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6,467 results for “process”
Tables for Finding Driver Mutations in Cancer: Elucidating the Role of Background Mutational Processes
<p>nucleotide and codon tables used for plots and figures and analysis, have all mutations in 520 cancer genes. </p> <p><strong>Table S2. </strong>Correlation between mutability and recurrence of mutations in cancer-associated genes.</p> <p><strong>Table S3. </strong>List of cancer-associated genes and GO terms.</p>
Visual stimuli used in fMRI experiment on processing of real and illusory surfaces
<p>These videos contain samples of visual stimuli used in an fMRI experiment on the processing of real and illusory surfaces in human early visual cortex [the experiments are part of my PhD thesis, in preparation]. Please note that these videos are short sample segments from the experiment, and that in the actual experiment the duration of rest blocks was much longer.</p>
Analysis of suprathermal nuclear processes in the solar core plasma
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2017JPhG...44d5202V/abstract">Voronchev et al. 2017</a>. Mesa version 4050.</p> <p>Publication DOI: <a href="https://doi.org/10.1088/1361-6471/aa5f30">10.1088/1361-6471/aa5f30</a></p>
Consistent Modeling of GS 1826-24 X-Ray Bursts for Multiple Accretion Rates Demonstrates the Possibility of Constraining rp-process Reaction Rates
<p>MESA inlists and run_star_extras associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018arXiv180505552M/abstract">Meisel (2018)</a>. MESA version 9793.</p> <p>Publication DOI: <a href="https://doi.org/10.3847/1538-4357/aac3d3">10.3847/1538-4357/aac3d3</a></p>
Dataset on production, processing and trading activities in Ghana's cocoa value chain
<p>This covers data on production, processing and trading activities in Ghana's cocoa value chain that were used to populate a stock-and flow diagram representing the baseline model of the cocoa value chain. Data are retrieved from different secondary sources, collated in excel sheets</p>
Processed input data for vampire-analysis-1
<p>Input files for the analysis and plotting code for the paper "Deep generative models for T cell receptor protein sequences." </p>
The Decision-Making Process Behind Library Adoption
<p>This is the Factors/Process Book for the research paper "The Decision-Making Process Behind Library Adoption", submitted to: The 35th IEEE International Conference on Software Maintenance and Evolution (ICSME).</p>
Initial leader properties during the preliminary breakdown processes of lightning flashes and their associations with initiation positions
<p>It is the dataset assocaited with the figures in the paper titled "Initial leader properties during the preliminary breakdown processes of lightning flashes and their associations with initiation positions" which was submitted to the Journal of Geophysical Research: Atmospheres for publication. It can be accessed using Matlab. The abstract of the manuscript is as follows.</p> <p>The properties of initial leaders (ILs) of 1056 flashes in two thunderstorms in Guangzhou, China, are analyzed. The median values of IL properties are 11.1 ms for duration, 2.2 km for vertical distance, 2.7 km for 3-D distance, 1.9 × 10<sup>5</sup> m s<sup>−1</sup> for vertical speed, 2.4 × 10<sup>5</sup> m s<sup>−1</sup> for 3-D speed, 28° for angle between the IL’s displacement direction and the vertical direction, 1.1 per ms for pulse rate, 179 m for vertical step length and 224 m for 3-D step length. All the IL properties follow lognormal distributions. Strong monotonic relationships of IL duration with speed and pulse rate and pulse rate with speed and step length are revealed. With increasing initiation altitude, IL duration and step length tend to increase, and IL speed and pulse rate tend to decrease. The ILs initiated at approximately the −20 ℃ isotherm are more vertically orientated on average than those initiated at lower or higher levels. The ILs initiated in the strong kinematic areas generally had a greater speed and pulse rate but smaller duration, distance and step length relative to those initiated in the weak kinematic areas. The possible impacts of the charge distribution patterns and density on the IL properties in different types of thunderstorms or thunderstorm areas are discussed.</p>
Raw and processed anomalous diffraction data for crystals of metal-free R2lox soaked with Mn, Fe and Co or Zn
<p>Raw and processed anomalous diffraction data for crystals of metal-free <em>Geobacillus kaustophilus</em> R2-like ligand-binding oxidase (R2lox) soaked with 5 mM each MnCl<sub>2</sub>, (NH<sub>4</sub>)<sub>2</sub>Fe(SO<sub>4</sub>)<sub>2</sub> and CoCl<sub>2</sub> or ZnCl<sub>2</sub>. Data were collected on two crystals each after soaking with Mn, Fe and Co or Mn, Fe and Zn, respectively. For each crystal, one dataset each was collected at the Fe K edge (7162 eV), Mn K edge (6589 eV) and Co K edge (7721 eV) or Zn K edge (9664 eV; X-ray energies corresponding to the theoretical K edge + 50 eV). Data were collected at 100 K on a Pilatus 6M detector at beamline X06SA of the Swiss Light Source (SLS, Villigen, Switzerland) on September 23, 2012, and processed with XDS and XSCALE. All raw and processed diffraction data for each crystal are compressed into one file. </p>
Multivariate mixed model application to mass cytometry data (processed data)
<p>This bachelor thesis demonstrates the results of mass cytometry data re-analysis using multivariate regression. I reanalyse a dataset by Palgen et al. (2019) using two models: a Poisson log-normal mixed model and a logistic linear mixed model from the R package ‘cytoeffect’ (Seiler et al., 2019). By exposing multivariate patterns and the associated uncertainty profiles in the data, the aim of this analysis is to replicate biological conclusions and uncover new biological findings. </p>
Termite abundance and ecosystem processes in Maliau Basin, 2015-2016 [HMTF]
<p><strong>Description: </strong></p> <p>This dataset consists of invertebrate abundance data and associated ecosystem measurements (Including leaf litter depth and mass, seedlings, soil moisture and nutrients, and rainfall) measured within an area of lowland, old growth dipterocarp rainforest in the Maliau Basin Conservation Area, Sabah, Malaysia between 2015 and 2016. Data were collected during a collaborative project which was included in the NERC Human-modified tropical forest (HMTF) programme.</p> <p><strong>Project: </strong>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/54"><strong>Biodiversity and land-use impacts on tropical ecosystem function (BALI): Experimental manipulations of biodiversity at SAFE</strong></a></p> <p><strong>Funding: </strong>These data were collected as part of research funded by:</p> <ul> <li>UK NERC-funded Biodiversity And Land-use Impacts on Tropical Ecosystem Function (BALI) consortium (Standard grant, NERC grant NE/L000016/1)</li> </ul> <p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p> <p> </p> <p><strong>Permits: </strong>These data were collected under permit from the following authorities:</p> <ul> <li>Sabah Biodiversity Centre (Research licence na)</li> </ul> <p> </p> <p><strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3265746">here</a></p> <p><strong>Files: </strong>This consists of 1 file: Termite_monitoring_maliau.xlsx</p> <p><strong>Termite_monitoring_maliau.xlsx</strong></p> <p>This file contains dataset metadata and 11 data tables:</p> <ol> <li> <p><strong>Leaf_litter_depth</strong> (described in worksheet Leaf_litter_depth)</p> <p>Description: summary of leaf litter measurements collected on experimental plots. An in situ assay of ecosystem-level decomposition was carried out by measuring leaf litter depth during the drought (March 2016) and non-drought (October 2016) periods. Forty leaf litter depth measurements were taken in total per plot in March 2016, with 10 measurements spaced every 3 m across four 30 m transect lines, with each transect being separated by 10 m. In October 2016, a total of sixty measurements were taken per plot, similarly spaced out across a total of six 30 m transect lines</p> <p>Number of fields: 5</p> <p>Number of data rows: 800</p> <p>Fields:</p> <ul> <li><strong>Date</strong>: The month and year in which leaf litter depth was recorded (Field type: Date)</li> <li><strong>Plot</strong>: The experimental plot that the data were collected from (Field type: Location)</li> <li><strong>LINE</strong>: The sampling line within each plot a leaf litter measurement was taken (Field type: ID)</li> <li><strong>Depth_cm</strong>: The depth of leaf litter measured at each point (Field type: Numeric)</li> <li><strong>Treatment</strong>: The experimental treatment that was applied to the plot (Field type: Categorical)</li> </ul> </li> <li> <p><strong>Leaf_litter_invertebrates</strong> (described in worksheet Leaf_litter_invertebrates)</p> <p>Description: In 2016 (two years after initial poisoning), fifteen 1 m2 leaf litter samples were collected from each plot. These were collected every 7m along a 100m transect. Sieved litter samples were suspended in Winkler bags for three days to extract invertebrates. All leaf litter invertebrates were identified to order and counted.</p> <p>Number of fields: 31</p> <p>Number of data rows: 120</p> <p>Fields:</p> <ul> <li><strong>Plot</strong>: The experimental plot that the data were collected from (Field type: Location)</li> <li><strong>Treatment</strong>: The experimental treatment that was applied to the plot (Field type: Categorical)</li> <li><strong>Distance</strong>: Distance along the sampling transect in metres (Field type: ID)</li> <li><strong>Coleoptera_Adults</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Coleoptera_Larvae</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Diptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Hemiptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Araneae</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Opiliones</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Isopoda</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Oligochaeta</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Hymenoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Formicidae</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Mollusca</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Lepidoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Chilipoda</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Diplopoda</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Thysanoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Psocoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Dermaptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Orthoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Blattodea</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Leeches</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Plecoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Neuoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Trichoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Mecoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Odonata</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Siphonaptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Termites</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Pseudoscorpions</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> </ul> </li> <li> <p><strong>Leaf_litter_mass</strong> (described in worksheet Leaf_litter_mass)</p> <p>Description: Decomposition rate was assessed using leaf litter decomposition bags. We collected freshly abscised Shorea johorensis leaf litter from trees close to our experimental plots for use in the leaf litter decomposition bags. The leaf litter was dried at 60 degrees Celsius until it reached a constant weight. We used 300-micron nylon mesh to produce macroinvertebrate exclusion bags, the closed-bag treatment, and created an open-bag treatment by cutting 10, 1 cm holes in each side of the 300-micron mesh bags to allow access to the material by termites and other macroinvertebrates. This approach avoided any unintentional bias due to the use of different mesh size. Each leaf litter bag contained on average 10.5 g ± 0.6 g of dried Shorea johorensis. We left litter bags on the forest floor for 112 days before collection. Bags were placed on plots at the beginning of the 2015 drought (August 2015) and again during the non-drought period (July 2016).</p> <p>Number of fields: 6</p> <p>Number of data rows: 87</p> <p>Fields:</p> <ul> <li><strong>Condition</strong>: The rainfall season in which leaf litter bags were deployed (Field type: Categorical)</li> <li><strong>Plot</strong>: The experimental plot that the data were collected from (Field type: Location)</li> <li><strong>Bag_treatment</strong>: The treatment applied to each leaf litter bag - open = accessible to invertebrates, closed = inaccessible to invertebrates (Field type: Categorical)</li> <li><strong>Plot_treatment</strong>: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)</li> <li><strong>Mass_loss</strong>: total leaf litter mass loss from each bag in grams (Field type: Numeric)</li> <li><strong>Proportion</strong>: the proportion of leaf litter mass loss from each bag (Field type: Numeric)</li> </ul> </li> <li> <p><strong>Non_target_inverts</strong> (described in worksheet Non_target_inverts)</p> <p>Description: non-termites were collected in 2014 (pre-drought and pre-suppression), 2015 (during the drought and the suppression) and 2016 (post-drought). We collected 1m2, leaf litter samples, sieved the leaf litter and extracted invertebrates with Winkler bags for three days.</p> <p>Number of fields: 5</p> <p>Number of data rows: 5040</p> <p>Fields:</p> <ul> <li><strong>Year</strong>: the year in which sampling occured (Field type: ID)</li> <li><strong>Treatment</strong>: The experimental treatment that was applied to the plot (Field type: Categorical)</li> <li><strong>variable</strong>: the order of non-target invertebrates samples (Field type: Taxa)</li> <li><strong>value</strong>: the number of individuals belonging to each order (Field type: Numeric)</li> <li><strong>log</strong>: the log of the number of individuals belonging to each order (Field type: Numeric)</li> </ul> </li> <li> <p><strong>Seedling_survival_non_drought</strong> (described in worksheet Seedling_survival_non_drought)</p> <p>Description: Seedling mortality was assessed using a seedling transplant experiment. In July 2015, 200 individuals of a leguminous liana, Agelaea borneensis, were collected from the forest matrix surrounding our plots. Seedlings were selected from seedling mats resulting from a masting event in 2014. We selected individuals that had only their cotyledons and had not yet developed their first true leaves, and were roughly the same height. We are therefore confident that individuals were all of the same age and developmental stage and that we minimised confounding influences of genetic variability by using individuals from the same conspecific seedling mat. Seedlings were planted in the ground in July 2015 in the same grid of 25 used to assess soil moisture (n = 25 per plot), which was located within the central 50 m sampling area of experimental plots. Each seedling was separated by at least 5 m from the next closest seedling. To minimise the effect of stochastic disturbance-induced mortality as a result of transplantation shock, we used the number of individuals alive one month after the initial transplant as the baseline abundance. Survival of seedlings during the drought was assessed 11 months after transplantation, in June 2016. Following this assessment, the number of live individuals in June 2016 was used as a new baseline abundance. Survival during non-drought conditions was assessed 12 months later in June 2017</p> <p>Number of fields: 4</p> <p>Number of data rows: 64</p> <p>Fields:</p> <ul> <li><strong>plot</strong>: The experimental plot that the data were collected from (Field type: Location)</li> <li><strong>alive_2016</strong>: whether or not each seedling was alive at the time of inspection - 1 = alive, 0 = dead (Field type: Numeric)</li> <li><strong>Treatment</strong>: The experimental treatment that was applied to the plot (Field type: Categorical)</li> <li><strong>alive.2017</strong>: whether or not each seedling was alive at the time of inspection - 1 = alive, 0 = dead (Field type: Numeric)</li> </ul> </li> <li> <p><strong>Seedling_survival_drought</strong> (described in worksheet Seedling_survival_drought)</p> <p>Description: Tree seedlings survival during the drought</p> <p>Number of fields: 3</p> <p>Number of data rows: 274</p> <p>Fields:</p> <ul> <li><strong>Plot</strong>: The experimental plot that the data were collected from (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)</li> <li><strong>alive_2016</strong>: whether or not each seedling was alive at the time of inspection - 1 = alive, 0 = dead (Field type: Numeric)</li> <li><strong>Treatment</strong>: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)</li> </ul> </li> <li> <p><strong>Soil_moisture</strong> (described in worksheet Soil_moisture)</p> <p>Description: Soil moisture was measured using a Delta-T Devices HH2 moisture metre in March and October 2016. Soil moisture was recorded at 25 points, spread evenly across each plot in a grid, with each sampling point separated by 5 m from the next point.</p> <p>Number of fields: 5</p> <p>Number of data rows: 400</p> <p>Fields:</p> <ul> <li><strong>plot</strong>: The experimental plot that the data were collected from (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)</li> <li><strong>treatment</strong>: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)</li> <li><strong>soil_moisture</strong>: the % soil moisture measured at each point (Field type: Numeric)</li> <li><strong>condition</strong>: The rainfall season in which soil moisture measurements were taken (Field type: Categorical)</li> <li><strong>date</strong>: the month and year in which the soil moisture recording was taken (Field type: Date)</li> </ul> </li> <li> <p><strong>Soil_nutrients</strong> (described in worksheet Soil_nutrients)</p> <p>Description: We used Plant Root Simulator (PRS®) resin probes to assess mineralization rates of plant available soil nutrients (NO3-, NH4+, P, K, Ca, Mg, Mn, Al, Fe, Zn) over a two-week period, during drought and non-drought conditions. In March 2016, we buried two anion and cation probe pairs at a random subsample of 12 points within the 25 sampling grid used to measure soil moisture. In October 2016, four probe pairs were placed at each point of the complete 25 sampling grid. We buried the probe membranes to a depth of 10 cm and left them in situ for two weeks, after which they were removed from the soil, cleaned with de-ionized water and subsequently analysed by Western Ag Innovations, Saskatoon, Canada.</p> <p>Number of fields: 15</p> <p>Number of data rows: 292</p> <p>Fields:</p> <ul> <li><strong>plot</strong>: The experimental plot that the data were collected from (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)</li> <li><strong>Treatment</strong>: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)</li> <li><strong>Condition</strong>: the season in which the soil nutrient sampling occurred – drought (2015) or non-drought (2016) (Field type: Categorical)</li> <li><strong>NO3_N_micro_grams/10cm2/burial length</strong>: Soil NO3 at 10cm2 profile (Field type: Numeric)</li> <li><strong>NH4_N_micro_grams/10cm2/burial length</strong>: Soil NH4 at 10cm2 profile (Field type: Numeric)</li> <li><strong>Ca_micro_grams/10cm2/burial length</strong>: Soil Ca at 10cm2 profile (Field type: Numeric)</li> <li><strong>Mg_micro_grams/10cm2/burial length</strong>: Soil Mg at 10cm2 profile (Field type: Numeric)</li> <li><strong>K_micro_grams/10cm2/burial length</strong>: Soil K at 10cm2 profile (Field type: Numeric)</li> <li><strong>P_micro_grams/10cm2/burial length</strong>: Soil P at 10cm2 profile (Field type: Numeric)</li> <li><strong>Fe_micro_grams/10cm2/burial length</strong>: Soil Fe at 10cm2 profile (Field type: Numeric)</li> <li><strong>Mn_micro_grams/10cm2/burial length</strong>: Soil Mn at 10cm2 profile (Field type: Numeric)</li> <li><strong>Cu_micro_grams/10cm2/burial length</strong>: Soil Cu at 10cm2 profile (Field type: Numeric)</li> <li><strong>Zn_micro_grams/10cm2/burial length</strong>: Soil Zn at 10cm2 profile (Field type: Numeric)</li> <li><strong>B_micro_grams/10cm2/burial length</strong>: Soil B at 10cm2 profile (Field type: Numeric)</li> <li><strong>Al_micro_grams/10cm2/burial length</strong>: Soil Al at 10cm2 profile (Field type: Numeric)</li> </ul> </li> <li> <p><strong>Termite_cumulative_attacks</strong> (described in worksheet Termite_cummulative_attack)</p> <p>Description: We monitored termite feeding activity on the plots using untreated TPRs. Sixteen untreated TPRs were placed on each plot and were scored for termite attack on a 0 to 5 scale, where 0 is untouched and 5 is completely eaten. After one month, TPR were scored and replaced. Before they were replaced, we recorded the cumulative amount of TPR consumed on each plot and calculated the plot-level cumulative mean attack scores.</p> <p>Number of fields: 4</p> <p>Number of data rows: 120</p> <p>Fields:</p> <ul> <li><strong>month</strong>: The month in which termite attack scores were recorded (Field type: ID)</li> <li><strong>cumulative_consumption</strong>: The cumulative consumption rate for each plot (Field type: Numeric)</li> <li><strong>plot</strong>: The experimental plot that the data were collected from (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)</li> <li><strong>treatment</strong>: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)</li> </ul> </li> <li> <p><strong>Termite_C_plot_SPI</strong> (described in worksheet Termite_C_plots_SPI)</p> <p>Description: To assess the relationship between rainfall and termite abundance, we carried out termite transects on control plots every 2 months from March 2016 to December 2016 and also at the beginning and the end of the experimental period in June 2015 and June 2017. Daily total rainfall was collected from Danum Valley forest reserve (4°57′53″ to 55″ N and 117°48′14″ to 30″E) from November 2010 to March 2017. Daily values were used to calculate total monthly rainfall in the region, and this was used to calculate 3-monthly Standardised Precipitation Index (SPI)[2] in the 'SPI' package in R. The SPI is a climatic proxy used to quantify and monitor drought; negative values indicate drier than average conditions, while positive values represent wetter than average conditions.</p> <p>Number of fields: 5</p> <p>Number of data rows: 32</p> <p>Fields:</p> <ul> <li><strong>Plot</strong>: the control plot on which samples were collected (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)</li> <li><strong>total</strong>: total number of termite hits recorded (Field type: Numeric)</li> <li><strong>date</strong>: the month in which sampling occurred (Field type: Date)</li> <li><strong>SPI</strong>: the standardized precipitation index number calculated for each time period from rainfall data collected at Danum Valley Field Station (Field type: Numeric)</li> <li><strong>Wet.dry</strong>: the rainfall conditions at the time of sampling (wet = 2017, dry = 2015) (Field type: Categorical)</li> </ul> </li> <li> <p><strong>Termite_hits_on_T_and_C_plot</strong> (described in worksheet Termite_hits_on_T_and_C_plots)</p> <p>Description: termite abundance data. In order to quantify the effect of the suppression treatment on termite community composition, we sampled termites on suppression and control plots in June 2015 and October 2016 using the Jones and Eggleton transect method</p> <p>Number of fields: 7</p> <p>Number of data rows: 192</p> <p>Fields:</p> <ul> <li><strong>Plot</strong>: The experimental plot that the data were collected from (Field type: Location)</li> <li><strong>Treatment</strong>: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)</li> <li><strong>date</strong>: The month and year in which the sampling occurred (Field type: Date)</li> <li><strong>genus</strong>: the genus to which each termite encounter belongs (Field type: Taxa)</li> <li><strong>hits</strong>: number of termite of hits on each plot (Field type: Numeric)</li> <li><strong>SPI</strong>: the standardized precipitation index at the time of each sampling occasion (Field type: Numeric)</li> <li><strong>Wet.dry</strong>: the season in which sampling occurred – wet = 2016, dry = 2015 (Field type: Categorical)</li> </ul> </li> </ol> <p><strong>Date range: </strong>2014-10-01 to 2017-07-30</p> <p><strong>Latitudinal extent: </strong>4.5000 to 5.0700</p> <p><strong>Longitudinal extent: </strong>116.7500 to 117.8200</p> <p><strong>Taxonomic coverage: </strong><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p> <p>Animalia<br>  - Annelida<br>  -  - Clitellata<br>  -  -  - [Oligochaeta]<br>  - Arthropoda<br>  -  - Arachnida<br>  -  -  - Araneae<br>  -  -  - Opiliones<br>  -  -  - Pseudoscorpiones<br>  -  - Chilopoda<br>  -  - Diplopoda<br>  -  - Insecta<br>  -  -  - Blattodea<br>  -  -  -  - [Termites]<br>  -  -  - Coleoptera<br>  -  -  - Dermaptera<br>  -  -  - Diptera<br>  -  -  - Hemiptera<br>  -  -  - Hymenoptera<br>  -  -  -  - Formicidae<br>  -  -  - Isoptera<br>  -  -  -  -  - <em>Procapritermes</em><br>  -  -  -  -  - <em>Prohamitermes</em><br>  -  -  -  - Rhinotermitidae<br>  -  -  -  -  - <em>Heterotermes</em><br>  -  -  -  -  - <em>Parrhinotermes</em><br>  -  -  -  -  - <em>Schedorhinotermes</em><br>  -  -  -  - Termitidae<br>  -  -  -  -  - <em>Bulbitermes</em><br>  -  -  -  -  - <em>Dicuspiditermes</em><br>  -  -  -  -  - <em>Globitermes</em><br>  -  -  -  -  - <em>Macrotermes</em><br>  -  -  -  -  - <em>Malaysiotermes</em><br>  -  -  -  -  - <em>Microcerotermes</em><br>  -  -  -  -  - <em>Odontotermes</em><br>  -  -  - Lepidoptera<br>  -  -  - Mecoptera<br>  -  -  - Neuroptera<br>  -  -  - Odonata<br>  -  -  - Orthoptera<br>  -  -  - Plecoptera<br>  -  -  - Psocodea<br>  -  -  - Siphonaptera<br>  -  -  - Thysanoptera<br>  -  -  - Trichoptera<br>  -  - Malacostraca<br>  -  -  - Isopoda<br>  - Mollusca</p> <p> </p>
Data supporting "Non-hydrostatic, non-linear processes in the surf zone", by Martins et al., submitted to JGR-Oceans
<pre>This file describes the structure of the sub-surface pressure and surface elevation data used in the paper "Non-hydrostatic, non-linear processes in the surf zone", submitted by Martins et al. to Journal of Geophysical Research: Oceans. The data set is composed of a single .mat file, which contains all raw timeseries for the 52 bursts used in the paper. Metadata and description of the data structure and variables are provided in the structure directly. This data set is distributed under the Creative Commons Attribution 4.0 International license. </pre> <p>The collection of this data set was funded by the Engineering and Physical Sciences Research Council (EPSRC) grant EP/N019237/1, Waves in Shallow Water, awarded to Chris Blenkinsopp (University of Bath).</p>
Modeling Data vs Modeling Processes: How Digital Humanities developed in Russia
<p>2nd lecture</p>
The Decision-Making Process Behind Software Library Adoption
<p>Dataset supporting journal publication "The Decision-Making Process Behind Software Library Adoption".</p>
Saccades Exert Spatial Control of Motion Processing for Smooth Pursuit Eye Movements
<p>Saccades modulate the relationship between visual motion and smooth eye movement. Before a saccade, pursuit eye movements reflect a vector average of motion across the visual field. After a saccade, pursuit primarily reflects the motion of the target closest to the endpoint of the saccade. We tested the hypothesis that the saccade produces a spatial weighting of motion around the endpoint of the saccade. Using a moving pursuit stimulus that stepped to a new spatial location just before a targeting saccade, we controlled the distance between the endpoint of the saccade and the position of the moving target. We demonstrate that the smooth eye velocity following the targeting saccade weights the presaccadic visual motion inputs by the distance from their location in space to the endpoint of the saccade, defining the extent of a spatiotemporal filter for driving the eyes. The center of the filter is located at the endpoint of the saccade in space, not at the position of the fovea. The filter is stable in the face of a distracter target, is present for saccades to stationary and moving targets, and affects both the speed and direction of the postsaccadic eye movement. The spatial filter can explain the target-selecting gain change in postsaccadic pursuit, and has intriguing parallels to the process by which perceptual decisions about a restricted region of space are enhanced by attention. The effect of the spatial saccade plan on the pursuit response to a given retinal motion describes the dynamics of a coordinate transformation.</p>
ENGLISH Participation in a new group and the identification processes: The quest for a positive social identity
<p>Dataset for manuscript Participation in a new group and the identification processes: The quest for a positive social identity</p>
Partis post-processed B cell receptor repertoires from BioProject PRJNA349143
<p>These files correspond to partis annotations of several datasets found in BioProject PRJNA349143. (DOI: 10.5281/zenodo.821659).</p>
Biological Process Summary (shared genes)
<p><strong>Compiled data for Gene Ontology analysis done using TOPPFUN for significant genes from pairwise Prakriti comparisons (PK,VK,VP)</strong></p>
Comparing the Use of Research Resource Identifiers and Natural Language Processing for Citation of Databases, Software and Other Digital Artifacts
<p><strong>The Research Resource Identifier was introduced in biomedicine in 2014 to more precisely identify the reagents and tools used in published biomedical research and to track use of tools across the breadth of the biomedical literature. The current RRID specification covers key biological and digital resources. Authors are instructed to include an RRID after the first mention of any resource used. RRIDs are designed to be easy to find using a full text search search engine. </strong></p> <p><strong>The published data sets were used in our comparative study where comparing the output of our RRID curation workflow with the outputs of automated text mining systems that have been used to identify mentions of resources in the text of publications. All files in tab-separated format (tsv). </strong></p> <p><strong>Scibot.tsv: Records of the RRID curation workflow using SciBot. </strong></p> <p>Each record shows that a resource RRID was identified in paper PMID with curator tags (Tag1, Tag2, both optional)</p> <p><strong> </strong>PMID: Pubmed ID</p> <p> RRID: Research Resource Identifier</p> <p> Tag1: Curator tags (optional)</p> <p> Tag2: Additional curator tags (optional)</p> <p><strong>rdwsorted.tsv: Records of the output from RDW, a text mining software. </strong></p> <p>RDW identifies mentions of research resources in papers. Each record shows that a resource RRID was identified in paper PMID.</p> <p><strong> </strong>PMID: Pubmed ID</p> <p> RRID: Research Resource Identifier</p> <p><strong>rridbyrdw05282019.tsv: Records of the output of the RRID-by-RDW in RDW. </strong></p> <p>RRID-by-RDW is a component in RDW that identifies mentions of research resources in papers by matching patterns of RRID specifications. Each record shows that a resource RRID was identified in paper PMID.</p> <p><strong> </strong>PMID: Pubmed ID</p> <p> RRID: Research Resource Identifier</p> <p> Context: Snippet where the RRID was found</p> <p><strong>resource_metadata20190418.tsv: Metadata of RRIDs</strong></p> <p>This file contains metadata of resources and their RRIDs. See file header for column definitions.</p> <p><strong>RRIDCUR-definitions.tsv: Definitions of curator tags used in Scibot.tsv.</strong></p> <p><strong> </strong>tag: Tag name</p> <p> definition: Definition of the tag</p>
Data from "Comprehensive observations of the wire destruction and plasma channel reestablishment process during the initial stage of triggered lightning"
<p>The attached is the dataset assoicated with the paper titled "Comprehensive observations of the wire destruction and plasma channel reestablishment process during the initial stage of triggered lightning" which was submitted to <em>Geophysical Research Letters</em> . The data can be used freely for scientific purposes with appropriate citation.</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.