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Fe chemical speciation collected using trace metal rosette in the Southern Ocean during the austral summer of 2016/2017, on board the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>Fe chemical speciation of filtered seawater data are presented in this dataset, resulting from samples collected from a trace metal rosette on board the Antarctic Circumnavigation Expedition (ACE). During the austral summer of 2016/2017, seawater samples were collected from the Atlantic and Indian Ocean sectors of the Southern Ocean and dissolved Fe concentration, iron-binding organic ligands concentration and the conditional stability constant of Fe’ are presented here.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_fe_chemical_speciation.csv, data file, comma-separated values</li> <li>figure1.pdf, metadata, portable document format</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This Fe chemical speciation dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Dataset: Environmental drivers of under-ice phytoplankton bloom dynamics in the Arctic Ocean
<p>This dataset is linked to this manuscript entitled "Environmental drivers of under-ice phytoplankton bloom dynamics in the Arctic Ocean" published in Elementa: Science of the Anthropocene (<a href="http://doi.org/10.1525/elementa.430">http://doi.org/10.1525/elementa.430</a>). Please find the abstract below:</p> <p>The decline of sea-ice thickness, area, and volume due to the transition from multi-year to first-year sea ice improves the under-ice light environment for pelagic Arctic ecosystems. One unexpected and direct consequence of this transition, the proliferation of under-ice phytoplankton blooms (UIBs), challenges the paradigm that waters beneath the ice pack harbor little planktonic life. Little is known about the diversity and spatial distribution of UIBs in the Arctic Ocean, or the environmental drivers behind their timing, magnitude, and species composition. Here, we compiled a unique and comprehensive dataset from seven major research projects in the Arctic Ocean (11 expeditions, covering the spring sea-ice-covered period to summer ice-free conditions) to identify the environmental drivers responsible for initiating and shaping the magnitude and assemblage structure of UIBs. The temporal dynamics behind UIB formation related to the ways that snow and sea-ice conditions impact the under-ice light field. In particular, the onset of snowmelt significantly increased under-ice light availability (> 0.1–0.2 mol photons m<sup>–2</sup> d<sup>–1</sup>), marking the concomitant termination of the sea-ice algal bloom and initiation of UIBs. At the pan-Arctic scale, bloom magnitude (expressed as maximum chlorophyll <em>a </em>concentration) was predicted best by winter water Si(OH)<sub>4</sub> and PO<sub>4</sub><sup>3–</sup> concentrations, as well as Si(OH)<sub>4</sub>:NO<sub>3</sub><sup>–</sup> and PO<sub>4</sub><sup>3–</sup>:NO<sub>3</sub><sup>–</sup><sub> </sub>drawdown ratios, but not NO<sub>3</sub><sup>–</sup> concentration. Two main phytoplankton assemblages dominated UIBs (diatoms or <em>Phaeocystis</em>), driven primarily by the winter nitrate:silicate (NO<sub>3</sub><sup>–</sup>:Si(OH)<sub>4</sub>) ratio and the under-ice light climate. <em>Phaeocystis</em> co-dominated in low Si(OH)<sub>4</sub> (i.e., NO<sub>3</sub>:Si(OH)<sub>4</sub> molar ratios > 1) waters, while diatoms contributed the bulk of UIB biomass when Si(OH)<sub>4</sub> was high (i.e., NO<sub>3</sub>:Si(OH)<sub>4</sub> molar ratios < 1). The implications of such differences in UIB composition could have important ramifications for Arctic biogeochemical cycles, and ultimately impact carbon flow to higher trophic levels and the deep ocean.</p>
Equilibrium ellipsoids
<p>Equilibrium rubble pile ellipsoids. The models are obtained through numerical N-body simulations, using the GRAINS software. Simulations are performed using non-spherical particles, mutually interacting under contact/collisions and self-gravity. See Ferrari & Tanga 2020 (doi: 10.1016/j.icarus.2020.113871) for analysis and discussion of results. See Ferrari et al 2020 (doi: 10.1093/mnras/stz3458), Ferrari et al 2016 (doi: 10.1007/s11044-016-9547-2) for further details on the numerical model.</p>
Hydrolysable carbohydrate data collected from the trace metal rosette in the Southern Ocean during the austral summer of 2016/2017, on board the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>Hydrolysable carbohydrate (referred to as TPZT from the analytical methodology used) is part of the labile pool of dissolved organic carbon that is excreted by most (micro)organisms or released by continental margins/sediments. It is a carbon source for heterotrophic bacteria. These carbohydrates could also potentially bind iron and act as an iron binding ligand.</p> <p>This data is used to explore the nature of iron ligands and relate to biological and chemical oceanography.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_hydrolysable_carbohydrates_tpzt_data.csv, data file, comma-separated values</li> <li>ace_hydrolysable_carbohydrates_tpzt_data_visual_summary.png, metadata, portable network graphics</li> <li>README.txt, metadata, text format</li> <li>data_file_header.txt, metadata, text format</li> <li>change_log.txt</li> </ul> <p><strong>Dataset license</strong></p> <p>This hydrolysable carbohydrate dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p> <p><strong>Change log</strong></p> <p>v1.1 - permissions changed to open access (CC BY 4.0 license) and small changes</p> <ul> <li>add license to README.txt</li> <li>format of data_file_header.txt</li> <li>add Frictionless Data schema files</li> </ul> <p>v1.0 - initial release of dataset</p>
Northwest Europe NEMO-ERSEM ocean model hindcast and climate projection under RCP8.5
<p>Dataset of model hindcast and climate projection data from a NEMO-ERSEM simulation of the 7km-resolution Atlantic Margin Model (AMM7). Model description and data are presented in </p> <p>Wakelin, S. L., Y. Artioli, J. T. Holt, M. Butenschön, and J. Blackford (2020), Controls on near-bed oxygen concentration on the Northwest European Continental Shelf under a potential future climate scenario, Progress in Oceanography, 102400. doi: https://doi.org/10.1016/j.pocean.2020.102400.</p> <p>Coupled NEMO-ERSEM model simulations are used to study temperature, salinity and near-bed oxygen concentrations on the northwest European Continental Shelf (NWES). Data are from a hindcast (1980 to 2007) and a climate projection (1980 to 2099) under the RCP8.5 climate emissions scenario.</p> <p>The climate projection (1980 to 2099) under the RCP8.5 climate emissions scenario is described as experiment E1 in</p> <p>Holt, J., J. Polton, J. Huthnance, S. Wakelin, E. O'Dea, J. Harle, A. Yool, Y. Artioli, J. Blackford, J. Siddorn, and M. Inall (2018), Climate-Driven Change in the North Atlantic and Arctic Oceans Can Greatly Reduce the Circulation of the North Sea, Geophysical Research Letters, 45(21), 11,827-811,836. doi: 10.1029/2018gl078878.</p> <p>The dataset consists of </p> <ul> <li>Hindcast simulation data</li> </ul> <ol> <li>AMM7_hindcast_3D_S_1980_2007.nc - monthly mean salinity fields.</li> <li>AMM7_hindcast_3D_T_1980_2007.nc - monthly mean temperature fields.</li> <li>AMM7_hindcast_near_bed_O2o_1980_2007.nc - near-bed oxygen concentrations on the NWES.</li> </ol> <ul> <li>Climate projection data</li> </ul> <ol> <li>AMM7_RCP8_5_3D_S_1980_2099.nc - monthly mean salinity fields.</li> <li>AMM7_RCP8_5_3D_T_1980_2099.nc - monthly mean temperature fields.</li> <li>AMM7_RCP8_5_3D_U_1980_2099.nc - monthly mean eastwards currents.</li> <li>AMM7_RCP8_5_3D_V_1980_2099.nc - monthly mean northwards currents.</li> <li>AMM7_RCP8_5_near_bed_1980_2099.nc - monthly mean near-bed oxygen concentrations and near-bed bacterial respiration on the NWES.</li> <li>AMM7_RCP8_5_netPP_1980_2099.nc - monthly mean depth integrated net primary production.</li> </ol>
Data presented in Devenish and Cerminara, Journal of Geophysical Research Atmosphere, 2021. doi:10.1029/2020JD033699
<p>The files contain the raw data of the atmospheric and concentration profiles respectively used and calculated by the LES and LSM simulations presented in Devenish and Cerminara (2020).</p> <p>The concentration data have been stored in two ASCII columns, the first being the elevation with respect to the vent level, and the second the concentration normalised by the initial concentration, where the initial concentration is the product of the source mass flux and the exit velocity.</p> <p>For the two cases of the intercomparison study, the initial mass flux is 1.5e6 kg/s and 1.5e9 kg/s for the weak and strong cases, respectively. The respective exit velocities are 135 m/s and 275 m/s.</p> <p>For the twenty cases with ambient wind, the initial mass flux and exit velocities can be extracted from the information given in the paper.</p> <p>Additional information can be found in Costa et al. (2016) and Aubry et al. (2019).</p>
A global map of terrestrial habitat types
<p>We provide a global spatially explicit characterization of 47 (version 001) terrestrial habitat types, as defined in the International Union for Conservation of Nature (IUCN) habitat classification scheme, which is widely used in ecological analyses, including for assessing species’ Area of Habitat. We produced this novel habitat map by creating a global decision tree that intersects the best currently available global data on land cover, climate and land use. The maps broaden our understanding of habitats globally, assist in constructing area of habitat (AOH) refinements and are relevant for broad-scale ecological studies and future IUCN Red List assessments. We hope that these data and outlined framework will spur further development of biodiversity-relevant habitat maps at global scales. An interactive interface helping to navigate the map can be found at on the Naturemap website ( https://explorer.naturemap.earth/map).</p> <p>Provided is the code to recreate the map (to made available soon), the global composite image at native -100m Copernicus resolution for level 1 and level 2 and layers of aggregated fractional cover (unit: [0-1] * 1000) at 1km for level 1 and level 2.</p> <p>Starting with version 004 there changemasks for the years 2016, 2017, 2018 and 2019 are supplied. Changemasks for the composite masks show the changed grid cells and their new values with earlier years being nested in later years, e.g. using the changemask for 2019 includes all changes up to 2019. For the fractional cover estimates at ~1km resolution, new fractional cover changemasks are supplied as subtraction (before - after) between the previous and current year (unit range: [-1 to 1] * 1000).</p> <p>We highlight that only changes in land cover are considered since most of the ancillary layers (e.g. pasture, forest management, climate, etc...) are static and thus not all changes in habitats can be found. We therefore recommend end users to continue using the 2015 dataset unless specific habitat updates to habitat are needed.</p> <p><strong>Citation:</strong></p> <p>Please cite the published paper and state the used version of the habitat map</p> <p>Jung, M., Dahal, P.R., Butchart, S.H.M., Donald, P.F., De Lamo, X., Lesiv, M., Kapos, V., Rondinini, C., Visconti, P., (2020). A global map of terrestrial habitat types. Sci. Data 7, 256. <a href="https://doi.org/10.1038/s41597-020-00599-8">https://doi.org/10.1038/s41597-020-00599-8</a></p>
Three-dimensional super-Yang--Mills theory on the lattice and dual black branes --- data release
<p>This HDF5 file collects data and analysis results for non-perturbative lattice field theory calculations investigating three-dimensional maximally supersymmetric SU(N) Yang--Mills theory on a skewed euclidean torus, and its holographic connection to dual D2-brane solutions in supergravity. See the README for further information.</p>
A Comprehensive Study of the Microclimate-Induced Conservation Risks in Hypogeal Sites: The Mithraeum of the Baths of Caracalla (Rome)
<p>The peculiar microclimate inside cultural hypogeal sites needs to be carefully investigated. This study presents a methodology that aimed at providing a user-friendly assessment of the frequently occurring hazards in such sites. A Risk Index was specifically defined as the percentage of time for which the hygrothermal values lie in ranges that are considered to be hazardous for conservation. An environmental monitoring campaign that was conducted over the past ten years inside the Mithraeum of the Baths of Caracalla (Rome) allowed for us to study the deterioration before and after a maintenance intervention. The general microclimate assessment and the specific conservation risk assessment were both carried out. The former made it possible to investigate the influence of the outdoor weather conditions on the indoor climate and estimate condensation and evaporation responsible for salts crystallisation/dissolution and bio-colonisation. The latter took hygrothermal conditions that were close to wall surfaces to analyse the data distribution on diagrams with critical curves of deliquescence salts, mould germination, and growth. The intervention mitigated the risk of efflorescence thanks to reduced evaporation, while promoting the risk of bioproliferation due to increased condensation. The Risk Index provided a quantitative measure of the individual risks and their synergism towards a more comprehensive understanding of the microclimate-induced risks.</p>
Database of Annotated Core Arguments: English, Lao and Russian
<p>This database contains corpus examples of transitive clauses with annotated core arguments realized as syntactic subjects and objects (A and P) in English, Lao and Russian. The coding scheme was developed together with Alena Witzlack-Makarevich</p>
HOSENG trial – HOme-based oral SElf-testiNG for absent and refusing individuals during a door-to-door HIV testing campaign: a cluster randomised clinical trial in rural Lesotho
<p>These are pseudo-anonymised data from the HOSENG randomized trial: " HOSENG trial – HOme-based oral SElf-testiNG for absent and refusing individuals during a door-to-door HIV testing campaign: a cluster randomised clinical trial in rural Lesotho". The data dictionary explains the data available in the dataset. Between July 2018 and December 2018, 10516 eligible individuals from 106 consenting villages were enrolled from two districts of Lesotho, and followed up for a maximum of 120 days. Main manuscript reference, DOI: <a href="https://doi.org/10.1016/s2352-3018(20)30233-2">10.1016/S2352-3018(20)30233-2. </a>The protocol was published, DOI:10.1186/s13063-019-3469-2.</p>
Herbarium occurences of Diospyros crassiflora Hiern
<p><strong>Supplementary Material S4 from Deblauwe V (2020) Life history, uses, trade and management of <em>Diospyros crassiflora</em> Hiern, the ebony tree of the Central African forests: A state of knowledge.<em> Forest Ecology and Management.</em></strong></p> <p>This table lists all the dried specimens of <em>Diospyros crassiflora</em> Hiern we found in the following herbaria: BR, BRLU, LBV, MA, MO, FHI, K, L, P, U, WAG and YA (acronyms following the Index Herbariorum, Thiers, continuously updated) and in the Global Plants database (http://plants.jstor.org). In total, 212 dried collections were found, some of which are duplicates in two or more herbaria.</p> <p>For each collection, the table shows the barcode(s), herbaria where samples are kept, species name, collector name, collector number, collection day, collection month, collection year, country, locality, habitat, additional description and geographic coordinates in decimal degrees. When not specified on the herbarium label, the geographical coordinates of the locality found on the herbarium samples were searched for on 1/200,000 IGN maps for Cameroon and www.openstreetmap.org for other countries. Apart from this, all fields in the table are provided as found on the label. Blank cells represent missing data.</p> <p>The species identification of each collection was verified using Letouzey and White (1970) taxonomic key. Collections that we found to be erroneously identified as <em>D. crassiflora</em> were not included here and the herbarium informed.</p> <p>Data are provided as a comma delimited UTF-8 CSV file.</p> <p>REFERENECES</p> <p>Letouzey, R., White, F., 1970. Flore du Cameroun: Ebénacées, Ericacées. Muséum national d'histoire naturelle, Paris.</p> <p>Thiers, B., continuously updated. Index Herbariorum: A global directory of public herbaria and associated staff. New York Botanical Garden's Virtual Herbarium. http://sweetgum.nybg.org/science/ih/</p>
Seawater salinity sample measurements from the Antarctic Circumnavigation Expedition (ACE)
<p><strong>Dataset abstract</strong></p> <p>This data set contains salinity measurements from discrete seawater samples that were collected in the Southern Ocean (south of 30deg S) during the Antarctic Circumnavigation Expedition (ACE). 657 samples were collected during the period December 24th, 2016 and March 18th, 2017 in the Southern Ocean from the surface ocean using the ship's underway line (UW; 328 samples) and in vertical profiles using Niskin bottles mounted on the CTD rosette (273 samples). A few additional samples (56) were collected from a parallel cast with a trace-metal rosette, with a bucket, and as duplicates to ensure data quality. All samples were analyzed for their salinity and results are reported on the Practical Salinity Scale 1978 (PSS-78; Sea-Bird Electronics, Inc., 1989). Measurements were performed on a Guildline Autosal Laboratory Salinometer 8400(B) at CSIRO (Hobart, Australia) for samples collected during leg 1, and on a OPTIMARE Precision Salinometer (OPS) at the Alfred Wegener Institute (Bremerhaven, Germany) for samples collected during legs 2 and 3. This circumpolar data set provides insights into the hydrological cycle of the Southern Ocean and the processes (precipitation, evaporation, sea-ice melting and freezing, ice-berg and land-ice melting) that determine the salinity of a certain water mass. It is being used to calibrate the CTD sensor vertical profiles (Henry et al., 2020) and thermosalinograph sensor underway measurements (Haumann et al., 2020) from the ACE cruise.</p> <p><strong>Dataset contents</strong></p> <p>Processed Data</p> <ul> <li>ace_18_data_salinity_ctd_20200812.csv, text format; contains salinity measurements of seawater samples collected from the Niskin bottles mounted on the CTD rosette</li> <li>ace_18_data_salinity_uw_20200812.csv, text format; contains salinity measurements of seawater samples collected from the underway line</li> <li>ace_18_data_salinity_other_20200812.csv, text format; contains salinity measurements of miscellaneous samples: Duplicate seawater samples; seawater bucket sample from Cumberland Bay, South Georgia; seawater samples from Niskin bottles mounted on the trace-metal rosette.</li> </ul> <p>Metadata</p> <ul> <li>data_file_header.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>figure*.pdf, metadata, portable document format</li> </ul> <p><strong>Dataset license</strong></p> <p>This physical and biogeochemical oceanography dataset is made available under the Creative Commons Attribution 4.0 License (CC BY 4.0) whose full text can be found at <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p>
Particle concentration data from: Long-term measurement of sub-3nm particles and their precursor gases in the boreal forest
<p>The knowledge of the dynamics of sub-3nm particles in the atmosphere is crucial for our understanding of first steps of atmospheric new particle formation. Therefore, accurate and stable long-term measurements of the smallest atmospheric particles are needed. In this study, we analyzed over five years of particle concentrations in size classes 1.1–1.7 nm and 1.7–2.5 nm obtained with the Particle Size Magnifier (PSM) and three years of precursor vapor concentrations measured with the Chemical Ionization Atmospheric Pressure Interface Time-of-Flight mass spectrometer (CI-APi-ToF) at the SMEAR II station in Hyytiälä, Finland. The results show that the 1.1–1.7 nm particle concentrations have a daytime maximum during all seasons, which is due to increased photochemical activity. There are significant seasonal differences in median concentrations of 1.7–2.5 nm particles, underlining the different frequency of new particle formation between seasons. Aerosol precursor vapors have notable diurnal and seasonal differences as well. Sulfuric acid and highly oxygenated organic molecule (HOM) monomer concentrations have clear daytime maxima, while HOM dimers have their maxima during the night. HOM concentrations for both monomers and dimers are the highest during summer and the lowest during winter. Higher median concentrations during summer result from increased biogenic activity in the surrounding forest. Sulfuric acid concentrations are the highest during spring and summer, with autumn and winter concentrations being two to three times lower. A correlation analysis between the sub-3nm concentrations and aerosol precursor vapor concentrations indicates that HOMs, particularly their dimers, and sulfuric acid play a significant role in new particle formation in the boreal forest. Our analysis also suggests that there might be seasonal differences in new particle formation pathways that need to be investigated further. </p> <p> </p>
Plant richness of the terrestrial ecoregions of the world with a mean aridity index lower than 0.65
<p>Data used to compose the <strong>Figure 1</strong> and the <strong>Table S1</strong> of the paper <strong>Biogeography of Global Drylands</strong>, by Maestre <em>et al</em>. (2021).</p>
Dataset to Manuscript: Vertical mobility of pyrogenic organic matter in soils: A column experiment, Marcus Schiedung et al. (Biogeosciences)
<p>Dataset to manuscript: Schiedung, M., Bellè, S.-L., Sigmund, G., Kalbitz, K., and Abiven, S.: Vertical mobility of pyrogenic organic matter in soils: A column experiment, Biogeosciences, https://doi.org/10.5194/bg-17-6457-2020, 2020.</p> <p>All parameters and variables are described in "Var_names" files.</p>
PsPM-EWO: Eye tracker (including pupillometry) measurements from emotional-words tasks
<p>This dataset includes eye tracker (including pupillometry) measurements for 37 healthy unmedicated participants (25 females and 12 males, age range: 18 - 34 years, mean age: 26.2 +/- 4.7 years) participating in an emotional-words task. In each session, participants were presented with 50 neutral and 50 negative five-letter nouns from the Berlin Affective Word List Reloaded (Võ et al., 2009). Also included are task information, keypress responses, keypress response times.</p>
PsPM-RRM1-2: SCR, ECG, respiration and eye tracker measurements in response to electric stimulation or visual targets
<p>This dataset includes skin conductance response (SCR), electrocardiogram (ECG), respiration and eye tracker (including pupillometry) measurements for each of 29 healthy unmedicated participants (7 males and 22 females aged 23.5 +/- 3.6 years) in response to 10 discomforting electric stimulations to the forearm (RRM1) or 10 visual targets in a visual detection task (RRM2). The sample partly overlaps with data set <a href="https://doi.org/10.5281/zenodo.1292568">PsPM-FR</a>. Some participants did not take part in RRM1 or RRM2 such that there are 25 recordings for RRM1 and 26 recordings for RRM2. Electric shock stimuli are 0.2 ms wide square current pulse repeated at 500 Hz for 500 ms and individually adjusted amplitude just below the pain threshold. Visual stimuli are red crosses (+) embedded in a white digit stream; each stimulus is presented during 200 ms and separated by a 800 ms blank interval. ITI is selected randomly on each trial from 40 s, 45 s or 50 s. A baseline period with distractors but no targets concludes experiment RRM2. (This is in contrast to the methods description in Bach et al. (2016), according to which the baseline period was randomly either in the beginning or at the end of the experiment. This discrepancy was caused by an error in the code that controlled the experiment presentation.)</p>
Silica Nanoparticles Enhance Disease Resistance in Arabidopsis Plants - RAW DATA
<p>These datasets are used to produce the figures/graphs published in our article</p> <p><strong>Silica Nanoparticles Enhance Disease Resistance in <em>Arabidopsis</em> Plants</strong></p> <p>in <em>Nat. Nanotechnol.</em> (2020). <a href="https://doi.org/10.1038/s41565-020-00812-0">https://doi.org/10.1038/s41565-020-00812-0</a></p> <p> </p><p><strong>Correspondence: </strong></p> <p></p> <p>fabienne.schwab@alumni.ethz.ch, Tel: +41 78 736 00 19;</p> <p>m.shetehy@uky.edu, Tel. +41 76 455 56 02</p> <p>Further raw data related to qPCR and microbiology are available upon reasonable request from M.H. El‑Shetehy.</p> <p>Further raw data related to the nanoparticles and plant microscopy are available upon reasonable request by F. Schwab.</p> <p> </p> <p><strong>Abstract</strong></p> <p>In plants, pathogen attack can induce an immune response known as systemic acquired resistance (SAR) that protects against a broad spectrum of pathogens. In the search for safer agrochemicals, silica nanoparticles (SiO<sub>2</sub>‑NPs, food additive E551) have recently been proposed as a new tool. However, initial results are controversial, and the molecular mechanisms of SiO<sub>2</sub>‑NP-induced disease resistance are unknown. Here, we show that SiO<sub>2</sub>‑NPs, as well as soluble orthosilicic acid (Si(OH)<sub>4</sub>), can induce SAR in a dose-dependent manner, that involves the defence hormone salicylic acid. Nanoparticle uptake and action occurred exclusively through stomata (leaf pores facilitating gas exchange) and involved extracellular adsorption in leaf air spaces of the spongy mesophyll. In contrast to treatment with SiO<sub>2</sub>‑NPs, induction of SAR by Si(OH)<sub>4 </sub>was problematic, since high concentrations caused stress. We conclude that SiO<sub>2</sub>‑NPs have the potential to serve as an inexpensive, highly efficient, safe, and sustainable alternative for plant disease protection.</p>
Smartphone sensor data (accelerometer, virtual keyboard) collected in-the-wild by Parkinson's Disease patients and Healthy Controls
<p>For detailed description of the dataset see the relevant <a href="https://www.nature.com/articles/s41598-020-78418-8">journal article</a>.</p> <p>Python code for model inference and training is available <a href="https://github.com/alpapado/deep_pd">here</a>.</p> <p> </p> <p><strong>DESCRIPTION</strong></p> <p>The dataset contains accelerometer recodings and keyboard typing data contributed by Parkinson's Disease patients and Healthy Controls. Accelerometer data consists of acceleration values recorded during phone calls and typing data consist of virtual keyboard press and release timestamps. The dataset is divided into two parts: the first part, called SData, contains data from a small, medically evaluated, set of users, while the second part, called GData, contains recordings from a large body of users with self-reported PD labels.</p> <p>The dataset is organized into 5 pickle files:</p> <p>1. <strong>imu_sdata.pickle</strong>: Contains the tri-axial accelerometer recordings for the SData part of the dataset in the form of a list of python dictionaries, one for each participating subject. Accelerometer data have been pre-processed to a sampling frequency of 100Hz and come segmented into non-overlapping 5 second windows. Hence, a segment's dimension will be 500 x 3 samples.</p> <p>Sample Python code for accessing the acceleration data of a subject</p> <pre><code class="language-python">sdata = pickle.load(open('imu_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] # subject_data is a list of length 4 ## The actual data is in the last element of the list acc_segments = subject_data[-1] num_acc_sessions_for_subject = len(acc_segments) acc_segments_for_first_session = acc_segments[0] acc_segments_for_second_session = acc_segments[1] # ..etc In: print(acc_segments_for_first_session.shape) Out: (3, 500, 3) ## The first accelerometer session for this subject consists of 3 five-second segments. In: print(acc_segments_for_second_session.shape) Out: (8, 500, 3) ## The second accelerometer session for this subject consists of 8 five-second segments.</code></pre> <p>2. <strong>imu_gdata.pickle</strong>: Same layout as imu_sdata.pickle but with data ffrom GData subjects.</p> <p>3. <strong>typing_sdata.pickle</strong>: This files contains the typing data originating from the SData part of the dataset. It is a list of dictionaries with one entry per subject. The typing data are given in the form of concatenated hold time (the time elapsed between press and release of the virtual key) and flight time (the time between releasing a key and press the next) histograms, computed over 10ms bins in the range of [0, 1]s for hold time and [0, 4]s for flight time (an additional bin that contains the values in the (1, +oo) and (4, +oo) intervals is also used). So, the total length of the concatenated histogram is 1000/10 + 1 + 4000/10 + 1 = 502.</p> <p>Sample Python code for accessing the typing data of a subject:</p> <pre><code class="language-python">sdata = pickle.load(open('typing_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] ## The actual data is in the first element of the list typing_histograms = subject_data[0] num_typing_sessions_for_subject = len(typing_histograms) typing_hist_for_first_session = typing_histograms[0] typing_hist_for_second_session = typing_histograms[1] # ..etc In: print(typing_hist_for_first_session.shape) Out: (502, ) ht_hist = typing_hist_for_first_session[:101] # Hold time histogram of the session ft_hist = typing_hist_for_first_session[101:] # Flight time histogram of the session</code></pre> <p>4. <strong>typing_gdata.pickle</strong>: Same layout as typing_sdata.pickle but with data from GData subjects.</p> <p>5. <strong>subject_metadata.pickle</strong>: A list of dictionaries with one entry per subject containing demographic information. The relevant demographic fields have the following interpretation:<br> 'age': Year of birth,<br> 'gender_id': 0 indicates male, 1 indicates female<br> 'healthstatus_id': 0 indicates PD patient, 1 indicates Healthy with PD family history, 2 indicates Healthy without PD family history</p> <p>In the case of SData subjects, there is also symptom UPDRS scores from one or two medical examinations. These are ncoded in the fields med_eval_1 and med_eval_2.</p> <p> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected is a multi-center study approved in each country available (for more info visit: <a href="http://www.i-prognosis.eu/?page_id=3606">http://www.i-prognosis.eu/?page_id=3606</a>). Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu/">i-prognosis.eu</a>).</p> <p> </p> <p><strong>CORRESPONDANCE</strong></p> <p>Any inquiries regarding this dataset should be adressed to:</p> <p>Mr. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate)</p> <p>Multimedia Understanding Groupmug<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996359, 996365 <br> Fax: +30 2310 996398<br> E-mail: alpapado@mug.ee.auth.gr</p> <p> </p> <p><br> </p> <p> </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.