Abstract

This collection contains plain text transcripts of therapy sessions, associated reference works and published narratives, memoirs, and other client narratives. Documents are sourced from Alexander Street Press' Counseling and Psychotherapy Transcripts: Volume I - Counseling and Psychotherapy Transcripts, Client Narratives, and Reference Works. The transcripts of therapy and counseling sessions illustrate a range of therapeutic techniques and practices. The pairing of these transcripts with associated reference works and published narratives, memoirs, and other client narratives enhances the collection's ability to provide insight into the experience of those undergoing therapy and living with mental illness. The dataset contains more than 2,000 session transcripts, 44,000 pages of client narratives, and 25,000 pages of secondary reference material.

Usage

For a complete list of materials, please see PSYC ESR with texts_QA completed by WS 1.7.21.xlsx (under Supporting files).

Under the **Files **tab:

• **Books/ **contains the plain text of books.

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• Transcripts/ contains the plain text of therapy transcripts.

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• **Raw Files/ **contains, where available, the original, scanned copies of books or therapy transcripts, usually as a pdf or series of jpgs.

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Under the **Tables **tab:

• Filter files by relevant features (e.g. School_of_Therapy, Symptoms) in the **Publication Metadata **table.
• Join the **Publication Metadata **extraction with the file indices to select files for export and analysis.

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The Radiotherapy Optimisation Test Set (TROTS) is an extensive set of problems originating from radiotherapy (radiation therapy) treatment planning. This dataset is created for 2 purposes: (1) to supply a large-scale dense dataset to measure performance and quality of mathematical solvers, and (2) to supply a dataset to investigate the multi-criteria optimisation and decision-making nature of the radiotherapy problem. The dataset contains 120 problems (patients), divided over 6 different treatment protocols/tumour types. Each problem contains numerical data, a configuration for the optimisation problem, and data required to visualise and interpret the results. The data is stored as HDF5 compatible Matlab files, and includes scripts to work with the dataset.

The set as present in this version is of date 13 May 2019. Updated versions of the Scripts and other extensions can be found at the following pages:

Persistent page with links: Erasmus University Rotterdam Library

Mirror project page: TROTS Mirror

Main publication: S. Breedveld & B. Heijmen, Data for TROTS - The Radiotherapy Optimisation Test Set, Data in Brief 12 (2017) 143-149

This study compared the therapeutic working alliance in brief counseling using two delivery methods: synchronous video delivery and in-person delivery. The alliance was measured using the Working Alliance Inventory (client version). Participants were 49 undergraduate college students between the ages of 18 and 22. Solution-Focused Brief Therapy was the treatment protocol, and the study used a randomized, controlled design. Welch’s t-tests and non-inferiority analyses were conducted, in addition to hierarchical regression analyses to examine the predictive value of the working alliance on post-treatment anxiety. Non-inferiority statistical analyses indicated that there was no statistically significant difference in working alliance for online delivery compared to in-person. A hierarchical regression anaIysis suggests that the therapeutic working alliance contributed to anxiety treatment outcomes for college student participants., Dataset was collected through pre and post Working Alliance Inventory assessments, as well as Beck's Anxiety Inventory assessments, in Qualtrics. Data was exported to SPSS where Welch's t-tests and non-inferiority analyses were conducted, as well as hierarchical regression analyses.Dataset was managed and accessible to authors only., , # Therapeutic Working Alliance in Brief Therapy with College Students: In-Person versus Telemental Health

https://doi.org/10.5061/dryad.b2rbnzspt

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This dataset includes responses of participants on the Beck’s Anxiety Inventory BEFORE the intervention, AFTER the intervention (3 solution-focused brief therapy counseling sessions) and at 3-week follow-up after end of the study. It also includes the results of the Working Alliance Inventory after one counseling session and after the final, 3rd counseling session. In addition, the delivery of counseling (whether in-person TAU or telemental health-experimental) is indicated. Finally, we looked at scores on the Counseling Center Assessment of Psychological Symptoms (CCAPS) re: anxiety. This allowed us to compare both the working alliance between in-person and telemental health and also the efficacy (changes in Beck’s anxiety scores) using t-tests and regression analyses.

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This is an auto-generated index table corresponding to a folder of files in this dataset with the same name. This table can be used to extract a subset of files based on their metadata, which can then be used for further analysis. You can view the contents of specific files by navigating to the "cells" tab and clicking on an individual file_kd.

The Improving Access to Psychological Therapies (IAPT) data set is a regular return of data generated by providers of NHS-commissioned IAPT services in England (including services provided by independent organisations). It was mandated as a monthly return from 1 April 2012 and collects details of all people accessing these services. Submissions are received by the Health and Social Care Information Centre as record-level anonymised data from patient-administration systems.

This publication comprises national and provider level data quality measures of some key data items in the IAPT data set: postcode; birthdate; gender; ethnicity; general medical practice code; NHS number; religious belief; sexual orientation; presence of a long-term physical health condition; provisional diagnosis; source of referral; Patient Health Questionnaire (PHQ 9) score; Generalised Anxiety Disorder (GAD) score; Work and Social Adjustment Scale score; appointment type; therapy type; disability. These measures are provided as both counts and percentages of all eligible IAPT records and will be of interest to stakeholders (e.g. the Department of Health and service commissioners), data providers and users of our statistics (e.g. mental health organisations including charities, and service users and their representatives).

Amendments to the construction of some of the data quality measures, previously agreed with the Department of Health, continue to be published in this release. These revisions affect the following measures: DQM26 Patient Health Questionnaire (PHQ 9) Score; DQM27 Generalised Anxiety Disorder (GAD 7) Score; DQM28 Work and Social Adjustment Scale Score; DQM29 Appointment Type; DQM30 Therapy type.

The rules for the Data Quality Measures can be found in the Related links section of this publication, and any revisions will be documented.

General Notes

The Health and Social Care Information Centre does not presently have permission to hold person-identifiable data. It is therefore only been possible to validate person-identifiable data items such as NHS number, Postcode and Date of Birth by using data items which have been derived from these items (as specified in the validation rules).

Since October 2013 the monthly file also contains activity data detailing the number of referrals received, entering treatment and completing treatment in the month, by CCG of GP Practice.

Since January 2015 this data has been subsumed into a general IAPT dataset available at https://data.gov.uk/dataset/improving-access-to-psychological-therapies-iapt-monthly-dataset.

Identification of risk factors of treatment resistance may be useful to guide treatment selection, avoid inefficient trial-and-error, and improve major depressive disorder (MDD) care. We extended the work in predictive modeling of treatment resistant depression (TRD) via partition of the data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) cohort into a training and a testing dataset. We also included data from a small yet completely independent cohort RIS-INT-93 as an external test dataset. We used features from enrollment and level 1 treatment (up to week 2 response only) of STAR*D to explore the feature space comprehensively and applied machine learning methods to model TRD outcome at level 2. For TRD defined using QIDS-C16 remission criteria, multiple machine learning models were internally cross-validated in the STAR*D training dataset and externally validated in both the STAR*D testing dataset and RIS-INT-93 independent dataset with an area under the receiver operating characteristic curve (AUC) of 0.70–0.78 and 0.72–0.77, respectively. The upper bound for the AUC achievable with the full set of features could be as high as 0.78 in the STAR*D testing dataset. Model developed using top 30 features identified using feature selection technique (k-means clustering followed by χ2 test) achieved an AUC of 0.77 in the STAR*D testing dataset. In addition, the model developed using overlapping features between STAR*D and RIS-INT-93, achieved an AUC of > 0.70 in both the STAR*D testing and RIS-INT-93 datasets. Among all the features explored in STAR*D and RIS-INT-93 datasets, the most important feature was early or initial treatment response or symptom severity at week 2. These results indicate that prediction of TRD prior to undergoing a second round of antidepressant treatment could be feasible even in the absence of biomarker data.

This paper estimates the dynamic returns to job training. We posit a model of sequential training participation, where decisions and outcomes depend on observed and unobserved characteristics. We analyze different treatment effects, including policy relevant parameters, and link them to continuation values and latent skills. The empirical analysis exploits administrative data combining job training records, matched employee-employer information, and pre-labor market ability measures from Chile. Although the average returns to training are small, these vary across the unobserved ability distribution and previous training choices. In fact, among young workers, the returns to training are lower when followed by additional training, providing evidence of dynamic substitutability. Policy experiments illustrate how increasing the local availability of training programs may affect earnings heterogeneously across dynamic responses.

The Criminal Justice Drug Abuse Treatment Studies 2 (CJ-DATS 2) was launched in 2008 with a focus on conducting implementation research in criminal justice settings. NIDA's ultimate goal for CJ-DATS 2 was to identify implementation strategies that maximize the likelihood of sustained delivery of evidence-based practices to improve offender drug abuse and HIV outcomes, and to decrease their risk of incarceration. The Medication-Assisted Therapy (MAT) study focuses on implementing linkages to medication assisted treatment in correctional settings. During the study period community corrections staff engaged in training about addiction pharmacotherapies, while leadership in the corrections and treatment facilities engage in a joint strategic planning process to identify and resolve barriers to efficient flow of clients across the two systems. This study includes 28 datasets and over 1,400 variables. The first five datasets for this study contain data on the baseline characteristics of the treatment and corrections sites that participated in the study as well as the characteristics of the staff working at those facilities. Opinions about Medication Assisted Treatment surveys were administered to personnel at the participating corrections and treatment sites (D6). Data on Inter-organization Relations between Probation and Parole staff with Treatment Providers were also collected (DS7-DS18). Information was extracted from the charts of clients about their alcohol and opioid dependence as well as the referrals and treatment the clients received (DS19). Probation and parole officers and treatment providers were surveyed about monthly counts of referrals (DS20-DS21). During the study 10 staff members from the community corrections agency and local treatment providers where MAT services were available were nominated to participate in a Pharmacotherapy Exchange Council (PEC). PEC members were involved with strategic planning for implementing changes to improve the usage of Medication-Assisted Therapy. PEC members were surveyed several times throughout the study. PEC members completed surveys on how well the sites were adhering to the Organizational Linkages Intervention (OLI) process (DS22). Community corrections staff, PEC members and Connections Coordinators in the experimental group were surveyed about their perceptions of organizational benefits and costs associated with the MATICCE intervention (DS23). The PEC rated the Connections Coordinators (DS24)and the Connections Coordinators rate the PEC (DS25). PEC researchers completed surveys on how much of the OLI was completed (DS26) as well as what the sustainability of the changes made through the MATTICE project (DS27). The final dataset provides a key for who took the KPI (Key Performance Indicators) training and who was a PEC member (DS28).

The Improving Access to Psychological Therapies (IAPT) data set is a regular return of data generated by providers of NHS-commissioned IAPT services in England (including services provided by independent organisations). It was mandated as a monthly return from 1 April 2012 and collects details of all people accessing these services. Submissions are received by the Health and Social Care Information Centre as record-level anonymised data from patient-administration systems. This publication comprises national and provider level data quality measures of some key data items in the IAPT data set: postcode; birthdate; gender; ethnicity; general medical practice code; NHS number; religious belief; sexual orientation; presence of a long-term physical health condition; provisional diagnosis; source of referral; Patient Health Questionnaire (PHQ 9) score; Generalised Anxiety Disorder (GAD) score; Work and Social Adjustment Scale score; appointment type; therapy type; disability. These measures are provided as both counts and percentages of all eligible IAPT records and will be of interest to stakeholders (e.g. the Department of Health and service commissioners), data providers and users of our statistics (e.g. mental health organisations including charities, and service users and their representatives). Amendments to the construction of some of the data quality measures, previously agreed with the Department of Health, continue to be published in this release. These revisions affect the following measures: DQM26 Patient Health Questionnaire (PHQ 9) Score; DQM27 Generalised Anxiety Disorder (GAD 7) Score; DQM28 Work and Social Adjustment Scale Score; DQM29 Appointment Type; DQM30 Therapy type. The rules for the Data Quality Measures can be found in the Related links section of this publication, and any revisions will be documented. General Notes The Health and Social Care Information Centre does not presently have permission to hold person-identifiable data. It is therefore only been possible to validate person-identifiable data items such as NHS number, Postcode and Date of Birth by using data items which have been derived from these items (as specified in the validation rules). Since October 2013 the monthly file also contains activity data detailing the number of referrals received, entering treatment and completing treatment in the month, by CCG of GP Practice. Since January 2015 this data has been subsumed into a general IAPT dataset available at https://data.gov.uk/dataset/improving-access-to-psychological-therapies-iapt-monthly-dataset.

Publicly available data and code for "Working Remotely? Selection, Treatment and the Market for Remote Work"How does remote work affect productivity and how productive are workers who choose remote jobs? We decompose these effects in a Fortune 500 firm. Before Covid-19, remote workers answered 12% fewer calls per hour than on-site workers. After the offices closed, the productivity gap narrowed by 4%, and formerly on-site workers’ call quality and promotion rates also declined. Even with everyone remote, an 8% productivity gap persisted, indicating negative selection into remote jobs. A cost-benefit analysis indicates that the savings from remote work in reducing turnover and office rents could outweigh remote work's negative productivity impact but not the costs of attracting less productive workers.

The goal of this project was to compare the effectiveness of a 26-week stages of change (SOC) group treatment approach with a standard Cognitive-Behavioral Therapy Gender-Reeducation (CBTGR) group treatment approach; to assess potential mediators of change; to conduct analyses on individual readiness to change as a moderator of treatment condition in predicting outcomes; to conduct exploratory analyses comparing the effectiveness of these 2 approaches in Spanish-speaking groups; and to assess the integrity of the 2 treatments with respect to therapist adherence. Male clients who were referred to the Montgomery County, Maryland, Abused Persons Program (APP) between June 2003 and January 2006 and who were appropriate for participation in either the English-speaking or Spanish-speaking 26-week group, were randomly assigned to either a Stage of Change (SOC) Treatment Format or a Cognitive-Behavioral Gender-Reeducation Format (CBTGR). All participants at the APP routinely underwent a standard intake procedure. Data collection consisted of (1) an intake interview and questionnaires completed by the batterer at intake, (2) an initial telephone interview of the partner, (3) data collected from the batterer at mid-treatment and post-treatment, (4) data collected at the end of treatment on the number of sessions attended, and (5) telephone-based follow-up information received from the partner at 6 and 12 months post-intake. The data file contains 550 cases and 901 variables. For the Abuser Intake Interview, the abuser was asked information regarding his age, education, employment status, income, relationship to the victim partner, current contact, children in common, and history of abuse and trauma. As part of this intake, the offender completed several instruments including (1) the Conflict Tactics Scales-Revised (CTS2), (2) the University of Rhode Island Change Assessment (URICA), (3) the Personality Assessment Inventory (PAI), (4) the Alcohol Use Disorders Identification Test (AUDIT), (5) the Generality of Violence-Revised (GVQ-R), (6) Perceptions of Procedural Justice, and (7) the Dissociative Violence Scale (DVS). The victim partner was asked about demographics as well as relationship status, children in common, and current contact with the batterer. As part of this interview, the victim partner also completed (1) the CTS2 items as they pertained to the batterer's behavior toward her in the previous six months and over the course of their relationship, (2) the Danger Assessment Scale (DAS), and (3) the Process of Change in Abused Women Scale (PROCAWS). At 8 and 16 weeks into treatment, APP staff administered the Working Alliance Inventory -- Short Form (WAI-S) along with the Group Cohesion Scale (GES-COH).

The Improving Access to Psychological Therapies (IAPT) data set is a regular return of data generated by providers of NHS-commissioned IAPT services in England (including services provided by independent organisations). It was mandated as a monthly return from 1 April 2012 and collects details of all people accessing these services. Submissions are received by the Health and Social Care Information Centre as record level anonymised data from patient-administration systems. This publication comprises national- and provider-level data quality measures of some key data items in the IAPT dataset: postcode; birthdate; gender; ethnicity; general medical practice code; NHS number; religious belief; sexual orientation; presence of a long-term physical health condition; provisional diagnosis; source of referral; Patient Health Questionnaire (PHQ 9) score; Generalised Anxiety Disorder (GAD) score; Work and Social Adjustment Scale score; appointment type; therapy type; disability. These measures are provided as both counts and percentages of all eligible IAPT records and will be of interest to stakeholders (e.g. the Department of Health and service commissioners), data providers and users of our statistics (e.g. mental health organisations including charities, and service users and their representatives). The Information Standards Notice for the IAPT data set can be found on the Information Standards Board website. Note that amendments to the construction of some data quality measures, agreed with the Department of Health, have been implemented in this release. These revisions affect the following measures: DQM26 Patient Health Questionnaire (PHQ 9) Score; DQM27 Generalised Anxiety Disorder (GAD 7) Score; DQM28 Work and Social Adjustment Scale Score; DQM29 Appointment Type; DQM30 Therapy type. The rules for the Data Quality Measures can be found in the links below, and any revisions will be documented. General Notes The Health and Social Care Information Centre does not presently have permission to hold person-identifiable data. It is therefore only been possible to validate person-identifiable data items such as NHS number, Postcode and Date of Birth by using data items which have been derived from these items (as specified in the validation rules).

A Gold Standard Corpus for Activity Information

Dataset Title: A Gold Standard Corpus for Activity Information (GoSCAI)

Dataset Curators: The Epidemiology & Biostatistics Section of the NIH Clinical Center Rehabilitation Medicine Department

Dataset Version: 1.0 (May 16, 2025)

Dataset Citation and DOI: NIH CC RMD Epidemiology & Biostatistics Section. (2025). A Gold Standard Corpus for Activity Information (GoSCAI) [Data set]. Zenodo. doi: 10.5281/zenodo.15528545

EXECUTIVE SUMMARY

This data statement is for a gold standard corpus of de-identified clinical notes that have been annotated for human functioning information based on the framework of the WHO's International Classification of Functioning, Disability and Health (ICF). The corpus includes 484 notes from a single institution within the United States written in English in a clinical setting. This dataset was curated for the purpose of training natural language processing models to automatically identify, extract, and classify information on human functioning at the whole-person, or activity, level.

CURATION RATIONALE

This dataset is curated to be a publicly available resource for the development and evaluation of methods for the automatic extraction and classification of activity-level functioning information as defined in the ICF. The goals of data curation are to 1) create a corpus of a size that can be manually deidentified and annotated, 2) maximize the density and diversity of functioning information of interest, and 3) allow public dissemination of the data.

LANGUAGE VARIETIES

Language Region: en-US

Prose Description: English as written by native and bilingual English speakers in a clinical setting

LANGUAGE USER DEMOGRAPHIC

The language users represented in this dataset are medical and clinical professionals who work in a research hospital setting. These individuals hold professional degrees corresponding to their respective specialties. Specific demographic characteristics of the language users such as age, gender, or race/ethnicity were not collected.

ANNOTATOR DEMOGRAPHIC

The annotator group consisted of five people, 33 to 76 years old, including four females and one male. Socioeconomically, they came from the middle and upper-middle income classes. Regarding first language, three annotators had English as their first language, one had Chinese, and one had Spanish. Proficiency in English, the language of the data being annotated, was native for three of the annotators and bilingual for the other two. The annotation team included clinical rehabilitation domain experts with backgrounds in occupational therapy, physical therapy, and individuals with public health and data science expertise. Prior to annotation, all annotators were trained on the specific annotation process using established guidelines for the given domain, and annotators were required to achieve a specified proficiency level prior to annotating notes in this corpus.

LINGUISTIC SITUATION AND TEXT CHARACTERISTICS

The notes in the dataset were written as part of clinical care within a U.S. research hospital between May 2008 and November 2019. These notes were written by health professionals asynchronously following the patient encounter to document the interaction and support continuity of care. The intended audience of these notes were clinicians involved in the patients' care. The included notes come from nine disciplines - neuropsychology, occupational therapy, physical medicine (physiatry), physical therapy, psychiatry, recreational therapy, social work, speech language pathology, and vocational rehabilitation. The notes were curated to support research on natural language processing for functioning information between 2018 and 2024.

PREPROCESSING AND DATA FORMATTING

The final corpus was derived from a set of clinical notes extracted from the hospital electronic medical record (EMR) for the purpose of clinical research. The original data include character-based digital content originally. We work in ASCII 8 or UNICODE encoding, and therefore part of our pre-processing includes running encoding detection and transformation from encodings such as Windows-1252 or ISO-8859 format to our preferred format.

On the larger corpus, we applied sampling to match our curation rationale. Given the resource constraints of manual annotation, we set out to create a dataset of 500 clinical notes, which would exclude notes over 10,000 characters in length.

To promote density and diversity, we used five note characteristics as sampling criteria. We used the text length as expressed in number of characters. Next, we considered the discipline group as derived from note type metadata and describes which discipline a note originated from: occupational and vocational therapy (OT/VOC), physical therapy (PT), recreation therapy (RT), speech and language pathology (SLP), social work (SW), or miscellaneous (MISC, including psychiatry, neurology and physiatry). These disciplines were selected for collecting the larger corpus because their notes are likely to include functioning information. Existing information extraction tools were used to obtain annotation counts in four areas of functioning and provided a note’s annotation count, annotation density (annotation count divided by text length), and domain count (number of domains with at least 1 annotation).

We used stratified sampling across the 6 discipline groups to ensure discipline diversity in the corpus. Because of low availability, 50 notes were sampled from SLP with relaxed criteria, and 90 notes each from the 5 other discipline groups with stricter criteria. Sampled SLP notes were those with the highest annotation density that had an annotation count of at least 5 and a domain count of at least 2. Other notes were sampled by highest annotation count and lowest text length, with a minimum annotation count of 15 and minimum domain count of 3.

The notes in the resulting sample included certain types of PHI and PII. To prepare for public dissemination, all sensitive or potentially identifying information was manually annotated in the notes and replaced with substituted content to ensure readability and enough context needed for machine learning without exposing any sensitive information. This de-identification effort was manually reviewed to ensure no PII or PHI exposure and correct any resulting readability issues. Notes about pediatric patients were excluded. No intent was made to sample multiple notes from the same patient. No metadata is provided to group notes other than by note type, discipline, or discipline group. The dataset is not organized beyond the provided metadata, but publications about models trained on this dataset should include information on the train/test splits used.

All notes were sentence-segmented and tokenized using the spaCy en_core_web_lg model with additional rules for sentence segmentation customized to the dataset. Notes are stored in an XML format readable by the GATE annotation software (https://gate.ac.uk/family/developer.html), which stores annotations separately in annotation sets.

CAPTURE QUALITY

As the clinical notes were extracted directly from the EMR in text format, the capture quality was determined to be high. The clinical notes did not have to be converted from other data formats, which means this dataset is free from noise introduced by conversion processes such as optical character recognition.

LIMITATIONS

Because of the effort required to manually deidentify and annotate notes, this corpus is limited in terms of size and representation. The curation decisions skewed note selection towards specific disciplines and note types to increase the likelihood of encountering information on functioning. Some subtypes of functioning occur infrequently in the data, or not at all. The deidentification of notes was done in a manner to preserve natural language as it would occur in the notes, but some information is lost, e.g. on rare diseases.

METADATA

Information on the manual annotation process is provided in the annotation guidelines for each of the four domains:

- Communication & Cognition (https://zenodo.org/records/13910167)

- Mobility (https://zenodo.org/records/11074838)

- Self-Care & Domestic Life (SCDL) (https://zenodo.org/records/11210183)

- Interpersonal Interactions & Relationships (IPIR) (https://zenodo.org/records/13774684)

Inter-annotator agreement was established on development datasets described in the annotation guidelines prior to the annotation of this gold standard corpus.

The gold standard corpus consists of 484 documents, which include 35,147 sentences in total. The distribution of annotated information is provided in the table below.

A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other data types. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic mechanistic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal population-size data. We demonstrate that the explicit inclusion of the transcriptomic information in the parameter estimation is critical for identification of the model parameters and enables accurate prediction of new treatment regimens. Inclusion of the transcriptomic data improves predictive accuracy in new treatment response dynamics with a concordance correlation coefficient (CCC) of 0.89 compared to a prediction accuracy of CCC = 0.79 without integration of the single cell RNA sequencing (scRNA-seq) data directly into the model calibration. To the best our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with longitudinal treatment response data into a mechanistic mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multimodal data sets into identifiable mathematical models to develop optimized treatment regimens from data. Single cell RNA-seq of MDA-MB-231 cell line with chemotherapy treatment

The Improving Access to Psychological Therapies (IAPT) data set is a regular return of data generated by providers of NHS-commissioned IAPT services in England (including services provided by independent organisations). It was mandated as a monthly return from 1 April 2012 and collects details of all people accessing these services. Submissions are received by the Health and Social Care Information Centre as record level anonymised data from patient-administration systems. This publication comprises national- and provider-level data quality measures of some key data items in the IAPT dataset: postcode; birthdate; gender; ethnicity; general medical practice code; NHS number; religious belief; sexual orientation; presence of a long-term physical health condition; provisional diagnosis; source of referral; Patient Health Questionnaire (PHQ 9) score; Generalised Anxiety Disorder (GAD) score; Work and Social Adjustment Scale score; appointment type; therapy type; disability. These measures are provided as both counts and percentages of all eligible IAPT records and will be of interest to stakeholders (e.g. the Department of Health and service commissioners), data providers and users of our statistics (e.g. mental health organisations including charities, and service users and their representatives). Read more about the IAPT dataset The Information Standards Notice for the IAPT data set can be found on the Information Standards Board website. Note that amendments to the construction of some data quality measures, agreed with the Department of Health, have been implemented in this release. These revisions affect the following measures: DQM26 Patient Health Questionnaire (PHQ 9) Score; DQM27 Generalised Anxiety Disorder (GAD 7) Score; DQM28 Work and Social Adjustment Scale Score; DQM29 Appointment Type; DQM30 Therapy type. The rules for the Data Quality Measures can be found in the following link and any revisions will be documented on the following page: www.hscic.gov.uk/iapt General Notes The Health and Social Care Information Centre does not presently have permission to hold person-identifiable data. It is therefore only been possible to validate person-identifiable data items such as NHS number, Postcode and Date of Birth by using data items which have been derived from these items (as specified in the validation rules).

The Estonian Drug Treatment Database is a state register which is kept on the people who have started drug treatment. The Drug Treatment Database started its work on January 1, 2008.

Collection and processing of data on these people is necessary for getting an overview on occurrence of mental and behavioural disorders related to drug use, as well as for organising of relevant health services and planning of drug abuse preventive actions. Health care institutions holding a psychiatry authorization in Estonia present data to the database if they are turned to by a patient who is diagnosed with a mental and behavioural disorder due to drug use.

On the basis of the database's data, an annual overview is compiled, giving information about drug addicts who have turned to drug treatment in the previous calendar year, about the health service provided, the patients' socio-economic background, drug use and the related risk behaviour.

The data on the Drug Treatment Database are also submitted to the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) and United Nations Office on Drugs and Crime (UNODC).

The data are part of Substance Abuse Treatment series, which comprises datasets that can be used in both quantitative and qualitative research. The respondents were drug and alcohol counsellors who worked in inpatient treatment facilities and who had a minimum of one year's experience in substance abuse treatment. The data consist of written responses to five different frame stories / vignettes. The respondents were presented with five different hypothetical situations in which clients told about their problems or asked for advice. The respondents were requested to write down how they would proceed and what they would say to the clients. The frame stories were part of a questionnaire, which is available as a separate dataset (FSD2690). The respondents received the frame stories with the questionnaire, but they were given two weeks to respond to them. 97 out of 162 respondents who had responded to the questionnaire (FSD2690) also responded to the vignettes.

Comparison of current substance use among US adults stratified by legality-based substance use disorder type.

Dataset on chemotherapeutic drug responses in TCGA cancer patients, cross-referenced for a hit in TCIA.at database, consisting of clinical (TCGA), cancer tissue gene-expression (TCGA) and tumor-immunome (TCIA) features. The dataset consists of 5 common chemotherapy agents, 3 CRC agents (FOLFOX, 5FU, Oxaliplatin) and 2 Lung agents (Carboplatin, Cisplatin). FOLFOX as a combinational therapy or regimen, was compiled from timings of monotherapies given to patients and as such is a novel dataset derived from TCGA data. FOLFOX dataset is primarily firstline treatment, while other drugs are not to be interpreted as firstline treatments. Drug datasets are individually available in own CSV files.

Citation Dalibor Hrg, Balthasar Huber, Lukas A. Huber. (2020). TCGA Chemotherapy Response Dataset. Zenodo. http://doi.org/10.5281/zenodo.3719291

The results here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.

License CC BY-SA 4.0 International https://creativecommons.org/licenses/by-sa/4.0. Authors take no liability for any use of this data.

Contributions D. Hrg and B. Huber acknowledge major and equal work effort: data understanding, data science and dataset preparation (monotherapies and FOLFOX); L. A. Huber: help with dictionary of drug names and curration/cleaning of FOLFOX entries, clinical validation.

Contact & Maintenance dalibor.hrg@gmail.com dalibor.hrg@i-med.ac.at

Abstract The community health agents are an important element in the management of medication information in primary healthcare setting, improving healthcare team interaction with the community. The aim of this study was to reveal how the use of medicines is present in the routine of agentes and understand the relationships established between agents, users and healthcare team. This is a quantitative and qualitative study, using participant observation, semi-structured interview and focus group. This study was conducted at three basic health units in a municipality of Minas Gerais, Brazil, between March, 2013 and February, 2015. The results show that agents constantly relate with patients who experience drug therapy problems and have doubts about the indication, effects, adverse drug reactions, among other. They perform some interventions directly with the patient, but usually they prefer to refer cases to the healthcare team. Some agents said recognizing the pharmacist as a reference on medicines, but the demand for this professional was low by the subjects of this study. We emphasize the importance of the agents training on medication use to instrumentalize them to recognize problematic situations, to develop interventions with the support of the healthcare team and to follow up patients using medicines.