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zenodo40/100

Artificial Intelligence and the Future of Smart Cities-Figure 4. Influence of AI in the development of smart cities by respondents age

Respondents were asked to indicate how they evaluate the influence of AI in the development of intelligent cities. In order to fully understand the concept of smart cities, the definition of smart city given by Caragliu (2009) was given to the respondents. It is presented in section 2. On question 6 two-way analysis was used to determine the difference by age and gender. There was no statistically significant interaction between groups as determined by two-way ANOVA F (3, 106) = 8.675, p value>0.05 (p=.387). The assumption of homogeneity of variance was tested using the Brown-Forsythe Test. There were statistically significant differences by gender (F=2.169, p<0.05) and by age (F=30.885, p<0.05). More than 9 in ten (almost 94%) consider AI to be important (50%) or very important (43.8%) while just a few (6.2%) recall a moderate importance for smart cities development. Female participants scored significantly higher (M=4.60, SD=.49) than male participants (M=4.27, SD=.62) on question 6 &bdquo;Generally speaking, how do you assess the influence of AI in the development of intelligent cities”. At the same question: the 41-50 age group scored the highest score followed by the 18-25 age group (M=4.40, SD=.49). The 26-30 age group scored lower than the 18-25 age group (M=4.37, SD=.48) and significantly higher than the 31-40 age group (Figure 4).

opencc-by-4.0Apr 2018View details →

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Artificial Intelligence and the Future of Smart Cities-Figure 5. Smart features as the main beneficiaries of AI in terms of the respondent's age (statistically significant differences only for 7.1 and 7.3)

The majority of the respondents who found the smart features to be the main beneficiaries of AI facilities were ranging between 31-40 years old and +41 age old, followed by the 18-25 age group (M=3.80, SD =0.75), 26-30 (MD=4.0, SD =.75) (Figure 5).

opencc-by-4.0Apr 2018View details →

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Artificial Intelligence and the Future of Smart Cities-Figure 3. ICT-based infrastructure and its four layers Source: adapted after Skouby et al., 2014

opencc-by-4.0Apr 2018View details →

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Artificial Intelligence and the Future of Smart Cities-Figure 8. Influence of AI on individual safety by respondents age

On question 11 respondents were asked to consider the influence of AI on individual safety using a 5 points Likert Scale ranging from 1 being “totally unimportant” and 5 being “very important”. The analysis of variance was used to determine the differences by age group and by gender. The analysis shows that no statistical interaction was found between age and gender F=3.081, p=0.08. We found statistically significant differences by gender F=7.639, p<0.05 and age F=6.318, p=.001). Female participants scored significantly higher (M=4.40, SD=.81) than male participants (M=4.00, SD=.60) on question 11 about the influence of AI on individual safety. At the same question: the 41-50 age group scored the highest (M=4.50, SD=.51), followed by the 18-25 age group (M=4.40, SD=.81); the 26-30 age group scored lower than the 18-25 age group and significantly higher than the 31-40 age group (M=3.87, SD=.60) (Figure 8).

opencc-by-4.0Apr 2018View details →

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Artificial Intelligence and the Future of Smart Cities-Figure 7. Q.9.Which of the following job functions will AI impact the most over the next 10 years? (Statistically significant differences by age for 9.3, 9.4, 9.5 and 9.6)

The analysis reveals that people perceive that AI will have a greater impact over the next 10 years on marketing (for example, intelligent customer targeting, planning and executing marketing campaigns) scored significantly higher (M=4.37, SD=.69) than on finance (for example, robotic financial advisors, automated corporate financial analysis) (M=3.87, SD=.60) (Figure 7). For the same question customer services scored significantly higher (M=3.75, SD=.83) than health (e.g. consultation and diagnosis, surgery) (M=3.25, SD=.83). For the same question, the analyses by gender reveals that the majority of female participants scored significantly higher (M=3.40, SD=.81) than male participants (M=3.18, SD=.57) and those aged in the second group.

opencc-by-4.0Apr 2018View details →

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Artificial Intelligence and the Future of Smart Cities-Figure 10. Respondents' opinions about the use of robots in different activities (grouped by age)

Question 14 was used to evaluate the respondents’ opinions about the use of robots in the following activities: performing medical surgeries, child care, supply of consumer goods, driving a car, assistance in performing tasks at work and cleaning (Figure 10). The respondents feel most confident and safe to use robots for cleaning (M=4.18, SD=1.07) and for assistance in performing tasks at work (M=4.12, SD=1.05) and less confident and safe to use robots for driving a car (M=3.87, SD=1.11), for supply of consumer goods (M=3.81, SD=.81) and performing medical surgeries (M=3.68, SD=.92) The child care obtained the lower score (M=2.00, SD=86).

opencc-by-4.0Apr 2018View details →

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Artificial Intelligence and the Future of Smart Cities-Figure 6. Importance of AI for respondents business or industry by respondent's age

On question 8 respondents have to indicate on a scale of 1 to 5 (1 being “not important” and 5 being “critical for survival”), how important they think the AI will be for their business or industry (or for the one they are preparing for) in the next 10 years? No statistical interaction was found between age and gender F (3, 108) = .174, p>0.05). There were statistically significant differences by gender F (1, 108) = 50.261, p<0.05 and age, F (3, 108) = 9.298, p<0.05. Female participants scored significantly higher (M=4.60, SD=.49) than male participants (M=3.36, SD=.88) on question 8 about the importance of AI for the fields of activity of the participants (or for those they are preparing for) in the next 10 years. At the same question: the 26-30 age group scored the highest  followed by the 18-25 age group (M=3.80, SD=1.18); the 31-40 age group scored lower than the 18-25 age group (M=3.50, SD=.87); the 31-40 age group scored also lower than the 18-25 age group (M=3.50, SD=.51) (Figure 6).

opencc-by-4.0Apr 2018View details →

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Artificial Intelligence Course

<a href="https://intellipaat.com/artificial-intelligence-deep-learning-course-with-tensorflow/">Intellipaat Artificial Intelligence course</a> online certification using TensorFlow is an industry-designed certification training to master Convolutional Neural Network (CNN), Perceptron in CNN, TensorFlow, TensorFlow-Code, graph visualization, transfer learning, recurrent neural networks, deep learning libraries, Keras & TFLearn API, GPU in deep learning, backpropagation, and hyperparameters through hands-on projects. Learn AI with this Artificial Intelligence course online using TensorFlow. In <a href="https://intellipaat.com/artificial-intelligence-course-india/">Indian</a> cities like <a href="https://intellipaat.com/artificial-intelligence-course-bangalore/">Bangalore</a>, and <a href="https://intellipaat.com/artificial-intelligence-course-hyderabad/">Hyderabad</a>, AI course is provided too.

opencc-by-4.0Nov 2019View details →

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Next-Gen Quality Assurance: A Deep Dive into Integrating Artificial Intelligence in the Pharmaceutical Industry

This article explores the transformative integration of Artificial Intelligence (AI) with advanced quality tools in the pharmaceutical industry. From the adoption of Analytical Quality by Design (AQbD) principles in method development to the application of AI in rapid testing, Design of Experiments (DoE), and statistical tools for trending and process capability analysis, a synergistic relationship emerges. The amalgamation of AI and advanced tools represents a paradigm shift, propelling quality assurance into a new frontier marked by precision, reliability, and excellence in pharmaceutical manufacturing. Despite encountering challenges such as data privacy and regulatory compliance, the industry is moving towards a future where AI is not merely a tool but a strategic partner, shaping a revolutionary landscape where the highest standards of quality are not only met but exceeded.

opencc-by-4.0Sep 2024View details →

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Appendix Figures A.1 - A.15 of the paper "Advanced classification of hot subdwarf binaries using artificial intelligence techniques and Gaia DR3 data". This work has been accepted for publication in the journal Astronomy & Astrphysics (A&A) on September 24, 2024.

Figure captions:   Fig. A.1. Heatmap with the number of common stars (true positives) labeled as binary for the five methods used.   Fig. A.2. Heatmap with the number of common stars (true negatives) labeled as single for the five methods used.   Fig. A.3. Color-magnitude diagrams, showing the 2815 stars of our sample from Sect. 3. Colors indicate the label predictions by SOM (left panel) and CNN (right panel).   Fig. A.4. K-S test comparing radial SOM (black) and CNN (blue).   Fig. A.5. Spectra of the star "LAMOSTJ112914.11+471501.7" (blue color) and in the background (gray color) the 35 stars classified as binary by Solano et al. (2022) with VOSA tools.   Fig. A.6. Spectra of the star "HD14829" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).   Fig. A.7. Spectra of the star "Feige98" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).   Fig. A.8. Spectra of the star "PG0304+184" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).   Fig. A.9. Spectra of the star "PG1510+635" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).   Fig. A.10. Cluster 0 of spectra (blue color) with the other spectra in the background (gray color).   Fig. A.11. Cluster 4 of spectra (brown color) with the other spectra in the background (gray color).   Fig. A.12. Cluster 1 of spectra (yellow color) with the other spectra in the background (gray color).   Fig. A.13. Cluster 3 of spectra (red color) with the other spectra in the background (gray color).   Fig. A.14. Cluster -1 of spectra (pink color) with the other spectra in the background (gray color).   Fig. A.15. Cluster 2 of spectra (green color) with the other spectra in the background (gray color).

opencc-by-4.0Sep 2024View details →

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Conceptual Change Texts in Computer Science to Expand Students' Conceptions on the Topic of Artificial Intelligence

General information on the survey and cohort The present data were collected in 2023 at a secondary school in Hamburg. A questionnaire was used as a pre- and post-test in computer science courses in years 10 and 11 to investigate the agreement with various items and how this changed. The questionnaires were transferred to SPSS for analysis. 76 students aged 15-17 took part in the survey and the teaching intervention. This data set only contains the data that is available in full. It therefore includes the responses of 69 students, of which a total of 22 felt assigned to the female gender and 47 to the male gender.   <h3>Data set</h3> The data is purely quantitative, as this is only the evaluation of the pre- and post-test and not the evaluation of the conceptual change texts themselves. The item list is made up of 20 items relating to artificial intelligence, which were drawn up according to the Big Ideas of the AI4K12 initiative [1]. The students' perceptions of the items come from various past studies that have surveyed students' conceptions of AI. The data has a pseudonymized code that can be linked to the conceptual change text of the experimental group and the data can be assigned accordingly. The data is also divided into the experimental group (abbreviation 2) and the control group (abbreviation 1) so that a comparison between the groups is quickly possible. The abbreviation W in front of the items stands for a “true” statement and SV for “student conception”. A Likert scale from 0 - does not apply at all to 3 - applies completely was used. The data set is cleansed data. All data sets that were not complete or that were given different codes in the pre-test and post-test and therefore could no longer be clearly assigned were removed. Literature [1]          AI for K12 (Hrsg.) (2020): AI4K12.org. Available online at: https://ai4k12.org/ [last checked 12.03.2023]

opencc-by-4.0Oct 2024View details →

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Current Applications and Future Prospects of Artificial Intelligence in Higher Medical Education: A Bibliometric Analysis

This dataset supports the study titled "Current Applications and Future Prospects of Artificial Intelligence in Higher Medical Education: A Bibliometric Analysis". It includes bibliometric data from Web of Science on the use of artificial intelligence in higher medical and pharmaceutical education. The dataset covers relevant literature, keywords, and citation analysis to provide insights into research trends, institutional contributions, and future directions in the field. This data can be valuable for researchers interested in the intersection of AI and medical education, as well as for those conducting further bibliometric analyses.

opencc-by-4.0Nov 2024View details →

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The use of Artificial Intelligence in Digital Forensics

Data ini digunakan untuk membuat penelitian berdasarkan tinjauan literatur dengan kata kunci "Digital Forensik" dan "Artificial Intelligence"

opencc-by-4.0Nov 2022View details →

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Supplementary data for: "Towards automatic generation of control structures for Process Flow Diagrams (PFDs) with Artificial Intelligence"

Uploaded on 20. February 2023 This is the supplementary data for the publication "Towards automatic generation of control structures for Process Flow Diagrams (PFDs) with Artificial Intelligence" (2023) by Edwin Hirtreiter, Lukas Schulze Balhorn, Artur M. Schweidtmann Corresponding author: A. M. Schweidtmann, E-mail: a.schweidtmann@tudelft.nl Delft University of Technology, Department of Chemical Engineering, Process Intelligence Group, Van der Maasweg 9, 2629 HZ Delft, The Netherlands The folder contains json files with the training (train), test (test), and augmented training (train_augm) data files. The json files contain syntetically generated SFILES.  The pre-print of the manuscript is accessible at https://doi.org/10.48550/arXiv.2211.05583

opencc-by-4.0Feb 2023View details →

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Ranking snapshot for the query "THE ROLE OF HUMAN INTELLIGENCE IN ARTIFICIAL INTELLIGENCE" performed on Google Scholar

This deposit provides a snapshot of results obtained through a Google Scholar search query on the topic of "THE ROLE OF HUMAN INTELLIGENCE IN ARTIFICIAL INTELLIGENCE". The search was performed by Alessandro Lotta, from Unipd, on May 30, 2023 at 6:34:32 PM. The captured data includes 2 pages of search results, which have been saved in the output-data.jsonld file. In addition to the citation data, the deposit includes PNG format screenshots of the search results, allowing visual reference to the captured information. The metadata for the Research Object Crate is also included in JSON format, providing essential details about the contents. The citation snapshot presented here is generated using the Unipd Ranking Citation Tool, a tool developed by Gianmaria Silvello and Alessandro Lotta (University of Padua). This tool, accessible at https://rankingcitation.dei.unipd.it

opencc-zeroMay 2023View details →

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Development, validation and use of artificial-intelligence-related technologies to assess basic motor skills in children: a scoping review

This project contains the following extended data: <ul> <li>Appendix 1. Supplementary Tables</li> <li>Appendix 2. Search formulas</li> </ul>

opencc-by-4.0Jun 2023View details →

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Mimicking Clinical Trials with Synthetic Acute Myeloid Leukemia Patients Using Generative Artificial Intelligence

We used two different methodologies of generative artificial intelligence, CTAB-GAN+ and normalizing flows (NFlow), to synthesize patient data based on 1606 patients with acute myeloid leukemia that were treated within four multicenter clinical trials. The resulting data set consists of 1606 synthetic patients for each of the models. This dataset is associated with our publication "Mimicking clinical trials with synthetic acute myeloid leukemia patients using generative artificial intelligence" by Eckardt et al., npj Digital Medicine, 2024 (<a href="https://doi.org/10.1038/s41746-024-01076-x" target="_new">https://doi.org/10.1038/s41746-024-01076-x</a>). If you use this dataset, please cite our paper.   Data Dictionary <table> <tbody><tr> <th>NAME</th> <th>LABEL</th> <th>TYPE</th> <th>CODELIST</th> </tr> </tbody><tbody> <tr> <td>AGE</td> <td>age</td> <td>num</td> <td>in years</td> </tr> <tr> <td>AMLSTAT</td> <td>AML status</td> <td>char</td> <td>de novo, sAML, tAML</td> </tr> <tr> <td>ASXL1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>ATRX</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>BCOR</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>BCORL1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>BRAF</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CALR</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CBL</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CBLB</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CDKN2A</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CEBPA</td> <td>CEBPA mutation</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CGCX</td> <td>complex cytogenetic karyotype</td> <td>char</td> <td>0 'No', 1 'Yes'</td> </tr> <tr> <td>CGNK</td> <td>cytogenetic normal karyotype</td> <td>char</td> <td>0 'No', 1 'Yes'</td> </tr> <tr> <td>CR1</td> <td>first complete remission</td> <td>char</td> <td>0 = 'not achieved', 1 = 'achieved'</td> </tr> <tr> <td>CSF3R</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CUX1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>DNMT3A</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>EFSSTAT</td> <td>status variable for EFSTM</td> <td>num</td> <td>0 'censored' 1 'event'</td> </tr> <tr> <td>EFSTM</td> <td>event free survival time</td> <td>num</td> <td>in months</td> </tr> <tr> <td>ETV6</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>EXAML</td> <td>extramedullary AML</td> <td>char</td> <td>0 'No', 1 'Yes'</td> </tr> <tr> <td>EZH2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>FBXW7</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>FLT3I</td> <td>FLT3-ITD mutation status</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>FLT3T</td> <td>FLT3-TKD mutation status</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>GATA2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>GNAS</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>HB</td> <td>hemoglobin</td> <td>num</td> <td>in mmol/l</td> </tr> <tr> <td>HRAS</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>IDH1</td> <td>IDH1 mutation status</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>IDH2</td> <td>IDH2 mutation status</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>IKZF1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>JAK2</td> <td>Jak2 Mutation</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>KDM6A</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>KIT</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>KRAS</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>MPL</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>MYD88</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>NOTCH1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>NPM1</td> <td>NPM1 mutation status</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>NRAS</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>OSSTAT</td> <td>status variable for OSTM</td> <td>num</td> <td>0 'censored' 1 'event'</td> </tr> <tr> <td>OSTM</td> <td>overall survival time</td> <td>num</td> <td>in months</td> </tr> <tr> <td>PDGFRA</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>PHF6</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>PLT</td> <td>platelet count</td> <td>num</td> <td>in 10⁶/l</td> </tr> <tr> <td>PTEN</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>PTPN11</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>RAD21</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>RUNX1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SETBP1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SEX</td> <td>sex</td> <td>char</td> <td>f 'female', m 'male'</td> </tr> <tr> <td>SF3B1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SMC1A</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SMC3</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SRSF2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>STAG2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SUBJID</td> <td>subject identifier</td> <td>char</td> <td> </td> </tr> <tr> <td>TET2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>TP53</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>U2AF1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>WBC</td> <td>white blood count</td> <td>num</td> <td>in 10⁶/l</td> </tr> <tr> <td>WT1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>ZRSR2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>inv16_t16.16</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t8.21</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.6.9..p23.q34.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>inv.3..q21.q26.2.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>minus.5</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>del.5q.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.9.22..q34.q11.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>minus.7</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>minus.17</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.v.11..v.q23.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>abn.17p.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.9.11..p21.23.q23.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.3.5.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.6.11.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.10.11.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.11.19..q23.p13.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>del.7q.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>del.9q.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>trisomy 8</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>trisomy 21</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>minus.Y</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>minus.X</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> </tbody> </table>

opencc-by-4.0Sep 2023View details →

ClinicalTrials.gov40/100

IntelliCare: Artificial Intelligence in a Mobile Intervention for Depression and Anxiety (AIM)

ClinicalTrials.gov study NCT02176226. IPD Sharing: NO. Countries: 1. Publications: 2.

closedIPD-NOFeb 2026View details →

ClinicalTrials.gov40/100

IntelliCare Study: Artificial Intelligence in a Mobile (AIM) Intervention for Depression

ClinicalTrials.gov study NCT02801877. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

ClinicalTrials.gov40/100

Detection of Colonic Polyps Via a Large Scale Artificial Intelligence (AI) System

ClinicalTrials.gov study NCT04693078. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record