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Questionnaire on gender and age-related peculiarities in informed consent to clinical trials – National legislation
<p>European experts from the six selected countries (Germany, Spain, Austria, France, Italy, and United Kingdom) included in research done within task 1.3 (Ethical and legal review of gender and age-related issues associated with the acquisition of informed consent) participated in a survey on gender and age-related peculiarities in informed consent to clinical trials within national legislations.</p> <p>Experts were selected for their high-level scientific expertise in the fields relevant to the objectives of task 1.3. A short questionnaire on “Gender and age-related peculiarities in informed consent to clinical trials within national legislations" has been prepared and circulated to contact experts. This questionnaire was meant to identify the legal review process and collect up-to-date data. It was structured in 10 queries, exclusively aimed at obtaining hard law and soft law information pertaining to the topics addressed in task 1.3.</p>
Analysis of websites, social media pages and apps for the development of new strategies for increasing participation of women in clinical trials
<p>For T2.5 of the i-CONSENT project, an analysis was undertaken of the strategies used to effectively communicate with women on the topic of women’s health or women’s health research, considering aspects such as tone, format and audience interaction. A total of 42 websites, social media pages and apps were included in the analysis. The attached document presents the findings from the data generation stage of this analysis.</p>
Clinical Trial Transparency at UK Universities (2018-2021)
<p>This dataset describes the analysis 20 U.K. universities' clinical trial registration and reporting policies and reporting performance of CTIMPs on EUCTR. This is supplementary information on a publication in the journal Clinical Trials: Journal of the Society for Clinical Trials. The manuscript is titled "Improving clinical trial transparency at U.K. universities: evaluating three years of policies and reporting performance on the European Clinical Trial Registry (EUCTR)". Please refer to the manuscript for more information on the methodology and analysis.</p>
Agronomic performance of cultivar mixtures and pure stands of 8 winter wheat varieties, obtained from mixture field trials in Switzerland from 2021 to 2023, together with associated functional traits measurements
<p>This dataset contains agronomic performance data for 8 Swiss winter wheat cultivars, grown in pure stands and in mixtures at 3 locations in Switzerland during 3 growing seasons (2021-2023). The dataset has been used to analyse the effects of cultivar mixtures on agronomic performance and stability, which is published in <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.eja.2024.127504" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.eja.2024.127504</a>. </p> <p>The dataset contains notably grain yield, protein content, thousand kernel weight, specific weight, and Zeleny sedimentation value, as well as functional traits measured at flowering for each mixture and pure stand plot. </p> <p>The field trials were performed under the Swiss Extenso (low input) conditions, conducted by Agroscope and DSP. </p> <h3>Methods </h3> <p> <em>Field trials </em></p> <div>Field trials were set up over the course of three growing seasons – 2020/2021, 2021/2022 and 2022/2023 – in three sites across the Swiss Central Plateau. The experimental sites were located in Changins (46°19′ N 6°14′ E, 455m a.s.l), Delley (46°55′ N 6°58′ E, 494m a.s.l) and Utzenstorf (47°97′ N 7°33′ E, 483m a.s.l.). </div> <div>Experimental communities consisted of pure stand plots, 2-cultivars mixtures, and one plot with the 8 cultivars mixed. We sowed every possible combination of 2-cultivar mixtures, amounting to a total of 28 2-cultivar mixtures treatments, to which we added the 8-cultivar mixture. Each community was grown in a plot of 7.1 m<sup>2</sup> (1.5m∗4.7m). We used a complete randomized block design, with 3 replicates, the plots being randomized at each site within each block. Sowing was performed with a small plot drill (Wintersteiger plotseed TC). Density of sowing was 350 viable seeds/m<sup>2</sup>. For the mixtures, seeds were mixed beforehand at a 2 × 50 % mass ratio for 2-cultivars mixtures and 8 × 12.5 % for the 8-cultivar mixture. We chose this method of mixing as this is what is commonly done by farmers in Switzerland. Plots were sowed mechanically each autumn and fertilized with ammonium nitrate at a rate of 140 N/ha in 3 applications (40 N/ha at tillering stage/BBCH 22–29; 60 N/ha at the beginning of stem elongation/BBCH 30–31; 40 N/ha at booting stage/BBCH 45–47). The trials were grown according to the Swiss <em>Extenso</em> scheme, i.e. without any fungicide, insecticide, and growth regulator. Weeds were regulated twice or thrice per season with the application of herbicides commonly used in Switzerland.</div> <div> </div> <div><em>Ear density</em></div> <div> </div> <div>Before harvest, we manually harvested horizontal bands of 1.5 × 0.3 square meters per plot. The location of the band was randomly chosen but we avoided plot edges (i.e. the band was located at more than 0.5 m from the lower and upper edge of each plot). We counted the heads, and obtained ear density from the head counts.</div> <div> </div> <div><em>Trait measurements </em></div> <div> </div> <div>At flowering time, we randomly sampled 6 healthy leaves per plot. We immediately wrapped this leaf in moist cotton; this was stored overnight at room temperature in open plastic bags. The following day, we removed excess surface water on the leaf and weighted it to obtain its water saturated weight. This leaf was then scanned with a flatbed scanner (Perfection V39II, Epson), oven-dried in a paper envelope at 80°C for 72 hours, and subsequently weighed again to obtain its dry weight. Leaf Dry Matter Content (LDMC) was calculated as the ratio of leaf dry mass (g) to water saturated leaf mass (g). Using the leaf scans, we measured leaf area with the image processing software ImageJ. Specific Leaf Area (SLA) was calculated as the ratio of leaf area (cm2) to leaf dry mass (g).</div> <div> </div> <div><em>Phenology and height </em></div> <div> </div> <div>For each plot, we recorded the heading date as the day of the year, in which 50 % of the ears of the plot had fully emerged from the flag leaf. Plant height was measured in each plot at BBCH 59–75, by taking the average height in centimeters from the ground to the top of five random ears, excluding awns.</div> <div> </div> <p><em>Harvest and post harvest measurements</em></p> <p>At maturity, we harvested each plot with a combine harvester (Zürn 150, Schontal-Westernhausen, Switzerland). The harvested grains were dried when needed, weighed a first time, then sorted and cleaned by air and with a sieve cleaner, and subsequently weighted again. We measured hectoliter weight (test weight, HLW, kg/hl) and water content at the plot level using a Dickey-John machine (GAC 2100). Grain yield was subsequently standardized to 15 % of humidity. Protein content (% of dry matter) was measured at the site level with a near-infrared instrument (ProxiMate™, Büchi instruments). Thousand kernel weight (TKW, g) was measured at the plot level with a Marvin seed analyzer (GTA Sensorik, Neubrandenburg, Germany). Zeleny sedimentation value was measured by the laboratory of Delley Seeds and Plants. </p> <p> </p>
Agronomic performance of cultivar mixtures of winter wheat varieties, obtained from mixture field trials at 5 locations in Switzerland from 2019 to 2020, together with yield data from the varieties in pure stand obtained from the national variety testing trial network
<p>This dataset contains agronomic parameters of 32 winter wheat variety mixtures tested during 2 growing seasons (2019-2020) at 5 locations in Switzerland, as well as yield data of these varieties in pure stands originating from the Swiss national variety testing network. The dataset has been used to investigate the links between asynchrony and yield stability, published in <a href="https://doi.org/10.1002/csc2.21151">https://doi.org/10.1002/csc2.21151</a>. </p> <p>The field trials were performed under the Swiss Extenso (low input) conditions, conducted by Agroscope and DSP. </p> <h2>Methods </h2> <p><em>Field trials </em></p> <p>The experiment took place in five sites across Switzerland, in 2019 and 2020. The sites were located in Nyon (1260), Delley (1567), Utzenstorf (3428), Zurich (8046), and Ellighausen (8566).</p> <p>Experimental communities consisted of 32 different two-variety mixtures grown in 7.1-m<sup>2</sup> plots (1.5 × 4.7 m). We replicated the mixture experiment three times per site with the exact same variety composition. We used a randomized block design, with plots being randomized at each site within each block. Density of sowing was 350 seeds/m<sup>2</sup>, and seeds were mixed beforehand at a 50:50 ratio in terms of mass. We used the 50:50 mass ratio as this is what is generally done in practice by farmers and seed suppliers. Plots were sown mechanically each autumn. The plots were mechanically fertilized according to the Principles of Agricultural Crop Fertilisation in Switzerland (Federal Office for Agriculture) with an average of 140 kg N/ha (ammonium nitrate), applied in three splits (40 at the tillering stage—60 at stem elongation stage—40 when the flag leaf is visible). The experimental trials were conducted following the extenso Swiss scheme, which means that there was no application of any fungicide, insecticide, or plant growth regulator. </p> <p>The performances of single varieties were obtained by going through the trials of the national variety testing program. We gathered the data for the years 2018/2019 and 2019/2020. The data regarding single varieties could be obtained for three out of the five sites used for the mixtures: 1260, 1567, and 8566. Because there were no national variety trials at the two other sites (8046, 3428), we could not get any data for single varieties in these sites. Thus, all further analyses including single variety data were only done for the three sites mentioned above. At each of these sites, the variety trials were located on the same plot as the mixture trials, even though a little further apart. Therefore, soil parameters and crop precedents were the same between the mixture and variety testing trials. Furthermore, we only selected the national variety testing trials that respected the <em>extenso</em> conditions, that is, no fungicide, pesticide, or growth regulator application, and that received the same amount of fertilization as the mixture trials. In 8566 and 1567, sowing and harvesting dates were identical between the two trials; in 1260, sowing and harvesting dates could vary but remained within a week of each other.</p> <p> </p> <p><em>Data collection </em></p> <p>For each plot, heading dates were monitored, and average height at BBCH 59–75 was measured.</p> <p>The prevalence of diseases was scored twice in the growing season. Specifically, the severity of brown rust, yellow rust, powdery mildew, and Septoria tritici blotch was assessed. This was performed by grading each individual plot from 1 to 9 for each disease, with 1 representing no disease and 9 a complete infection. The scoring scale follows a logistic progression based on the symptoms of the top three leaves. We used the data from the final scoring for statistical analysis, as the disease severity was usually more important then.</p> <p>At maturity, we harvested each plot with a combine harvester. The harvested grains were dried when needed, weighed a first time, then sorted and cleaned by air and with a sieve cleaner, and subsequently weighted again. We measured specific weight and water content at the plot level using a Dickey-John machine (GAC 2100). Grain yield was subsequently standardized to 15% of humidity. Protein content was measured at the site level with a near-infrared instrument (ProxiMate; Büchi instruments).</p>
For METADATA evaluation: TSEM image of TiO2 particles from UNITO trials
TSEM (more 'material/thickness' sensitive, see 'Signal A= SE'). Don't be confused by 'SE'. It means in fact 'transmission mode'.
Dataset from: Leveraging open tools to realize the potential of self-archiving: A cohort study in clinical trials
<p>This record includes the data associated with the study "Leveraging open tools to increase the potential of self-archiving to increase discoverability: A cohort study in clinical trials". The code used to generate these data is available under an open license in GitHub (<a href="https://github.com/delwen/oa-archiving-permissions">https://github.com/delwen/oa-archiving-permissions</a>). The deposit includes:</p> <p>- `intovalue.csv`: download of the IntoValue dataset (IntoValue 1 and IntoValue 2), which is actively maintained in GitHub (<a href="https://github.com/maia-sh/intovalue-data">https://github.com/maia-sh/intovalue-data</a>). The data was downloaded on 17 December 2022. More information on the generation of this dataset can be found at: <a href="https://doi.org/10.5281/zenodo.5141343">https://doi.org/10.5281/zenodo.5141343</a>. This data corresponds to the start of the trial screening flow diagram in the manuscript (n = 3,788).</p> <p>- `oa-unpaywall.csv`: dataset containing the results of the Unpaywall API query (query date: 17 December 2022). The dataset queried includes the following adaptations from `intovalue.csv`:</p> <ul> <li>As updated registry data had been downloaded on 1 November 2022, the IntoValue inclusion criteria were re-applied: <ul> <li>Interventional</li> <li>Study completion date between 2009 and 2017</li> <li>Complete based on study status</li> <li>Conducted by a German university medical center.</li> </ul> </li> <li>The dataset was further limited to: <ul> <li>Unique trials (trials from IntoValue 2 were preserved)</li> <li>Unique publications with a DOI</li> </ul> </li> </ul> <p>- `oa-syp-permissions.csv`: dataset containing the results of the Shareyourpaper API query (query date: 17 December 2022). The dataset queried is the same as in `oa-unpaywall.csv`.</p> <p>- `oa-merged-data.csv`: dataset containing the merged Unpaywall and Shareyourpaper data for all clinical trial results publications considered in this study. The dataset was further limited to journal articles that resolved in Unpaywall and were published between 2010 - 2020 (based on the publication date in Unpaywall). This is the main dataset underlying the analyses in the manuscript.</p>
Multi-location trials and population-based genotyping reveal high diversity and adaptation to breeding environments in a large collection of red clover
<p>This dataset accompanies the article with the same title made available on bioRxiv <a href="https://doi.org/10.1101/2022.12.19.520744">https://doi.org/10.1101/2022.12.19.520744</a> </p>
Data and Code Supplement to: "Processes of change in a randomized clinical trial of Radically Open Dialectical Behavior Therapy (RO DBT) for adults with treatment refractory depression"
<p>Dataset to support secondary analyses reported in "Processes of change in a randomized clinical trial of Radically Open Dialectical Behavior Therapy (RO DBT) for adults with treatment refractory depression" in the Journal of Consulting and Clinical Psychology</p>
Eurex-LUNa Abisko Trials Lawnmower Vectoring
<p>Navigation data of the AUV DeepLeng during field trials in an ice-covered lake in Abisko, Sweden.</p> <p>For more information on this field trials: <a href="https://www.dfki.de/web/forschung/eurex-abisko">https://www.dfki.de/web/forschung/eurex-abisko</a></p> <p>This dataset is described in the paper</p> <p>M. Hildebrandt, T. Creutz, B. Wehbe, M. Wirtz and M. Zipper, "Under-Ice Field tests with an AUV in Abisko/Torneträsk," OCEANS 2022, Hampton Roads, Hampton Roads, VA, USA, 2022, pp. 1-7, doi: 10.1109/OCEANS47191.2022.9977094.</p> <p>For more details on how to use the data files, please refer to the Readme.md</p> <p>Contact: tom.creutz@dfki.de, bilal.wehbe@dfki.de</p> <p><br> Funded by BMWi (Kennziffer 50 NA 2002)</p>
VISION Invited lecture - New Perspectives in Clinical Trials
<p>Recording and presentation of the invited lecture that took place online on 15 June 2022 - <strong>Prof. Stefano Bonassi, Ph.D., ERT - New Perspectives in Clinical Trials</strong>.</p> <p>Clinical trials are our best approach to generate clinical evidence. Since the early stages in the first decades of the past century, clinical trial has been in continuous evolution. Innovation has interested the formulation of stopping guidelines for safety, efficacy, and futility; the use of internet, changes in the way the informed consent is requested, the introduction of pragmatic trials, the challenge of personalized medicine, the concept of non-inferiority, and many others. Today, trials range from a single person to hundred-thousand people, from a single lab to hundreds of centers around the world, from simple two-arm randomizations to increasingly complex study designs. The lecture will address issues related to the performance of clinical trials, how to deal with the increasing use of the internet, the dramatic change concerning ethics in clinical studies.</p>
Confirmation of the suitability and stability of the imatinib mesylate drug product formulation for intravenous administration in the INVENT-COVID clinical trial
<p>Abstract:</p> <p>An isotonic sterile aqueous solution of imatinib mesylate was formulated and then manufactured according to cGMP by KABS Laboratories. A solution pH of 5.0 is optimum for imatinib mesylate solubility and stability and this is maintained by a sodium acetate buffer. The drug product for the clinical study was presented as a sterile solution in a Type 1 clear glass vial with chlorobutyl stopper and seal. Stability studies on the drug product have confirmed an excellent stability profile and these data are presented in Table 1. The drug product specification detailed in this table is consistent with Regulatory Authority requirements for this stage in clinical development. These data support the conclusion that the drug product used in the INVENT-COVID trial was fully compliant with all cGMP and EU Regulatory Authority requirements.</p> <p> </p>
Tea bag index (TBI) for a field trial (olive orchard) in Ferrandina (Basilicata, Italy)
<p>The file contains mean k and S value per GPS location, with as meta-data the starting date, duration and biome of the study.</p>
Dataset related to article "Harmonization of sensorimotor deficit assessment in a registered multicentre pre-clinical randomized controlled trial using two models of ischemic stroke"
<p>https://figshare.com/search?q=10.6084%2Fm9.figshare.21346731</p>
Clinical Trials_Original dataset. Rising Pharmaceutical Innovation in the Global South.
<p>This is a supplementary document of the research reports from the "Research Collaboration on Technology, Equity, and the Right to Health", between the Global Health Centre (GHC) at the Geneva Graduate Institute in Switzerland, the James P. Grant School of Public Health at BRAC University in Bangladesh, and the Universidad de los Andes (ANDES) in Colombia, supported by the Open Society University Network (OSUN). For more information please refer to: Knowledge Portal on Innovation and Access to Medicines - https://www.knowledgeportalia.org/. </p>
Clinical trials analysis results. Rising pharmaceutical innovation in the Global South.
<p>This is a supplementary document of the research reports from the "Research Collaboration on Technology, Equity, and the Right to Health", between the Global Health Centre (GHC) at the Geneva Graduate Institute in Switzerland, the James P. Grant School of Public Health at BRAC University in Bangladesh, and the Universidad de los Andes (ANDES) in Colombia, supported by the Open Society University Network (OSUN). For more information please refer to: Knowledge Portal on Innovation and Access to Medicines - <a href="https://www.knowledgeportalia.org/">https://www.knowledgeportalia.org/</a></p>
Database and Syntax for Analysis of the Paper: "Effects of introducing the WHO Labour Care Guide on Caesarean section: a pragmatic, stepped-wedge, cluster randomized trial in India"
<p>The following files contains the information used to analyze the trial “Implementing the WHO Labour Care Guide to reduce the use of Caesarean section in four hospitals in India: a pragmatic, stepped wedge, cluster randomized pilot trial” in which it was hypothesized that the intervention would promote correct LCG use by these providers, changing their labour monitoring and management practices to align with WHO’s intrapartum recommendations. In turn, this could reduce overuse of Caesarean section, improve maternal and newborn outcomes, and enhance women’s care experiences. </p> <p>Two datafiles with extension “csv” are uploaded. The databased named “LCG Trial Women Database (transition period included).csv” is the database which contains the data of the recruited women in the trial. There is one row per women. The databased named “LCG Trial Neonates Database (transition period included).csv” is the database which contains the data of the neonates born from the recruited women. There is one row per neonate.</p> <p>The excel file “Data Dictionary LCG to Share.xlsx” is the data dictionary of the two databases. In the sheet named “Maternal Variables” a list and description of the variables included in the maternal database is included and, in the sheet, named “Neonatal Variables” a list and description of the variables included in the neonatal database is included.</p> <p>Three files of “R” extension and one “rmd” are included. The file named “RunningModelsFunctions.R” is the one use to run the models that are included in the analyses, the file named “2. Final Analysis LCG Trial.R” is the one in which the tables are prepared, and the file named “3. LCG Results Output Final.rmd” is used to export the tables with results. The R file named “funciones.tablas.R” is used in the analyses.</p>
Data_Synthesis_Musa_Trials_BLSD: v1.0.0
<p>Companion code and data for the paper "Rank-based data synthesis of heterogeneous trials to identify the effects of climatic factors on the reaction of <em>Musa</em> genotypes to black leaf streak disease".</p>
Loss of socioemotional and occupational roles in people with Long COVID according to sociodemographic and clinical factors: Secondary data from Randomized Clinical Trial.
<p>This is a cross-sectional study was carried out with the participation of 100 patients diagnosed with Long-COVID, over 18 years of age and attended by Primary Health Care in the Autonomous Community of Aragon. The purpose of this study is to analyse the loss of socioemotional and occupational roles that people with Long COVID have suffered in their lives as a consequence of the disease. As a secondary objective, it was proposed to analyze the sociodemographic and clinical factors associated with this loss of roles. The main study variable was the loss of significant socioemotional and occupational roles of the participants. Sociodemographic and clinical data were also collected through a structured interview.</p>
Mimicking Clinical Trials with Synthetic Acute Myeloid Leukemia Patients Using Generative Artificial Intelligence
<p>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.</p> <p>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.</p> <p> </p> <p><strong>Data Dictionary</strong></p> <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>
ScienceDex guides
Understand access before you commit
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.