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583 results for “prescription”
The Effect of Prescription Drug Monitoring Programs (PDMPs) and Overdose Reversal Drug Accessibility Laws on Opioid Analgesic Mortality in the United States
<p><strong>Purpose: </strong>This analysis focuses on the impact of Prescription Drug Monitoring Programs (PDMPs) and increased layperson access to the overdose reversal drug Naloxone on prescription opioid mortality rates. The prescription opioid mortality rate was analyzed against state laws governing PDMPs and Naloxone accessibility to laypersons to evaluate if there was a correlation between mortality reduction and implementation of the laws.</p> <p>Three main analyses were conducted:</p> <ol> <li>Does a state’s opioid mortality reduction correlate to an effective PDMP and/or Naloxone law?</li> <li>Do states with strong PDMP laws have a corresponding opioid overdose mortality rate reduction?</li> <li>Do states with strong Naloxone accessibility laws have a corresponding opioid overdose mortality rate reduction?</li> </ol> <p><strong>Conclusion:</strong> Patterns show that PDMP and Naloxone accessibility can be successful in reducing mortality rates, but implementations and results vary dramatically between states. Further changes to both PDMPs and Naloxone accessibility are needed for states to see reliable and consistent mortality reductions. Changes to regulations need time to implement and take effect, meaning longer term measurements and data will be required to see if positive impacts can be sustained.</p> <ol> </ol> <p><strong>Data and Datasets:</strong></p> <p>There are three main datasets used in the analysis.</p> <ul> <li>Opioid mortality rate by state from 2006 to 2016. <ul> <li>This datasetis from the CDC website (<a href="https://www.cdc.gov/drugoverdose/data/statedeaths.html">https://www.cdc.gov/drugoverdose/data/statedeaths.html</a>). I used web scraping and regular expression search to extract the data and calculated mortality reduction of each state.</li> <li><em>Mortality reduction formula: (Highest mortality rate) – (2016 mortality rate)</em><br> </li> </ul> </li> <li>PDMP law implementation in each state and its timelinefrom January 1, 1998 to July 1, 2016. <ul> <li>Dataset is from the Prescription Drug Abuse Policy System(<a href="http://pdaps.org./datasets/prescription-monitoring-program-laws-1408223332-1502818372">http://pdaps.org./datasets/prescription-monitoring-program-laws-1408223332-1502818372</a>). This dataset encompasses laws regulating which professions have access to the database and for what purpose, whether practitioners can delegate their access, whether patients can see their own information, and the extent to which access to individually-identified records may be granted for law enforcement purposes.</li> <li>This dataset is not quantitative, but a set of questions on the characteristics of the laws governing the PDMP.I performed the following data preparation to allow for analysis and visualization:</li> <li><em>PDMP law effectiveness calculation:Each question with a “Yes” answer adds one point to the law’s cumulative effectiveness score on the year it was implemented.</em></li> </ul> </li> </ul> <ul> <li>Naloxone accessibility to laypersons law implementation in each state and its timeline from January 1, 2001 to December 31, 2016. <ul> <li>Dataset is also from the Prescription Drug Abuse Policy System (<a href="http://pdaps.org./datasets/laws-regulating-administration-of-naloxone-1501695139">http://pdaps.org./datasets/laws-regulating-administration-of-Naloxone-1501695139</a>).This dataset focuses on state laws that provide civil or criminal immunity to licensed healthcare providers or lay responders for opioid antagonist administration.</li> <li>This dataset is not quantitative, but a set of questions on the characteristics of the laws governing Naloxone accessibility to laypersons. I performed the following data preparation to allow for analysis and visualization:</li> <li><em>Naloxone accessibility law effectiveness calculation:Each question with a “Yes” answer adds one point to the law’s cumulative effectiveness score on the year it was implemented.</em></li> </ul> </li> </ul>
Can accurate demographic information about people who use prescription medications non-medically be derived from Twitter?
<p>This archive contains over 3 billion Tweet IDs associated with the paper:<br>"Can accurate demographic information about people who use prescription medications non-medically be derived from Twitter?"</p> <p>The data provides:<br>X (Twitter) IDs of posts included in the study. The IDs can be used to retrieve the original posts via the Twitter API. Posts removed by the original subscribers or whose visibilities are no longer public cannot be retrieved by the poster. </p> <p>Full citation:<br>Yang YC, Al-Garadi MA, Love JS, Cooper HLF, Perrone J, Sarker A. Can accurate demographic information about people who use prescription medications nonmedically be derived from Twitter? Proc Natl Acad Sci U S A. 2023 Feb 21;120(8):e2207391120. doi: 10.1073/pnas.2207391120. Epub 2023 Feb 14. PMID: 36787355; PMCID: PMC9974473.</p> <p>Python scripts related to the analysis are available as supplementary material with the paper. </p> <p>Contact: <br>Abeed Sarker<br>abeed@dbmi.emory.edu</p> <p>Funding:<br>National Institute on Drug Abuse (R01DA057599).</p>
Dataset from: Binary population synthesis with probabilistic remnant mass and kick prescriptions
<p>This contains the data behind the paper "<a href="https://ui.adsabs.harvard.edu/abs/2021MNRAS.500.1380M">Binary population synthesis with probabilistic remnant mass and kick prescriptions</a>"<br> <br> See<a href="https://github.com/TeamCOMPAS/COMPAS"> https://github.com/TeamCOMPAS/COMPAS</a> for code information and instructions on parsing the data.</p>
Evaluation of Medical Cannabis and Prescription Opioid Taper Support for Reduction of Pain and Opioid Dose in Patients With Chronic Non-Cancer Pain
ClinicalTrials.gov study NCT04827992. IPD Sharing: YES. Countries: 1. Publications: 1.
Study of Dupilumab for the Treatment of Patients With Prurigo Nodularis, Inadequately Controlled on Topical Prescription Therapies or When Those Therapies Are Not Advisable (LIBERTY-PN PRIME)
ClinicalTrials.gov study NCT04183335. IPD Sharing: YES. Countries: 8. Publications: 3.
Study of Dupilumab for the Treatment of Patients With Prurigo Nodularis, Inadequately Controlled on Topical Prescription Therapies or When Those Therapies Are Not Advisable (PRIME2)
ClinicalTrials.gov study NCT04202679. IPD Sharing: YES. Countries: 11. Publications: 4.
Gait analysis results for different pediatric prosthetic knee prescription protocols
<p>Traditionally, children who need a prosthetic knee joint are not provided with one in their first prosthesis. An alternative Early Knee protocol provides a flexing knee in the first prosthesis. The purpose of this multi-site study was to examine kinematic outcomes during walking in separate groups of young children in an Early prosthetic knee prescription protocol (EK) versus a traditional prosthetic knee protocol (TK), along with a population of children without lower limb amputations. Eighteen children aged 12 months to five years were recruited for this study at two clinical sites, six in each of the three groups. Children in the two prosthesis groups had unilateral limb loss and had been treated at one site with the TK protocol or at another with the EK protocol. All children walked at self-selected speed while kinematic data were collected using similar Vicon motion analysis systems. Data include joint angles, extrema, and ranges of motion used for the determination of temporal and spatial gait parameters and swing-phase clearance adaptations. </p>
PxCorpus : A Spoken Drug Prescription Dataset in French for Spoken Language Understanding and Dialogue
<h2><strong>PxCorpus : A Spoken Drug Prescription Dataset in French</strong></h2><p> </p><p>PxCorpus is to the best of our knowledge, the first spoken medical drug prescriptions corpus to be distributed.</p><p>It contains 4 hours of transcribed and annotated dialogues of drug prescriptions in French acquired through an experiment with 55 participants experts and non-experts in drug prescriptions.</p><p> </p><p>The automatic transcriptions were verified by human effort and aligned with semantic labels to allow training of NLP models. The data acquisition protocol was reviewed by medical experts and permit free distribution without breach of privacy and regulation.</p><p> </p><h3>Overview of the Corpus</h3><p>The experiment has been performed in wild conditions with naive participants and medical experts.</p><p>In total, the dataset includes 2067 recordings of 55 participants (38% non-experts, 25% doctors, 36% medical practitioners), manually transcribed and semantically annotated.</p><p> </p><p>| Category | Sessions | Recordings | Time(m)|</p><p>|-----------------------| ------------- | --------------- | ----------- |</p><p>| Medical experts | 258 | 434 | 94.83 |</p><p>| Doctors | 230 | 570 | 105.21 |</p><p>| Non experts | 415 | 977 | 62.13 |</p><p>| Total | 903 | 1981 | 262.27 |</p><p> </p><h3>License</h3><p>We hope that that the community will be able to benefit from the dataset which is distributed with an attribution 4.0 International (CC BY 4.0) Creative Commons licence.</p><p> </p><h3>How to cite this corpus</h3><p> </p><p>If you use the corpus or need more details please refer to the following paper: A spoken drug prescription datset in French for spoken Language Understanding</p><p> </p><p>@InProceedings{Kocabiyikoglu2022,</p><p> author = "Alican Kocabiyikoglu and Fran{\c c}ois Portet and Prudence Gibert and Hervé Blanchon and Jean-Marc Babouchkine and Gaëtan Gavazzi",</p><p> title = "A spoken drug prescription datset in French for spoken Language Understanding",</p><p> booktitle = "13th Language Ressources and Evaluation Conference (LREC 2022)",</p><p> year = "2022",</p><p> location = "Marseille, France"</p><p>}</p><p>a more complete description of the corpus acquisition is available on arxiv </p><p>@misc{kocabiyikoglu2023spoken,</p><p> title={Spoken Dialogue System for Medical Prescription Acquisition on Smartphone: Development, Corpus and Evaluation},</p><p> author={Ali Can Kocabiyikoglu and François Portet and Jean-Marc Babouchkine and Prudence Gibert and Hervé Blanchon and Gaëtan Gavazzi},</p><p> year={2023},</p><p> eprint={2311.03510},</p><p> archivePrefix={arXiv},</p><p> primaryClass={cs.CL}</p><p>}</p><p> </p><h3>Project Structure</h3><p> </p><p>The project contains the following elements</p><p>.</p><p>├── LICENSE</p><p>├── PxDialogue/</p><p>├── PxSLU/</p><p>├── readme.md</p><p> </p><h3>PxSLU : Prescription Corpus for Spoken Language Understanding</h3><p> </p><h4>Directory Structure</h4><p>.</p><p>├── LICENSE</p><p>├── metadata.txt</p><p>├── paths.txt</p><p>├── PxSLU_conll.txt</p><p>├── readme.md</p><p>├── recordings</p><p>├── seq.in</p><p>├── seq.label</p><p>├── seq.out</p><p>├── Demo.ipynb</p><p>└── verifications.py</p><p> </p><h4>Recordings</h4><p> </p><p>The recordings directory contains the 903 recording sessions. Each session can contain several recordings. For instance,</p><p>the directory</p><p> recordings/J7aVvWb67L</p><p>contains the records </p><p> recording_0.wav recording_2.wav</p><p>which represent two attempts to record a drug prescription</p><p> </p><p>All records are stored as mono channel wav files of 16kHz 16bits signed PCM</p><p> </p><h4>Paths</h4><p> </p><p>contains the list of all the .wav files in the recordings directory</p><p>00MYcyVK0t/recording_0.wav</p><p>00MYcyVK0t/recording_2.wav</p><p>02Qp6ICj9Q/recording_0.wav</p><p>02Qp6ICj9Q/recording_1.wav</p><p>...</p><p> </p><p>All other files (metadata.txt, seq.*) refer to this list to describe the recording.</p><p> </p><h4>Metadata</h4><p> </p><p>contains the information about the participants:</p><p> </p><p>48,60+,F,non-expert</p><p>48,60+,F,non-expert</p><p>24,18–28,F,doctor</p><p>24,18–28,F,doctor</p><p>...</p><p> </p><p>The first column is the participant unique id, the second is the age range, the third is the gender and the final is the category of the participant in {doctor,expert, non-expert}. doctor correspond to a physician, (other)expert to a pharmacist or a biologist specialized in drugs while non-expert are other people not entering in these categories. The lines are synchronised with the paths.txt lines.</p><p> </p><h4>Labels</h4><p> </p><p>the three files seq.label, seq.in, seq.out represent respectivly the intent, the transcript and the entities in BIO format.</p><p> </p><p> seq.label | seq.in | seq.out</p><p>medical_prescription | flagyl 500 milligrammes euh qu/ en... | B-drug B-d_dos_val B-d_dos_up O O ...</p><p>medical_prescription | 3 comprimés par jour matin midi ... | B-dos_val B-dos_uf O O B-rhythm_tdte B-rhythm_tdte O B-rhythm_tdte ...</p><p> ... | ... | ...</p><p> </p><p>These lines are synchronised with the paths.txt lines.</p><p> </p><p>Another file "PxSLU_conll.txt" is provided in a format inspired by the conll format (https://universaldependencies.org/format.html). However, this one is *not* aligned with the acoustic records file paths.txt.</p><p> </p><h4>Scripts</h4><p> </p><p>verifications.py performs the checking of the alignement of all the seq.* paths.txt and metadata files. A user of the dataset does not need to use this script unless she plan to extend the datasets with her own data.</p><p>Demo.ipynb is a jupyter notebook that a user can run to search through the dataset. It is intended to let the user have a quicker and smoother view on the dataset.</p><p> </p><p> </p><h4>Data splits</h4><p>In the data_splits folder, you can find a data split of this dataset organized as following:</p><p>- train.txt: medical experts + non experts (80%) = 1128 samples</p><p>- dev.txt: medical experts + non experts (20%) = 283 samples</p><p>- test.txt: doctors (100%) = 570 samples</p><p> </p><p>Each file contains references to line numbers of the corpus. For example, first line of the test.txt is 904, seq.in file contains the utterance "nicopatch". Users can access the labels, slots, metadata using the same line number 904 in the parallel files (paths.txt,seq.out,seq.label,...).</p><p> </p><h3>PxDialogue : Prescription recording corpus for dialogue systems</h3><p> </p><p>PxDialogue corpus comes as an extension of the PxSLU corpus and provides additional information about the dialogues that was collected through spoken dialogue. This corpus includes two additional files:</p><p>├── events.txt</p><p>├── dialogue_annotations.txt</p><p> </p><p> </p><h4>Events.txt:</h4><p> </p><p>For each dialogue session, all dialogue events are given in this text file</p><p>which can be used to train/evaluate dialogue systems.</p><p> </p><p>Usage example:</p><p> </p><p>PxSLU (paths.txt)</p><p>- 00MYcyVK0t/recording_0.wav</p><p>- 00MYcyVK0t/recording_2.wav</p><p> </p><p>PxDialogue (events.txt)</p><p>- (-1, 'START', 'APP', None, 0) (1, 'user', 'ASR', 'flagyl 500 mg en cachet pendant 8 jours', 30) (1, 'system', 'TTS', 'Choisissez le médicament correspondant à votre recherche', 34) (2, 'user', 'UI', 'listview_item_clicked', 40) (2, 'system', 'TTS', 'Pourriez vous préciser la posologie pour le patient?', 40) (3, 'user', 'ASR', '3 comprimés par jour matin midi et soir pendant 10 jours', 64) (3, 'system', 'TTS', "Est-ce que vous confirmez l'ajout de cette prescription sur la liste?", 66) (4, 'user', 'UI', '/inform{"validate":"validate"}', 73) (4, 'system', 'TTS', 'Prescription validée avec succès. Traitement ajouté sur le dossier du patient', 73) (-1, 'END', 'APP', '', 73)</p><p>- N/A</p><p> </p><p>For example, in this dialogue session (00MYcyVK0t), there are two recordings.</p><p>The events are given in a single row for each dialogue session once in the</p><p>first recording (recording_0). Dialogues are described in form of events</p><p>where each action taken by the user or the system is considered as a dialogue</p><p>turn in a tuple form.</p><p> </p><p>(-1, 'START', 'APP', None, 0)</p><p> </p><p>- First element of the event is the dialogue turn number. -1 means that the application</p><p>is initialized.</p><p>- Second element describes who initiated the event: user, system, START, END</p><p>- Third element describes the type of the event: APP (start and end events)</p><p>, ASR (automatic speech recognition), TTS (text-to-speech), UI (user interface)</p><p>User clicks on buttons triggers sometimes explicit intent recognition. For</p><p>example (4, 'user', 'UI', '/inform{"validate":"validate"}') describes the</p><p>explicit intent of validation of the prescription.</p><p>- Fourth element is the timestamp (in seconds)</p><p> </p><h4>Dialogue annotations</h4><p> </p><p>We also include a manual annotation for dialogues (dialogue_annotations.txt) which indicates for each recording, if the system gave the correct answer given the utterance.</p><p> </p><p>Each line contains a keyword, either [Fail] or [OK]. The following example shows a dialogue sample with annotations:</p><p> </p><p>| dialogue_annotations.txt | paths.txt | seq.in |</p><p>|----------------------------------|----------------------------------------|--------------------------------------------------------------------------------------|</p><p>| OK | 14yHtAe555/recording_0.wav | oxytetracycline solution euh |</p><p>| OK | 14yHtAe555/recording_1.wav | oxytetracycline solution 5 gouttes matin et soir pendant 14 jours |</p><p>| Fail | 14yHtAe555/recording_2.wav | oxytetracycline solution 5 gouttes matin et soir pendant 14 jours |</p><p> </p><p>For these 3 dialogues, the dialogue annotations are accordingly OK, OK and Fail.</p><p>[Ok] means that the dialogue system reacted correctly to the input.</p><p>[Fail] means that the action of the system after this utterance should not be used</p><p>for evaluation or training. </p><p>We can notice that in the first utterance, the information are missing, however</p><p>after the second example the system normally have all of the required slots</p><p>for the prescription validation.</p><p> </p><p>It is to note that free comments added using the ASR system were noted as</p><p>Fail as these dialogues did not enter the dialogue state tracking. In this</p><p>example, the last utterance is recorded as a free comment by the prescriber</p><p>and was annotated as Fail.</p><p> </p><h4>Linking audio records to ASR events</h4><p>In order to link audio records to ASR events, the user has to use both paths.txt and events.txt</p><p> </p><p>For example for the following dialogue session (lines 1:2 of paths.txt):</p><p>00MYcyVK0t/recording_0.wav</p><p>00MYcyVK0t/recording_2.wav</p><p> </p><p>Events.txt include two ASR events:</p><p>(1, 'user', 'ASR', 'flagyl 500 mg en cachet pendant 8 jours', 30)</p><p>(3, 'user', 'ASR', '3 comprimés par jour matin midi et soir pendant 10 jours', 64)</p><p> </p><p>These ASR events corresponds to the recording files that can be found in the recordings folder.</p><p> </p><h4>Available user action annotations</h4><p> </p><p>Events.txt include annotations such as below with the following explanation:</p><p> </p><p>- /inform{"validate":"validate"} : User clicks on the validate button after seeing the prescription</p><p>- /inform{"validate":"refuse"} : User clicks on the refuse button after seeing the prescription</p><p>- ASR : User clicks on the push-to-talk button to record an utterance</p><p>- listview_item_clicked : User clicks on the list to choose a drug</p><p>- listview_cancel_clicked : User clicks on the cancel button after seeing a list of drugs</p><p>- FREE_COMMENT_ADDED : User clicks and records a free-form utterance by clicking "add free comment" button</p><p>- EMPTY_UTTERANCE: Recording containing an empty utterance</p><p>- APP_CRASH : An application crash that happened in the dialogue turn</p><p>- EVAL_FINISH_APPROVED : User clicks on the final upload button to finish the experiment</p><p>- RESTART_CONVERSATION_SESSION : User clicks on the restart conversation button</p><p>- RESTART_CANCEL_CLICKED : User cancels the restart process by clicking on the cancel button</p><p>- EVAL_FINISH_CANCELED : User cancels the final upload process by clicking on the cancel button</p><p> </p><p>** Free Comments: **</p><p>The users had the possibility of recording a speech-to-text message upon viewing a prescription.</p><p>These messages had not beed added to the dialogue state tracking but were visualized on the interface and saved in</p><p>the database. Users can find free comments by searching for FREE_COMMENT_ADDED events in the events.txt to find out</p><p>about these events.</p>
Knowledge of Primary Health Care Nurses at Selected Public Institutions Regarding Prescription of Antihypertensive Drugs
<p>Hypertension has been among the most studied topics of the previous century and has been one of the most significant comorbidities contributing to the development of stroke, myocardial infarction, heart failure, and renal failure. In the mist of that, there is limited data on nurses’ knowledge regarding prescription of antihypertensive drugs in Lesotho. The aim of this study was to assess primary health care nurses’ knowledge regarding prescription of antihypertensive drugs. Quantitative descriptive cross-sectional study of primary health care nurses recruited by purposive sampling was conducted. An electronic questionnaire was used to collect data and data were analyzed using descriptive statistics. Most participants were females and had attained diploma in general nursing and midwifery. A proportion of 26% fully knew the steps to be followed for diagnosing hypertension and only 23% felt highly confident to initiate patients on antihypertensive drugs. Moreover, knowledge on mechanism of action of antihypertensive drugs was poor and less than half only knew antihypertensive drug of choice when there is comorbidity. The study identified that nurses have knowledge deficit regarding prescription of antihypertensive drugs. There is a need for interventions to improve nurses’ knowledge so as to curb hypertension burden. </p>
Antibiotic Prescription in Children with Acute Upper Respiratory Tract Infections-Egypt
<p>This dataset for data collected to study knowledge , attitude and practice of non-specialized physician in Assiut, Egypt as regards antibiotic prescription in cases of children with acute upper respiratory tract infections.<br> Questionnaire of the study has been uploaded with the dataset and for each question the responses were coded in dataset as presented in the questionnaire (i.e No=0, Yes=1).</p>
Antibiotic administration without prescription dataset
<p>This data set includes information related to the administration of antibiotic without prescription antibiotic use (labeled as "administration_antibiotic_without_prescription") among the urban people of Bangladesh. The data set is included sociodemographic information such as age, sex, marital status, and educational status. The attitudes towards antibiotic use are included the information within two months of antibiotic taking frequency (labeled as "took_antibiotics"), awareness of use (labeled as "attitude_awareness_use"), abuse of antibiotics (labeled as "attitude_abuse_antibiotic"), antibiotic resistance (labeled as "attitude_resistance"), and effect of resistance (labeled as "attitude_effect_resistance"). The knowledge of antibiotics treatable diseases such as COVID-19 (labeled as "knowledge_COVID-19"), dengue (labeled as "knowledge_dengue"), diabetics (labeled as "knowledge_diabetics"), pneumonia (labeled as "knowledge_pneumonia"), and tuberculosis (labeled as "knowledge_tuberculosis") are included. The knowledge of types of disease specification are also included bacterial (labeled as "knowledge_bacterial"), viral (labeled as "knowledge_viral"), parasitic (labeled as "knowledge_parasitic"), fungal (labeled as "knowledge_fungal"), and helminthic (labeled as "knowledge_helminthic"). Finally, knowledge of antimicrobials drugs specifications is included Penicillin (labeled as "knowledge_Penicillin"), Amoxicillin (labeled as knowledge_Amoxicillin), Cefixime (labeled as "knowledge_Cefixime"), Azithromycin (labeled as "knowledge_Azithromycin"), Remdisivir (labeled as "knowledge_Remdisivir"), and Albendazole (labeled as "knowledge_Albendazole").</p>
Low-mass X-ray binaries: the effects of the magnetic braking prescription
<p>MESA inlists associated with Formation of <a href="https://ui.adsabs.harvard.edu/?#abs/2019MNRAS.483.5595V/abstract">Low-mass X-ray binaries: the effects of the magnetic braking prescription</a></p>
Appendix to 'Digital Marketing in Indian Pharmaceuticals: Strategies for Prescription Products Targeting Healthcare Professionals
<p>This appendix supplements the scoping review on digital marketing strategies in the Indian pharmaceutical industry from 2014 to 2024. It includes additional data, case studies, reference tables, and detailed analyses that support the main paper, providing further insights into the adoption and effectiveness of digital tools among healthcare professionals. The appendix also incorporates supplementary materials that expand on the use of AI, social media, omnichannel marketing, and key theoretical frameworks, such as the Unified Theory of Acceptance and Use of Technology (UTAUT) and Customer Journey Mapping (CJM), used in the main study.</p>
The Effect of Prescription Medications in Marijuana Users
ClinicalTrials.gov study NCT00893074. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Impact of the Lab-score on Antibiotic Prescription Rate in Children With Fever Without Source
ClinicalTrials.gov study NCT02179398. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Default Dosing Settings for Opioid Prescriptions to Adolescents and Young Adults After Tonsillectomy
ClinicalTrials.gov study NCT04066829. IPD Sharing: NO. Countries: 1. Publications: 1.
Evaluating the Impact of a Value-based Prescription Drug Formulary
ClinicalTrials.gov study NCT04904055. IPD Sharing: NO. Countries: 1. Publications: 1.
A Genomic Approach to Warfarin Dose Prescription in Admixed Caribbean Hispanics
ClinicalTrials.gov study NCT02345356. IPD Sharing: YES. Countries: 2. Publications: 5.
Prescription Digital Therapeutic for the Treatment of Insomnia
ClinicalTrials.gov study NCT04909229. IPD Sharing: YES. Countries: 1. Publications: 1.
Assessment of a Fruit and Vegetable Prescription Program for Children
ClinicalTrials.gov study NCT04767282. IPD Sharing: YES. Countries: 1. Publications: 8.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.