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DATASET OF RESPONSIBLE RESEARCH AND INNOVATION IN CITIZEN SCIENCE
<p><span>The research aim was to explore what aspects of citizen science (CS) make the involvement of researchers (the ones who implement CS projects) meaningful in terms of responsible research and innovation (RRI) principles. The following research questions were formulated:</span></p> <p><span>1) How does RRI contribute to the meaningfulness of CS projects and in which CS aspects?</span></p> <p><span>2) What motivates researchers to accommodate RRI principles in CS projects? </span></p> <p><span>3) What impedes researchers in accommodating RRI principles in CS projects?</span></p> <p><span>To answer these research questions, a qualitative research approach was employed using individual semi-structured interviews for data collection. Using a purposive criterion-based sample, inclusion criteria were the following:</span></p> <p><span>(i) European researchers (principal investigators/project managers) that are running (at least) one CS project; </span></p> <p><span>(ii) researchers who may represent different organisational settings with scientific orientation (e.g. academia, museums, and others) within Europe; </span></p> <p><span>(iii) the CS project, started before 2013 (year of introducing the concept of RRI into European Union Research and Innovation (EU R&I) policy) should be ongoing during the research conduct, or the CS project started in the period of 2014–2018 (the year 2014 was a starting point since it is the date of embedding RRI in the EU R&I policy as a mandatory component of all research activities) should be still ongoing; and </span></p> <p><span>(iv) the CS project covers any academic discipline.</span></p> <p><span>To identify potential informants, we used the list of CS projects publicised in Wikipedia (</span><span><a href="https://en.wikipedia.org/wiki/List_of_citizen_science_projects"><span>https://en.wikipedia.org/wiki/List_of_citizen_science_projects</span></a></span><span>) and added CS projects from authors’ home countries. In addition, we posted the invitation to participate in the study in a newsletter within the citizen science community (e.g. ECSA) and in social media targeting specific groups and using hashtags, namely on Facebook and Twitter.</span><span> </span><span>At the end, we identified 117 CS projects relevant to our research aim.</span><span> </span><span>20 CS projects (five females and fifteen males)</span><span> </span><span>consented to take part in the study.</span><span> </span><span>CS projects covered different academic disciplines, such as psychology, zoology, biology, ecology, linguistics, palaeontology, history and others.</span></p> <p><span>We constructed a questionnaire consisting of four items: self-identity and ties with CS, enablers of RRI in CS, limitations of RRI in CS and impact of RRI on CS. Interviews were conducted remotely. The interview language was English, except for one interview that was held in the participant’s first language and then translated into English. Though some interviews had minor language-specific flaws (for most informants English is not a native language), they did not interfere with understanding an informant.</span></p> <p><span>Each interview was audio-recorded, transcribed, and pseudonymized if such request was expressed in the informed consent. Average length of interview was 53 minutes. Non-pseudonymised full interviews contained an average of 6,409 words.</span></p> <p><span>Different strategies were used to validate all interview transcripts for purposes of data accuracy and clarifying inaudible responses (e.g. validation of half of transcripts involved two researchers, then validation of eleven transcripts involved interviewees). </span></p> <p><span>Nine informants allowed to publish pseudonymised transcripts while eight informants preferred to have non-pseudonymised transcripts published. Three informants disagreed to make publish a pseudonymised transcript as open research data.</span></p> <p><span> </span></p> <p><span>The complete research is published as Tauginienė, L., Butkevičienė, E., Heinisch, B., Massetti, L., Ugolini, F., Popov, S. (2024). Making Responsible Research and Innovation Meaningful in Citizen Science. </span><em><span>Science and Public Policy</span></em><span>. https://doi.org/10.1093/scipol/scae078 </span></p>
Dataset on the experimental investigation of the seismic response of moment-resisting steel frames using shaking table tests
<h2>Description:</h2> <p>This dataset contains data from experimental shake table tests conducted on a two-storey steel-frame structure, involving linear sweep, white noise, impulse, and seismic excitations (see 'Load_Protocols_v1.0.0.pdf'). The experiments were carried out using the uniaxial shaking table at the <a href="https://www.lbb.rwth-aachen.de/cms/lbb/der-lehrstuhl/~bjlfvq/geraetezentzrum/?lidx=1">RWTHDynLab</a> of the <a href="https://www.lbb.rwth-aachen.de/go/id/eaxh/">Chair of Structural Analysis and Dynamics (LBB) - RWTH Aachen University</a>, in cooperation with the <a href="https://www.stb.rwth-aachen.de/cms/~iozv/STB/">Institute of Structural Steel (STB) - RWTH Aachen</a> and the <a href="https://www.cwe.rwth-aachen.de/home-2/">Center for Wind and Earthquake Engineering (CWE) – RWTH Aachen</a>.</p> <p>The experimental campaign was developed to gain a better understanding of the interaction between the main structure and non-structural components, to investigate the reliability and accuracy of analytical methods to predict response floor spectra and non-structural component acceleration described in various guidelines and seismic codes. Regarding applications of Structural Health Monitoring the necessity for additional sensors on non-structural components was studied. Three single-degree-of-freedom oscillators (SDOFs) were connected to the upper floor representing non-structural components. The test structure was subjected to a total of twelve earthquake excitations with different spectral properties. The main objectives of the test campaign were:</p> <ul> <li>Identification of the modal properties of the test structure.</li> <li>Measurement of the floor response in terms of acceleration and displacement.</li> <li>Determination of the real floor response spectra based on the measurements of the acceleration sensors installed on the first and second floors.</li> <li>Comparison of the expected peak accelerations from the floor response spectra with the peak accelerations measured by the accelerometers attached to the three SDOFs.</li> </ul> <h3>Test structure:</h3> <p>The test structure consisted of a two-storey steel structure which was stabilised in the direction of excitation by moment resisting frames (MRFs). In the transverse direction the global stability was ensured by concentrically braced frames (braces QRo 50x5). The structure was designed in accordance with provisions of prEN-1998-1 for energy dissipation and ductile seismic behaviour. As ductile members were considered the frame beams so that the columns and connections remain undamaged. An illustration of the test structure is depicted in 'Test_Structure_Sketch_v1.0.0.pdf'. The dimensions of the test structure are: 2.40 m in length, 2.40 m in width and 3.78 m in total height (1st storey: 2.02 m; 2nd storey: 1.76 m). Four large steel I-sections, each with a dead weight of 1700 kg, were attached to the main structure as masses and secured by U-Profiles. A tank with a dead load of approx. 100 kg and a volume of 400 litres was mounted onto the first floor. The tank remained empty during this test series. HEA200 profiles (S355-J2) were selected as column profiles, whereas IPE160 profiles (S235-JR) as frame beams. All main and secondary beams were realised by HEA140 profiles (S235-JR). Four L60x6 bars were arranged in a rhombus shape in the floor plane to ensure a diaphragm action. In the area of the MRF connections, the columns were reinforced with an additional double web plate (t = 10 mm) and with three ribs (t = 10 mm) at the level of the beam top flange, the beam bottom flange and the haunch flange. The beam-to-column connections were classified as full strength and semi-rigid in terms of capacity and stiffness. The critical welds connecting the frame transom to the top plate (t = 15 mm) were designed as full penetration groove welds in accordance with the specifications of Annex E of prEN1998-1 for seismically standardised connections. Twelve M16-10.9 bolts were used to ensure the force transfer between the beam and column. All connections of the secondary beams to the main beams were realised as end plate connections to prevent premature failure due to combined loading by normal and shear forces. The arrangement of the secondary beams and the corresponding force transmission was conceptualized in such a way that the frame beams could be replaced after a series of tests without having to remove the masses and the tank. The columns were hinged to the shaking table (see 'Column_Base_Anchorage_v1.0.0.pdf'). Slots in the anchor plates allow the rotation of the support base around the strong axis of the columns. The anchoring to the shaking table was realised using four M24-8.8 threaded rods. To simulate non-structural components, three SDOFs were attached to the centre of the secondary beams that run across the frame transom on the second floor (see 'Test_Structure_v1.0.0.pdf'). The SDOFs consisted of a flat steel and a mass. Depending on the thickness of the flat steel and the position of the mass, the three SDOFs were calibrated so that the natural frequency of the first SDOF matches the natural frequency of the second modal shape of the test structure in the frame direction, and the natural frequency of the third SDOF corresponds the first natural frequency of the structure. The natural frequency of the second SDOF was set so that it lies between those of the other SDOFs, creating a staggered range of dynamic responses.</p> <h3>Test setup:</h3> <p>The shaking table specifications are:</p> <ul> <li>Table size: 3.0x3.0 m</li> <li>Max. specimen mass: 10 t</li> <li>Max. overturning moment: 30 m t</li> <li>Max. actuator stroke: +/- 250 mm</li> <li>Max. table velocity: +/- 1 m/s at rated load</li> <li>Max. table acceleration: +/- 1g at rated load</li> <li>Test frequency: 0 to 50 Hz</li> </ul> <p>The instrumentation scheme of the test setup consisted of accelerometers and displacement tranducers, measuring the excitation provided by the shaking table and the response of the structure. Regarding the global response of the test structure, the recordings of the accelerometers and displacement tranducers indicated in the uploaded file 'Instrumentation_Scheme_v1.0.0.pdf' are provided. </p> <p>The properties of the accelerometers are:</p> <ul> <li>Type: M3701-series</li> <li>Manufacturer: PCB Piezotronics, Inc.</li> <li>Measurement range: +/- 3g</li> <li>Frequency range: 0-500 Hz</li> <li>Sensitivity: 900 mV/g</li> <li>Resolution: 2.2e-5g</li> <li>Noise: 1 µg/Hz<sup>-0.5</sup></li> </ul> <p>The properties of the displacement tranducers are:</p> <ul> <li>Type: LZW-M-500</li> <li>Manufacturer: WayCon Positionsmesstechnik GmbH</li> <li>Measurement range: +/- 250 mm</li> <li>Linearity: +/- 0.05%</li> <li>Repeatability: 0.01 mm</li> <li>Displacement force: ≤15 N</li> <li>Displacement speed: ≤5 m/s</li> </ul> <h2>Files:</h2> <ul> <li>Column_Base_Anchorage_v1.0.0.pdf <ul> <li>Photo of the column-base anchorage.</li> </ul> </li> <li>Data_v1.0.0.zip <ul> <li>Contains all data files according to the load protocols.</li> <li>The experimental data is provided as .csv files for each load protocol. </li> </ul> </li> <li>Instrumentation_Scheme_v1.0.0.pdf <ul> <li>.pdf file illustrating the sensor placements on the test structure.</li> </ul> </li> <li>Load_Protocols_v1.0.0.pdf <ul> <li>.pdf file listing all load protocols applied to the structure.</li> </ul> </li> <li>References_v1.0.0.bib <ul> <li>Contains a bibtex reference with the associated publications.</li> </ul> </li> <li>Shake_Table.jpg <ul> <li>Photo of the shaking table without any specimen.</li> </ul> </li> <li>Test_Structure_v1.0.0.pdf <ul> <li>Photo of the shaking table including the test structure.</li> </ul> </li> <li>Test_Structure_Sketch_v1.0.0.pdf <ul> <li>.pdf file illustrating the test structure.</li> </ul> </li> <li>Time_Histories_v1.0.0.pdf <ul> <li>.pdf file including plots of the measurement data.</li> </ul> </li> </ul> <h2>File format of the datasets:</h2> <p>The data is stored in .csv files, where each file contains the following columns (see also 'Instrumentation_Scheme_v1.0.0.pdf'):</p> <ul> <li>Time (s): Time in seconds since the start of the test (time step equals 0.0025 s).</li> <li>Acc_0 (m/s2): Acceleration signal measured in m/s<sup>2</sup> on the shaking table in the direction of excitation (Axis A-A).</li> <li>Acc_1 (m/s2): Acceleration response of the structure measured in m/s<sup>2</sup> on the first floor in the direction of excitation (Axis A-A).</li> <li>Acc_2 (m/s2): Acceleration response of the structure measured in m/s<sup>2</sup> on the second floor in the direction of excitation (Axis A-A).</li> <li>Acc_L (m/s2): Acceleration response of SDOF I measured in m/s<sup>2</sup> in the direction of excitation.</li> <li>Acc_F (m/s2): Acceleration response of SDOF II measured in m/s<sup>2</sup> in the direction of excitation.</li> <li>Acc_H (m/s2): Acceleration response of SDOF III measured in m/s<sup>2</sup> in the direction of excitation.</li> <li>Acc_G (m/s2): Acceleration response of the structure measured in m/s<sup>2</sup> on the second floor in the direction of excitation (Axis B-B).</li> <li>Acc_J (m/s2): Acceleration response of the structure measured in m/s<sup>2</sup> on the second floor in the transverse direction of excitation (Axis B-B).</li> <li>Dis_0 (mm): Displacement signal measured mm on the shaking table in the direction of excitation (Axis A-A).</li> <li>Dis_1 (mm): Displacement response of the structure measured in mm on the first floor in the direction of excitation (Axis A-A).</li> <li>Dis_2 (mm): Displacement response of the structure measured in mm on the second floor in the direction of excitation (Axis A-A).</li> </ul> <p>These data files can easily be uploaded using the pandas library in Python. For example by:</p> <pre><code>import pandas as pd df = pd.read_csv('1_IM_4mm.csv') time = df["Time (s)"] acc_0 = df["Acc_0 (m/s2)"] dis_0 = df["Dis_0 (mm)"]</code></pre> <h2>Contact:</h2> <p>Please send your enquiries regarding the shaking table to <a href="dynamics@lbb.rwth-aachen.de">dynamics@lbb.rwth-aachen.de</a>. Further information can be found on our <a href="https://www.lbb.rwth-aachen.de/cms/lbb/der-lehrstuhl/~bjlfvq/geraetezentzrum/?lidx=1">website</a>.</p> <h2>Usage/License:</h2> <ul> <li>The data is licensed under CC BY-SA 4.0.</li> <li>If you have used our data and are publishing your work, we ask you to please reference both <ul> <li>this database by its DOI, and</li> <li>any publication that is associated with the experiments. See the "References_v1.0.0.bib" for the associated publication references.</li> </ul> </li> </ul> <h2>Fundings:</h2> <ul> <li>Deutsche Forschungsgemeinschaft - <em>Grant number: INST 222/1161-1 FUGG</em>. Einaxialer Schwingtisch für dynamische Modell- und Bauteilversuche.</li> <li>Bundesministerium für Bildung und Forschung - <em>Grant number: 03G0892A</em>. ROBUST – Nutzerorientiertes Erdbebenfrühwarnsystem mit intelligenten Sensorsystemen und digitalen Bauwerksmodellen – Entwicklung Installation und Anwendung von sensorbasierten Monitoringsystemen mit BIM-Integration zur Echtzeit-Schadenerkennung in kritischen Infrastrukturen.</li> </ul>
Database of permacultural adoption responses in Mexicali, BC, Mexico. based on Circular Economy, Knowledge Management, and Sustainability policies
<p>Database documenting the perspectives of citizens in Mexicali, Baja California, Mexico, regarding the adoption of permaculture practices. The study is analyzed through the lenses of Knowledge Management, Circular Economy, and Sustainability Policies. The data was collected during the summer of 2024. </p>
Dataset for "Reduction-responsive immobilised and protected enzymes" research article
<p>The dataset for the paper titled "Reduction-responsive immobilised and protected enzymes".<br>The dataset includes the following items:<br><br>1. Unprocessed tif files (4 items) of the scanning electron microscopy (SEM) micrographs;<br>These SEM micrographs are presented in Fig. 3a and Fig. 3b in the manuscript and also in Fig. S1a and Fig. S1b in the supporting information document.<br><br>2. "Reduction-responsive immobilised and protected enzymes" xls file (1 file) including 4 datasheets;<br>- The "<strong>Cell experiment" </strong>sheet includes the cell viability results in Fig. 5d and Fig. S5.<br>- The "<strong>Enzyme activity - B-Gal"</strong> sheet includes all the results regarding the B-Gal enzyme activity in Fig. 4, main text, and Fig. S3.<br>- The "<strong>Enzyme activity - ASNase"</strong> sheet includes all the results regarding the ASNase enzyme activity in Fig. 5b, Fig. 5c, and Fig. S4.<br>- The<strong> "B-Gal and ASNase layer growth"</strong> sheet includes all the results regarding the layer growth reactions and kinetics of B-Gal and ASNase enzymes in Fig. 3d, Fig. 5a, and Fig. S1d.<br><br>3. "SNP and layer growth analysis" xls file (1 file) including 13 datasheets;<br>These datasheets contain the raw data of the size measurements of the silica nanoparticles conducted on the SEM micrographs before and after layer growth for each sampling timepoint for both B-Gal and ASNase enzymes with glutaraldehyde (Glu) and DSP as linkers.<br>Note: These data were used to make the graphs in the "<strong>B-Gal and ASNase layer growth" </strong>sheet.</p>
TCGA Chemotherapy Response Dataset
<p>Dataset on chemotherapeutic drug responses in TCGA cancer patients, cross-referenced for a hit in TCIA.at database, consisting of clinical (TCGA), cancer tissue gene-expression (TCGA) and tumor-immunome (TCIA) features. The dataset consists of 5 common chemotherapy agents, 3 CRC agents (FOLFOX, 5FU, Oxaliplatin) and 2 Lung agents (Carboplatin, Cisplatin). FOLFOX as a combinational therapy or regimen, was compiled from timings of monotherapies given to patients and as such is a novel dataset derived from TCGA data. FOLFOX dataset is primarily firstline treatment, while other drugs are not to be interpreted as firstline treatments. Drug datasets are individually available in own CSV files.</p> <p><strong>Citation</strong><br> Dalibor Hrg, Balthasar Huber, Lukas A. Huber. (2020). TCGA Chemotherapy Response Dataset. Zenodo. <a href="http://doi.org/10.5281/zenodo.3719291">http://doi.org/10.5281/zenodo.3719291</a></p> <p>The results here are in whole or part based upon data generated by the TCGA Research Network: <a href="https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga">https://www.cancer.gov/tcga</a>.</p> <p><strong>License </strong><br> CC BY-SA 4.0 International <a href="https://creativecommons.org/licenses/by-sa/4.0">https://creativecommons.org/licenses/by-sa/4.0</a>. Authors take no liability for any use of this data.</p> <p><strong>Contributions</strong><br> D. Hrg and B. Huber acknowledge major and equal work effort: data understanding, data science and dataset preparation (monotherapies and FOLFOX); L. A. Huber: help with dictionary of drug names and curration/cleaning of FOLFOX entries, clinical validation.</p> <p><strong>Contact & Maintenance</strong><br> <a href="mailto:dalibor.hrg@gmail.com">dalibor.hrg@gmail.com</a><br> dalibor.hrg@i-med.ac.at</p>
Effects of Sinusoidal Vibrations on the Motion Response of Honeybees - datasets
<p>data sets on the effects of sinusoidal stimuli on the motion activity of honeybees. For more details please refer to </p> <p>Stefanec, M., Oberreiter, H., Becher, M. A., Haase, G., & Schmickl, T. (2021). Effects of Sinusoidal Vibrations on the Motion Response of Honeybees. <em>Frontiers in Physics</em>, <em>9</em>, 318.</p> <p>amplitude_experiments.csv contains the data of measured motion activity according to the pixel-based motion index in a certain region of interest in regards to different amplitudes at different frequencies.</p> <p>amplitude_experiments_with_velocity.csv contains the data of measured motion activity according to the pixel-based motion index in a certain region of interest in regards to different amplitudes at different frequencies as well as a post-hoc derived intensity measurement at a certain amplitude. This intensity measurement was detected by laser vibrometer on the surface of the honeycomb and represents the measurement at the point in the region of interest that had the highest intensity. This measurement could not be made during the experiments on the animals, but had to be made post-hoc, since a laser vibration measurement was only possible without animals passing through the laser point.<br> <br> frequency_experiments.csv contains the data of measured motion activity according to the pixel-based motion index in a certain region of interest in regards to different frequency stimuli.</p>
Inferring size-based functional responses from the physical properties of the medium
<p>Databases used to test the model described in the article "Inferring size-based functional responses from the physical properties of the medium", Frontiers in Ecology and Evolution. Please read the "Readme.pdf" file for detailed information. This file explains all the variables and provides full references for the data in each of the datasets.</p> <p>"Portalier_et_al_2021_Species_Speeds.csv" provides species speeds according to body size for numerous species in aquatic systems.</p> <p>"Portalier_et_al_2021_Predator_Prey_Interactions.csv" provides attack rates, capture probabilities and handling times for numerous predator-prey interactions in aquatic systems.</p>
EEG Data for Emotive Response to Robot Facial Expressions
<p>This dataset consists of EEG recorded during visual human-robot interaction from 10 healthy participants to investigate the emotive response in EEG to different robot facial expressions. Participants observed four different facial expressions (angry, happy, sad and surprised along with neutral expression) displayed by the social robot Miko on its digital screen. EEG was recorded from 16 unipolar channels in frontal, central, temporal, parietal, and occipital locations . During each trial, an emotion stimulus was displayed for approximately 4s followed by 4s break during which the Miko robot displayed neutral expression and blinked regularly. Emotions were displayed in random order. Total of 240 EEG trials were recorded from each participant with 60 trials per emotion. The dataset provides raw minimally filtered EEG along with cleaned EEG with artefacts removal using ICA with sampling frequency of 128 Hz, and corresponding stimulus onset markers. Please refer to README file for further details and example code.</p> <p><em>Please cite the original publication:</em></p> <p>M. Wairagkar et al., "Emotive Response to a Hybrid-Face Robot and Translation to Consumer Social Robots," <em>IEEE Internet of Things Journal</em>, DOI: <a href="https://doi.org/10.1109/JIOT.2021.3097592">10.1109/JIOT.2021.3097592</a>.</p> <p><em>Preprint: </em></p> <p>M. Wairagkar et al., "Emotive Response to a Hybrid-Face Robot and Translation to Consumer Social Robots," <a href="https://arxiv.org/abs/2012.04511">arXiv:2012.04511</a></p>
DNA methylation dynamics during stress-response in woodland strawberry (Fragaria vesca)
<p><strong>Genome sequence and annotation of Fragaria vesca cv. Reine des Vallées</strong></p> <p>In order to generate a reference genome for Fragaria vesca cv. Reine des Vallées, we used MinIon long-read sequencing data to substitute the <em>F. vesca</em> genome v.4.0.a2 genome. The detailed method used to obtain these results were the following:</p> <p><em>Genome sequencing and assembly NIL Fb2</em></p> <p>Genomic DNA from strawberry plants was extracted by a Hexadecyltrimethylammonium bromide (Cetrimonium bromide, CTAB) modified protocol (Healey, Furtado, Cooper, & Henry, 2014) and purified with Agencourt AMPure XP beads (cat# A63880). Long-read sequencing was performed for the genome assembly; Genomic DNA by Ligation (Oxford Nanopore, cat# SQK-LSK109) library was prepared as described by the manufacturer and sequenced on a MinION for 72 h (Oxford Nanopore).</p> <p><em>Reference genome polishing</em></p> <p>Reads obtained from nanopore were filtered with Filtlong v0.2.1 (<a href="https://github.com/rrwick/Filtlong">https://github.com/rrwick/Filtlong</a>) using --min_mean_q 80 and --min_length 200. Cleaned reads were then aligned to the most recent version of the <em>F. vesca</em> genome v4.0.a2, downloaded from the Genome Database for Rosaceae (GDR) (<a href="https://www.rosaceae.org/species/fragaria_vesca/genome_v4.0.a2">https://www.rosaceae.org/species/fragaria_vesca/genome_v4.0.a2</a>), using minimap2 v2.21 (H. Li, 2018) with parameters -aLx map-ont --MD -Y. The generated BAM file was then sorted and indexed with samtools v1.11 (H. Li et al., 2009). We used mosdepth v0.3.1 (Pedersen & Quinlan, 2018) to verify that coverage on chromosomic scaffolds was over 50 X. Sniffles v1.0.12a (Sedlazeck et al., 2018) with parameters -s 10 -r 1000 -q 20 --genotype -l 30 -d 1000 was used to detect structural variations larger than 30 bp. The VCF files obtained from Sniffles was sorted and filtered with BCFtools v1.14 (Danecek et al., 2021) to keep only structural variants (SV) with smaller than 200,00 bp (we observed that larger SV were most of the time false positive caused by misalignments in regions with gaps or Ns), supported by 10 or more reads and with allelic frequencies above 0.8 (we were interested in homozygous changes). The complete filtering command used is “bcftools view -q 0.8 -Oz -i '(SVTYPE = "DUP" || SVTYPE = "INS" || SVTYPE = "DEL" || SVTYPE = "TRA" || SVTYPE = "INV" || SVTYPE = "INVDUP") && %FILTER = "PASS" && FMT/DV>9 && SVLEN>29 && SVLEN<200000' “</p> <p>From the VCF listing all the structural variants that we detected in our <em>F. vesca </em>accession, we generated a substituted genome version based on the reference <em>F. vesca</em> genome v.4.0.a2. The reference genome was first indexed with samtools faidx v1.11(Danecek et al., 2021) and a sequence dictionary was generated with Picard CreateSequenceDictionary v2.25.6 (<a href="https://broadinstitute.github.io/picard">https://broadinstitute.github.io/picard</a>). The VCF containing the SV produced from our Nanopore sequencing was also indexed with gatk (Van der Auwera GA & O'Connor BD, 2020) IndexFeatureFile v4.2.0.0 (<a href="https://gatk.broadinstitute.org/hc/en-us/articles/360037262651-IndexFeatureFile">https://gatk.broadinstitute.org/hc/en-us/articles/360037262651-IndexFeatureFile</a>). FastaAlternateReferenceMaker v4.2.0.0 (<a href="https://gatk.broadinstitute.org/hc/en-us/articles/360037594571-FastaAlternateReferenceMaker">https://gatk.broadinstitute.org/hc/en-us/articles/360037594571-FastaAlternateReferenceMaker</a>) was then run with the reference genome and the VCF file to generate a substituted genome representative of our <em>Fragaria</em> accession.</p> <p>As substituting our genome with the detected structural variants changes genomic coordinates, we also corrected the public GFF genome annotation of <em>F. vesca</em> (Y, Pi, Gao, Liu, & Kang, 2019) using liftoff v1.6.1 (Shumate & Salzberg, 2021). Liftoff also detects and annotates duplications within the substituted genome.</p> <p>Transposable elements annotation was carried out using the EDTA transposable element annotation pipeline v. 1.9.6 (S. Ou et al., 2019) on the substituted genome using default parameters<em>.</em></p> <p><strong>Differentially methylated regions</strong></p> <p>The file Stress_vs_control_DMRs.zip file contains the DMRs that were called using the reads submitted to ENA (ERP135585) and obtained as follows:</p> <p>First, bedGraph files from wgbs pipeline were pre-filtered for a minimum coverage of 5 reads using awk command. These output files were then used as input for the EpiDiverse/dmr bioinformatics analysis pipeline for non-model plant species to define DMRs (Nunn <em>et al</em>., 2021) with default parameters (minimum coverage threshold 5; maximum q-value 0.05; minimum differential methylation level 10%; 10 as minimum number of Cs; Minimum distance (bp) between Cs that are not to be considered as part of the same DMR is 146 bp). The pipeline uses metilene v.0.2.6.1 (<a href="https://www.bioinf.uni-leipzig.de/Software/metilene/">https://www.bioinf.uni-leipzig.de/Software/metilene/</a>) for pairwise comparison between groups and R-packages ggplot2 v.3.3.5 and gplots v.3.1.1, for visualization results (Fig. S1). Based on our <em>F. vesca</em> genome transcript annotation and methylation data (overlapped regions with DNA methylation cytosines and DMRs), we detected the methylated genes, promoters, 3’ UTRs, 5’UTR and transposable elements in strawberry. Global DNA methylation and DMR plots were performed with R-package ggplot2. Gene analyses by methylation patterns and analysis of per-family TE DNA methylation profiles were performed with deepTools v.3.5.0 (Ramírez <em>et al</em>., 2014). DMRs comparison between treatments were done by the Venn diagram v.1.7.0 R-package.</p> <p>We produced several genome browsers tracks with DMRs that we integrated in our local instance of JBrowse available at the following url: <a href="https://jbrowse.agroscope.info/jbrowse/?data=fragaria_sub">https://jbrowse.agroscope.info/jbrowse/?data=fragaria_sub</a></p>
Genetic association analysis of anti-VEGF treatment response in neovascular age-related macular degeneration
<p>Summary statisics of an association study of 6,908,005 genetic variants with anti-VEGF nAMD treatment response in 179 treatment-naïve nAMD probands. This dataset supplements the publication "Genetic Association Analysis of Anti-VEGF Treatment Response in Neovascular Age-Related Macular Degeneration" (DOI: 10.3390/ijms23116094). Details regarding the methods and version numbers can be found in the corresponding manuscript.</p>
Bridging the gap between single nanoparticle imaging and global electrochemical response by correlative microscopy assisted by machine vision
<p>The data in this repository corresponds to experimental data: linear sweep voltammetry, optical movie and the database of the SEM images. They support the findings of a study discussed in the article by Godeffroy et al. published in Small Methods with the doi: http:/doi.org/10.1002/smtd.202200659. The data analysis to reproduce the results presented in the article has been carried out by homemade Python program routines also provided in this repository. The descirption of each routine is also provided in a text file.</p>
Tensile response of low clinker UHPFRC subjected to fully restrained shrinkage
<p>This dataset is from an experimental campaign on 6 free and 6 restrained TSTM (Temperature Stress Testing Machine) specimen made of UHPFRC (Ultra High Performance Fiber Reinforced Concrete), subjected to partial and fully restrained conditions, under quasi-isothermal conditions at 20°C, performed by Mohamed Hafiz in the framework of his doctoral thesis. The development of autogenous shrinkage and corresponding eigenstresses under various degrees of restraint were studied for two types of mixes: Mix I with type I cement and silica fume and Mix II with silica fume and 50% mass replacement of type I cement with limestone filler. Amir Hajiesmaieli developed Mix II.</p>
IPBES Data Management Tutorials - Session 2.3: Roles and responsibilities
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data and knowledge management policy. They cover topics ranging from data and knowledge management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management Policy </em>chapter provides an introduction of the IPBES data management policy. It discusses why IPBES has a data management policy and who is responsible for what in the implementation and further development of this policy. </p> <p>This session on <em>roles and responsibilities </em>outlines the responsibilities of all involved players as stipulated in the IPBES data management policy. These are discussed in light of the importance for IPBES experts.</p> <p>Following version 2.0 of the data and knowledge management policy, a new PDF supplement has been added which covers the roles and responsibilities of the task force and technical support unit on Indigenous and local knowledge. </p>
Responsive Environmental Assessment Commercially Hosted (REACH)
<p>This is the historical REACH data release. These are the “v3” files produced at The Aerospace Corporation, spanning March 2017 until December 2019. It is accompanied by a README (PDF), which includes a brief mission description, describes the features of the collection of dosimeters, discusses data availability throughout the dataset, describes data quality flags, and concludes with a data dictionary.</p>
Supporting data for: Type 1 diabetes risk genes mediate pancreatic beta cell survival in response to proinflammatory cytokines
<p><strong>SUMMARY OF THE STUDY</strong></p> <p>We combined functional genomics and human genetics to investigate processes that affect type 1 diabetes (T1D) risk by mediating beta-cell survival in response to proinflammatory cytokines. We mapped 38,931 cytokine-responsive candidate <em>cis-</em>regulatory elements (cCREs) in beta-cells using ATAC-seq and snATAC-seq and linked them to target genes using co-accessibility and HiChIP. Using a genome-wide CRISPR screen in EndoC-βH1 cells we identified 867 genes affecting cytokine-induced survival, and genes promoting survival and up-regulated in cytokines were enriched at T1D risk loci. Using SNP-SELEX, we identified 2,229 variants in cytokine-responsive cCREs altering transcription factor (TF) binding, and variants altering binding of TFs regulating stress, inflammation and apoptosis were enriched for T1D risk. At the 16p13 locus, a fine-mapped T1D variant altering TF binding in a cytokine-induced cCRE interacted with <em>SOCS1</em>, which promoted survival in cytokine exposure. Our findings reveal processes and genes acting in beta-cells during inflammation that modulate T1D risk.</p> <p><strong>DESCRIPTION OF FILES:</strong></p> <ul> <li>Supplementary Data 1. List of islet cCREs annotated with cell type and cytokine response - also in GSE205853</li> <li>Supplementary Data 2. Coaccessible sites in untreated beta cells and promoter annotations - also in GSE205853</li> <li>Supplementary Data 3. Coaccessible sites in cytokine-treated beta cells and promoter annotations - also in GSE205853</li> <li>Supplementary Data 4. Coaccessible sites in cytokine treated and untreated beta cells and promoter annotations - also in GSE205853</li> <li>Supplementary Data 5. Chromatin interactions in EndoC-BH1 cells - also in GSE205853</li> <li>Supplementary Data 6. Variants selected for SNP-SELEX assay </li> <li>Supplementary Data 7. Variants with TF binding and allelic binding results from SNP-SELEX</li> <li>Supplementary Data 8. snATAC-seq barcodes and metadata - also in GSE205853</li> <li>Supplementary Data 9. CRISPR-KO screen results - also in GSE205853</li> <li>Supplementary Data 10. Bulk ATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 11. Bulk RNA-seq count matrix - also in GSE205853</li> <li>Supplementary Data 12. Alpha cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 13. Acinar cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 14. Beta cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 15. Stellate cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 16. Endothelial cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 17. Delta cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 18. Luciferase assay rs10483809</li> <li>Supplementary Data 19. SOCS1 knockdown qPCR results</li> <li>Supplementary Data 20. SOCS1 knockdown Apotracker (flow-cytometry)results</li> </ul> <p><strong>Raw data deposited at GEO, accessions GSE205853 and GSE118725.</strong></p> <p><em>Please refer to publication and GEO for details on methods.</em></p>
Catching the audience in a job interview: Effects of emotion regulation strategies on subjective, physiological, and behavioural responses
<p>Dataset used in the publication: Santos, A. C., Arriaga, P., & Simões, C. (2021). Catching the audience in a job interview: Effects of emotion regulation strategies on subjective, physiological, and behavioural responses. Biological Psychology, 162, 108089. <a href="https://doi.org/10.1016/j.biopsycho.2021.108089" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.biopsycho.2021.108089</a></p> <p>Includes: data in SPSS and CSV and codebook. </p> <p>In the emotion regulation process more than one strategy is often used, though studies continue to rely on the manipulation of one strategy alone. This study compares the effects of Combined Cognitive Reappraisal (CCR: acceptance and reappraise via perspective-taking) and suppression using the Trier Social Stress Test (TSST). One hundred participants were randomly assigned to one of the two groups and subjective, physiological, and behavioural data were recorded. Continuous electrocardiography was recorded to measure heart rate variability (HRV) and stress levels. Affective ratings were provided before and after the TSST. Behavioural expressions were videotaped and analysed independently. Trait social anxiety/fear, age and gender entered as covariates. Although no group differences were found on affective ratings, the CCR group presented less physiological stress, higher HRV, their speech was better perceived, displayed more affiliative smile and hand gestures. Results suggested that CCR is more appropriate than suppression for managing social stress situations.</p>
PsPM-RRM3: SCR, ECG and respiration measurement in response to aversive/arousing IAPS pictures, and neutral/aversive sounds
<p>This dataset includes skin conductance response (SCR), electrocardiogram (ECG) and respiration measurements for each of 20 healthy unmedicated participants (10 males and 10 females aged 25.3 +/- 5.1 years; male/female numbers were misprinted in Bach et al. 2016) in response to negatively and positively arousing IAPS pictures and neutral (65 dB) and aversive (85 dB) white noise sounds. All stimuli had 1 s duration. ITI was selected randomly on each trial from 40 s, 45 s or 50 s.</p>
Auditory stimuli suppress contextual fear responses in safety learning independent of a possible safety meaning
<p>This repository stores the raw data that gave rise to the study by Mombelli et al. (2024) (Title: Auditory stimuli suppress contextual fear responses in safety learning independent of a possible safety meaning; DOI: 10.3389/fnbeh.2024.1415047, Journal: Frontiers in Behavioral Neuroscience). Below we supply information on the provided metadata files which, in turn, refer to individual raw data files.</p> <p><strong>General structure of the repository:</strong></p> <p>· the raw data is organized in 5 subsets defined by the figures or supplementary figures they contribute to. Each subset is documented by its own metadata file. Raw data files were compressed into ZIP archives, one per subset;</p> <p>· the metadata files listing names of the individual data files are provided in “.csv” format, one per data subset. Field separator: comma;</p> <p>· the dataset is accessible at the following doi: 10.5281/zenodo.13524007</p> <p> </p> <p><strong>Description of the non-textual data formats:</strong></p> <p>· video recordings of animal behavior were provided as unmodified ".wmv" files created by the VideoFreeze acquisition software (Med Associates Inc). Video stream parameters: wmv3 codec, color space yuv420p, 320x240 pixels, 30 fps, bitrate 300 kb/s.</p> <p>· movement traces were obtained from the videos, as described in the Methods section (Mombelli et al., 2024).</p>
Behavioral and fMRI Data: Nurturing the reading brain: Home literacy practices are associated with children's neural response to printed words through vocabulary skills
<p>This is the behavioral and fMRI dataset described in "Nurturing the reading brain: Home literacy practices are associated with children’s neural response to printed words through vocabulary skills". </p> <p>Because of anonymization concerns within the framework of EU privacy regulations (<a href="https://gdpr-info.eu">GDPR</a>), we cannot provide raw MRI data. Therefore, the fMRI data consists of individual pre-processed volumes, normalized into the MNI template (see paper for details about the preprocessing pipeline). Anonymized behavioral data and first level analyses are also provided for each participant (SPM.mat file as well as beta, con, spmT, RPV and ResMS files). Note that the dataset also include runs and GLM results for a third task (Dots) that was not analyzed in the paper. Finally, the <a href="https://www.psychopy.org">PsychoPy</a> implementation of the tasks is also provided. If you have any questions, please send an email to jerome.prado [at] univ-lyon1.fr. </p> <p><strong>IMPORTANT:</strong></p> <p>In accordance with EU privacy regulations, we ask that you sign and return a Data Use Agreement (DUA) before downloading the data. You can download the DUA <a href="https://zenodo.org/record/4965716/files/DUA.pdf?download=1">here</a>. Please, sign it and send it to jerome.prado [at] univ-lyon1.fr.</p>
Dataset. Responses to digital disinformation as part of hybrid threats: an evidence-based analysis on the effects of disinformation and the effectiveness of fact-checking/debunking
<p>Dataset from the meta-analysis carried out in the article Responses to digital disinformation as part of hybrid threats: a systematic review on the effects of disinformation and the effectiveness of fact-checking / debunking using the EU-HYBNET Meta-Analysis Survey Instrument for Evaluating the Effects of Disinformation and the Effectiveness of counter-responses</p>
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.