource.


    purpose
    The purpose of the resource in the context of education; e.g., instruction, professional education, assessment.


    rating
    The aggregation of input from all user assessments evaluating users' reaction to the learning resource following Kirkpatrick's model of training evaluation.


    ratings
    Inputs from users assessing each user's reaction to the learning resource following Kirkpatrick's model of training evaluation.


    resource_modification_date
    Date in which the resource has last been modified from the original published or broadcast version.


    status
    System generated publication status of the resource w/in the registry as a yes for published or no for not published.


    subject
    Subject domain(s) toward which the resource is targeted. There may be more than one value for this field.


    submitter_email
    (excluded) Email address of person who submitted the resource.


    submitter_name
    Submission Contact Person


    target_audience
    Audience(s) for which the resource is intended.


    title
    The name of the resource.


    url
    URL that resolves to a downloadable version of the learning resource or to a landing page for the resource that contains important contextual information including the direct resolvable link to the resource, if applicable.


    usage_info
    Descriptive information about using the resource, not addressed by the License information field.


    version
    The specific version of the resource, if declared.

A small brain and short life allegedly limit cognitive abilities. Our view of invertebrate cognition may also be biased by the choice of experimental stimuli. Here, the stimuli (color) pairs in Match-To-Sample (MTS) tasks affected the performance of buff-tailed bumblebees (Bombus terrestris). We trained the bees to roll a tool, ball, to a goal that matched its color. Color-matching performance was slower with yellow-and-orange/red than with blue-and-yellow stimuli. When assessing the bees' concept learning in a transfer test with a novel color, the bees trained with blue-and-yellow (novel color: orange/red) were highly successful, the bees trained with blue-and-orange/red (novel color: yellow) did not differ from random, and those trained with yellow-and-orange/red (novel color: blue) failed the test. These results highlight that stimulus salience can affect the conclusions on test subjects' cognitive ability. Therefore, we encourage paying attention to stimulus salience (among other factors) when assessing invertebrate cognition. Methods Study system The experiments were conducted in 2018 in bumblebee facilities at the Botanical Garden of the University of Oulu, Finland. We obtained bumblebees from a continuous rearing program (Koppert B.V., The Netherlands). Each of the bumblebee hives (N = 7) used in the study were housed in a wooden box (31 cm × 13.5 cm × 11.5 cm) that had holes for air exchange and separate entrance and main hive chambers, with a 3 cm layer of cat litter at the bottom of the former. Each hive had a queen and ~30 workers. We provided each hive with ~7 g commercial pollen (Koppert B.V., The Netherlands) on every second day and, when not being trained or tested (see below) in which the bees had a continuous opportunity to forage on a 30% sucrose solution from a feeder. We used one hive at a time. Its entrance chamber was connected to a transparent plexiglass corridor (25 cm × 4 cm × 4.5 cm), which allowed bumblebees to access an arena (60 cm × 25 cm × 43 cm). Three transparent plastic sliding doors along the corridor provided means to control the access of bumblebees to the arena (for testing purposes). This setup was used during pretraining, training and testing (see below). Pretraining The aim of this pretraining was to allow the bees to learn the location where to access the reward. In the pretraining, the bumblebees had unrestricted access to the arena where they could access 30% sucrose solution from the middle of a circular white platform (Ø 150 mm) that was placed in the central part of the arena. During the pretraining, the most active foragers were identified by an observer (OJL and VN) and each of these bees was marked with a small number tag. These tagged individuals were used in the training and test. Training The purpose of the training was to assess our hypothesis, while training the bees to match to a sample. In the training, the center of the arena had a white circular plastic platform (Ø 150 mm with a bordering wall 12 mm high). This platform had a hole in its center (Ø 12 mm) and a colored circular zone encompassing the center hole (Ø 35 mm). Three lanes (20 mm wide at the center section, outlined by 1 mm high and 10 mm wide plastic strips) ran from the rim of the platform and converged at the central zone at 120° angles relative to the adjacent lanes (Figure 1A). The platform also had two wooden balls (Ø 8.5 mm) of different colors (blue and yellow, blue and orange/red, or yellow and orange/red), painted using Uni POSCA PC-5M, Mitsubishi Pencil Co., LTD. Japan (Figure 1). The bordering wall, the three lanes, the circular zone around the center hole (collectively referred to as 'platform'), and one of the two balls had the color that matched the platform, while the other ball was of a different color. During the training, only one tagged bee (N = 28 over the experiment) was allowed to access the arena at a time. Each bee was randomly assigned to a treatment group (blue and yellow, blue and orange/red, or yellow and orange/red) and thus only exposed to only two of the three colors used in the experiment. In each treatment group, a bee was exposed to the platform of two different colors. There were two balls, with one of the two balls always matching the color of the platform and the other ball having the other color. Each bee was challenged with a color matching task in the context of token use. The bee was given 5 minutes to complete a training bout. During a training bout, the 'correct' (rewarding) action required the bumblebee to successfully roll the ball that matched the color of the platform, from the rim of the platform to its center hole. Rolling the ball onto the central zone surrounding the hole, but not all the way into the hole, was also considered as successful. If the bee was successful, the experimenter used a syringe to immediately place a reward of 30% sucrose solution ad libitum (>200 µl) in the central hole for the bee to drink. Failing to accomplish the task within 5 minutes (or rolling the non-matching ball onto the central zone) was deemed as ‘incorrect’ (i.e., the bumblebee did not accomplish the task). After each training bout (successful or fail), the bumblebee was allowed to use the connecting corridor to visit the hive and then later to return to the arena to try again (i.e., the start of another training bout). We cleaned both balls and the platform cleaned with ethanol to neutralize any odor cues after each training bout. We also switched the color of the platform's color theme between the two options used for that particular bumblebee after every 1-3 training bouts. The behavior of the bee was video recorded for later analysis using a Sony Xperia XZ Premium smartphone. As soon as an individual reached the criterion of training (matching the ball that had the color as the platform for 5 or more consecutive bouts), she was used in the transfer test. At the end of each day, all the bees were allowed to freely access the arena to forage from a white platform, as during the pre-training phase. The training progressed in a stepwise fashion that included four steps. In the first step, the ball that matched the color of the platform was already in the central hole, and the bumblebee was rewarded as soon as it touched that ball. Once this had happened, the task progressed to the second step, in which the 'correct' ball was placed next to the central zone. After this step was successfully completed, the third step involved the balls being placed in the midway between the central zone and the rim of the platform. Once the focal bumblebee completed this step, the final step involved both balls being placed at the rim of the platform, from where the bumblebee needed to roll the ball to the center. Most, if not all, of the bees failed one or more steps during the training. When a bumblebee did not successfully perform the task correctly within 5 minutes training bouts, the experimenter (OJL and VN) used a plastic model bumblebee (which mimicked the color patterns of a B. terrestris worker), attached to a thin transparent stick, to demonstrate how to solve the task. The experimenter then used a syringe to give the sucrose solution directly to the bumblebee. A model, rather than living, bumblebee demonstrator ensured a desired and standardized demonstration. Transfer test The purpose of the transfer test was to assess whether the bees exhibit concept learning by applying a learned rule in a novel context. The test was conducted once a bee reached the training criterion (5 or more successful training bouts in a row). The test consisted of a single bout that was similar to the last phase of training with the following exceptions: The platform was of the 'third' color that the bumblebee had not encountered during the training. In addition, the platform had 3 balls of different colors: blue, yellow or orange/red. One ball was placed at the end of each lane, next to the rim of the platform. The test ended if the bee rolled the correct ball to the central hole. If the bumblebee rolled a ball of a color that did not match with that of the platform, it was considered to have failed the test and the test continued during which the 'incorrect' ball was returned to the trim of the platform until 10 minutes passed. QUANTIFICATION AND STATISTICAL ANALYSIS All statistical analyses were conducted using R version 3.6.2 and SPSS v25 (IBM Corp). Generalised Linear Mixed Models (GLMM) with a poisson distribution (link = log) in the package 'glmmTMB' were used to examine whether the color pair (three levels: blue-and-yellow, blue-and-orange/red, and yellow-and-orange/red) affected the number of bout taken to reach the training criterion. We included bee ID nested within colony ID as the random variable. To assessing whether bumblebees learned to solve the generalization task, we used 1/3 as the baseline expectation and compared it to bumblebees' performance (in terms of the number of bumblebees that solved vs. did not solve the task) for each of the treatment group using a binomial test. To examine the effect of the color pairs in relation to success in the test, we conducted a GLMM with binomial distribution (link = logit). However, due to convergent issues (likely related to the zeros, or the bees in the yellow-and-orange/red treatment group completely failed the test), the model could not be run. Accordingly, we compared bumblebees’ test performance between the three treatment groups using Fisher’s exact test. We also used Fisher’s exact test with Bonferroni corrections for posthoc analyses, when comparing the performance between any two treatment groups (adjusted significance level: P≤0.017). Another GLMM with poisson distribution (link = log) was conducted to examine whether the training bouts differed between the bees that have successfully completed the transfer test and those bees that failed the test. For this analysis, we included colony, bee

The Remote Sales Training Tool market is rapidly evolving, driven by the increasing need for organizations to adapt their sales training methodologies in an increasingly digital-driven world. As companies pivot to remote work environments, these tools have become essential in enhancing the skills of sales teams acro

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Upon thorough corporate training market analysis and research, the following factors has been identified as the critical market trends during the forecast period 2020-2024:

emergence of cost-effective e-learning training modules

The corporate training market report also provides several other key information including:

CAGR of the market during the forecast period 2020-2024
Detailed information on factors that will drive corporate training market growth during the next five years
Precise estimation of the corporate training market size and its contribution to the parent market
Accurate predictions on upcoming trends and changes in consumer behavior
The growth of the corporate training market industry across APAC, Europe, MEA, North America, and South America
A thorough analysis of the market’s competitive landscape and detailed information on vendors
Comprehensive details of factors that will challenge the growth of corporate training market vendors

RNA expression analysis was performed on the corpus luteum tissue at five time points after prostaglandin F2 alpha treatment of midcycle cows using an Affymetrix Bovine Gene v1 Array. The normalized linear microarray data was uploaded to the NCBI GEO repository (GSE94069). Subsequent statistical analysis determined differentially expressed transcripts ± 1.5-fold change from saline control with P ≤ 0.05. Gene ontology of differentially expressed transcripts was annotated by DAVID and Panther. Physiological characteristics of the study animals are presented in a figure. Bioinformatic analysis by Ingenuity Pathway Analysis was curated, compiled, and presented in tables. A dataset comparison with similar microarray analyses was performed and bioinformatics analysis by Ingenuity Pathway Analysis, DAVID, Panther, and String of differentially expressed genes from each dataset as well as the differentially expressed genes common to all three datasets were curated, compiled, and presented in tables. Finally, a table comparing four bioinformatics tools' predictions of functions associated with genes common to all three datasets is presented. These data have been further analyzed and interpreted in the companion article "Early transcriptome responses of the bovine mid-cycle corpus luteum to prostaglandin F2 alpha includes cytokine signaling". Resources in this dataset:Resource Title: Supporting information as Excel spreadsheets and tables. File Name: Web Page, url: http://www.sciencedirect.com/science/article/pii/S2352340917304031?via=ihub#s0070

The nature and severity of climate change impacts varies significantly from region to region. Consequently, high-resolution climate information is needed for meaningful impact assessments and the design of mitigation strategies. This demand has led to an increase in the coupling of Empirical Statistical Downscaling (ESD) models to General Circulation Model (GCM) simulations of future climate. Here, we present a new open-source Python package (pyESD; github.com/Dan-Boat/PyESD) that implements several Perfect Prognosis ESD (PP-ESD) methods and the whole downscaling cycle. The latter includes routines for data preparation, predictor selection and construction, model selection and training, evaluation, utility tools for relevant statistical tests, visualization, and more. The package includes a collection of well-established Machine Learning algorithms and allows the user to choose a variety of estimators, cross-validation schemes, objective function measures, hyperparameter optimization, etc., in relatively few lines of code. The package is highly modular and flexible and allows quick and reproducible downscaling of any climate information, such as precipitation, temperature, wind speed, or even glacial retreat. The dataset presented here serves as supporting material for the package description and evaluation manuscript

Global Virtual Training And Simulation market size was valued at USD 374.97 Bn in 2023 & USD 1103.32 Bn by 2032, at a CAGR of 12.74% from 2024-2032

Data availability. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence.

• SPSS DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15).
• EXCEL DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15).
• Data of Project factorial.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel).
• Data Project reliability.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel).
• FIGURES. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (Figure 1.jpeg, Figure 2.jpeg, Figure 3 and Figure 4.jpeg).

The BUTTER Empirical Deep Learning Dataset represents an empirical study of the deep learning phenomena on dense fully connected networks, scanning across thirteen datasets, eight network shapes, fourteen depths, twenty-three network sizes (number of trainable parameters), four learning rates, six minibatch sizes, four levels of label noise, and fourteen levels of L1 and L2 regularization each. Multiple repetitions (typically 30, sometimes 10) of each combination of hyperparameters were preformed, and statistics including training and test loss (using a 80% / 20% shuffled train-test split) are recorded at the end of each training epoch. In total, this dataset covers 178 thousand distinct hyperparameter settings ("experiments"), 3.55 million individual training runs (an average of 20 repetitions of each experiments), and a total of 13.3 billion training epochs (three thousand epochs were covered by most runs). Accumulating this dataset consumed 5,448.4 CPU core-years, 17.8 GPU-years, and 111.2 node-years.

Skills Bootcamps for Londoners aim to help Londoners aged 19+ to enter employment, upskill or change career and are open to adults who are full-time or part-time employed, self-employed or unemployed, as well as adults returning to work after a break. Bootcamp training courses provide access to in-demand sector specific skills training and provide a guaranteed job interview on completion. In addition to technical training, learners will also receive guidance on entering professional working environments to fully prepare them for new roles. More information on the programme can be found here. The Skills Bootcamp for Londoners data is a summary of provider-reported Skills Bootcamps starts, completions and outcomes from courses funded by the Greater London Authority. Wave 3 data includes Bootcamps started between April 2022 and March 2023. Completions and outcomes can occur and be reported in the 2022-23 financial year and in a defined period after that year. Wave 3 was the first wave of Skills Bootcamps that were delegated to the Greater London Authority.

This traffic dataset contains a balance size of encrypted malicious and legitimate traffic for encrypted malicious traffic detection and analysis. The dataset is a secondary csv feature data that is composed of six public traffic datasets.

Our dataset is curated based on two criteria: The first criterion is to combine widely considered public datasets which contain enough encrypted malicious or encrypted legitimate traffic in existing works, such as Malware Capture Facility Project datasets. The second criterion is to ensure the final dataset balance of encrypted malicious and legitimate network traffic.

Based on the criteria, 6 public datasets are selected. After data pre-processing, details of each selected public dataset and the size of different encrypted traffic are shown in the “Dataset Statistic Analysis Document”. The document summarized the malicious and legitimate traffic size we selected from each selected public dataset, the traffic size of each malicious traffic type, and the total traffic size of the composed dataset. From the table, we are able to observe that encrypted malicious and legitimate traffic equally contributes to approximately 50% of the final composed dataset.

The datasets now made available were prepared to aim at encrypted malicious traffic detection. Since the dataset is used for machine learning or deep learning model training, a sample of train and test sets are also provided. The train and test datasets are separated based on 1:4. Such datasets can be used for machine learning or deep learning model training and testing based on selected features or after processing further data pre-processing.

Introduction

Training.gov.au (TGA) is the National Register of Vocational Education and Training in Australia and contains authoritative information about Registered Training Organisations (RTOs), Nationally Recognised Training (NRT) and the approved scope of each RTO to deliver NRT as required in national and jurisdictional legislation.

TGA web-services overview

TGA has a web service available to allow external systems to access and utilise information stored in TGA through an external system. The TGA web service is exposed through a single interface and web service users are assigned a data reader role which will apply to all data stored in the TGA.

The web service can be broadly split into three categories:

1. RTOs and other organisation types;

2. Training components including Accredited courses, Accredited course Modules Training Packages, Qualifications, Skill Sets and Units of Competency;

3. System metadata including static data and statistical classifications.

Users will gain access to the TGA web service by first passing a user name and password through to the web server. The web server will then authenticate the user against the TGA security provider before passing the request to the application that supplies the web services.

There are two web services environments:

1. Production - ws.training.gov.au – National Register production web services

2. Sandbox - ws.sandbox.training.gov.au – National Register sandbox web services.

The National Register sandbox web service is used to test against the current version of the web services where the functionality will be identical to the current production release. The web service definition and schema of the National Register sandbox database will also be identical to that of production release at any given point in time. The National Register sandbox database will be cleared down at regular intervals and realigned with the National Register production environment.

Each environment has three configured services:

1. Organisation Service;

2. Training Component Service; and

3. Classification Service.

Sandbox environment access

To access the download area for web services, navigate to http://tga.hsd.com.au and use the below name and password:

Username: WebService.Read (case sensitive)

Password: Asdf098 (case sensitive)

This download area contains various versions of the following artefacts that you may find useful

• Training.gov.au web service specification document;

• Training.gov.au logical data model and definitions document;

• .NET web service SDK sample app (with source code);

• Java sample client (with source code);

• How to setup web service client in VS 2010 video; and

• Web services WSDL's and XSD's.

For the business areas, the specification/definition documents and the sample application is a good place to start while the IT areas will find the sample source code and the video useful to start developing against the TGA web services.

The web services Sandbox end point is: https://ws.sandbox.training.gov.au/Deewr.Tga.Webservices

Production web service access

Once you are ready to access the production web service, please email the TGA team at tgaproject@education.gov.au to obtain a unique user name and password.

This dataset represents a group of paper records (a "series") within the Steven Lawrence Gortmaker papers, 1955-1998 (inclusive), 1977-1997 (bulk), which can be accessed on-site at the Center for the History of Medicine at the Francis A. Countway Library of Medicine in Boston, Massachusetts. The series consists of readings, class session records, and course evaluations generated and compiled by Steven Lawrence Gortmaker as a product of teaching two courses in the Harvard School of Public Health Department of Health and Social Behavior: HSB 206 (HIV, Transmission, & Social Behavior); and HSB 230 (Social & Behavioral Research Methods). Class readings include reprints, newspaper clippings, brochures, and pamphlets assigned to students as part of the course curriculum. Class session records of: course syllabi; lecture and presentation records; class handouts; student lists and information sheets; and student assignments. Course evaluation records include raw data course evaluations completed by students, and related summarized and analyzed data tables. Subjects covered in HSB 206 include various topics related to sexuality, HIV, and AIDS, such as: sexual behavior; drug and alcohol use; sexual abuse; bisexuality and homosexuality; prostitution; and educational and behavioral intervention. The HSB 230 course covered various areas of study design and statistical analysis, including: study implementation and delivery; data collection and processing methods; sample size and statistical significance; statistical models; and regression, among other topics. Some papers are in Spanish. Data and associated records are accessible onsite at the Center for the History of Medicine per the conditions governing access described below. Conditions Governing Access to Original Collection Materials: The series represented by this dataset includes student information that is restricted for 80 years from the date of record creation, and Harvard University records that are restricted for 50 years from the date of record creation. Researchers should contact Public Services for more information. The Steven Lawrence Gortmaker papers were processed with grant funding from the Andrew W. Mellon Foundation, as awarded and administered by the Council on Library and Information Resources (CLIR) in 2016. View the Steven Lawrence Gortmaker Papers finding aid for a full collection inventory of the records, and for more information about accessing and using the collection.

This paper investigates the optimal approach for predicting corporate bankruptcy risk within the context of Vietnam based on financial ratios. As a unique dataset of listed Vietnamese firms from 2010 to 2021 is employed, we confirm that machine learning models for bankruptcy prediction significantly surpass the traditional logistic regression. In addition, our dataset is divided into two subsets for training and testing models with proportions of 75% and 25%, respectively. The results demonstrate that the XGBoost and Random Forest techniques are superior to K-Nearest Neighbor and Logistic Regression in forecasting failure in both periods. Notably, our paper reveals that the predictive performance was slightly decreased compared to the two periods, and the forecasting after one year is higher than two years ahead.