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North Atlantic jet stream clusters: daily and seasonal occurence
<p>This dataset contains the time series used in Madonna et al 2020 (Reconstructing winter climate anomalies in the Euro-Atlantic sector using circulation patterns, DOI: 10.5194/wcd-2021-6)</p> <p><br> Filenames:</p> <p>1) seasonal_timeseries.txt</p> <p>Time series of the occurrence (in % = days/season*100) of time during winter of each jet cluster, blocking and the NAO.<br> Winters are defined as December, January and February (DJF). The season name is given by the last month (i.e. 1980 is December 1979, January 1980 and February 1980). 29 February is removed from the data so that each winter season has 90 days.</p> <p>Jet clusters are calculated following Madonna et al 2017. The five clusters are named as in Madonna et al 2017: Northern (N), Central (C), Mixed (M), Southern (S) and Tilted (T).<br> Blocking are calculated following Scherrer et al. 2006 and averaged over Greenland (GB), offshore of the Iberian Peninsula also called Iberian wave breaking (IWB) and over Scandinavia (SBL). The exact definition of the regions can be found in Madonna et al 2020.</p> <p>The NAO index was downloaded from ftp://ftp.cpc.ncep.noaa.gov/cwlinks/norm.daily.nao.index.b500101.current.ascii. Positive (NAO+) and negative (NAO-) days are defined as those that exceed 0.5 DJF standard deviation, corresponding to values greater than 0.613 and lower than -0.177, respectively.</p> <p>Example: during winter 1980, 7.78% of the days were in the North jet cluster. This is equivalent to 7 days -> 7.78 * 90 (days per season) /100</p> <p><br> 2) daily_inverse_distance_from_centroid.txt contains information about the similarity of the 2D zonal wind field to the cluster centroids which is used to determine the jet state.</p> <p>The file has 12 columns, labelled as follow:<br> date, lat, speed, N4, C4, M4, S4, N5, C5, M5, S5, T5</p> <p>The first column (date) shows the day in YYYYMMDD format, the second (lat) is the latitude (in °N) of the maximum zonal wind in the 60°W-0°W sector (i.e. the jet latitude index, see Woollings et al. 2010 or Madonna et al. 2017 for more details), and the third (speed) is the zonal averaged (60°W-0°) zonal wind speed (in m/s) at the latitude given by column 2.</p> <p>Columns 4-7 give the inverse distance from each cluster centroids using four (4) clusters: Northern (N4), Central (C4), Mixed (M4), Southern (S4) and is normalized from 0 to 1. Values close to 1 means that the clusters are similar to its centroid. The distances sum up to 1.</p> <p>Columns 8-12 show similar to 4-7 the inverse distance from the centroids using five (5) clusters: Northern (N5), Central (C5), Mixed (M5), Southern (S5) and Tilted (T5). Distances are also normalized and sum up to 1.</p> <p>In the study of Madonna et al 2020, a day has a defined cluster X (X=N, C, M, S, T), if the inverse distance from the cluster centroid X exceeds 0.5 and it clearly dominates over the other clusters.</p> <p><br> Example: 1 January 1979, the zonal mean zonal wind is maximum at 47°N and has a value of 15.61 m/s.<br> Considering 4 clusters, the jet resembles most the Mixed cluster (M4=0.36), followed by the Southern (S4=0.23), Northern (N4=0.22) and Central (C4=0.18). The sum of the distances (0.36 + 0.23 + 0.22 + 0.18 = 0.99 due to decimal approximation) is equal to 1. Using 4 clusters, this day would be assigned to cluster M4. The day is, however, not clearly identified as a Mixed jet, as the inverse distance (M4=0.36) is smaller than 0.5. The threshold of 0.5 is set to identify days where a centroid clearly leads over the others.<br> If we consider 5 clusters, the jet on 1 Jan 1979 resembles the tilted jet (T5 = 0.73) and has very little in common with the other centroids (values of 0.05-0.08). Thus, considering 5 clusters, this day is classified as a tilted jet. It is also clearly defined, as 0.73 > 0.5.</p> <p><br> References:</p> <p>Madonna, E., Li, C., Grams, C.M. and Woollings, T. (2017), The link between eddy‐driven jet variability and weather regimes in the North Atlantic‐European sector. Q.J.R. Meteorol. Soc, 143: 2960-2972. https://doi.org/10.1002/qj.3155</p> <p>Scherrer, S. C., Croci‐Maspoli, M., Schwierz, C., and Appenzeller, C. (2006). Two‐dimensional indices of atmospheric blocking and their statistical relationship with winter climate patterns in the Euro‐Atlantic region. International Journal of Climatology, 26(2), 233-249</p> <p>Woollings T, Hannachi A and Hoskins B. (2010). Variability of the North Atlantic eddy‐driven jet stream. Q. J. R. Meteorol. Soc. 136: 856– 868.</p> <p> </p> <p> </p> <p> </p>
The Nature and Orbit of the Ophiuchus Stream
<p>The *_chain.txt files contain Markov chains that model the line-of-sight<br /> velocity (RV_chains.txt), color-magnitude diagram (CMD_chains.txt), and<br /> proper motion and the extent of the Ophiuchus stellar stream (PM_chains.txt). By randomly selecting rows from these files, one can sample the corresponding probability density functions.The columns in each file are briefly described below.</p> <p>RV_chains.txt:</p> <p> column 1: line-of-sight velocity at {ell}_0=5deg,<br /> column 2: gradient in line-of-sight velocity, d(v_los)/d({ell})<br /> column 3: additional scatter in line-of-sight velocities, s</p> <p>CMD_chains.txt:<br /> column 1: age, t<br /> column 2: mass-loss parameter, eta<br /> column 3: metallicity content, Z<br /> column 4: offset in reddening with respect to the Schlegel et al. (1998)<br /> [1998ApJ...500..525S] reddening, (E(B-V)_off)<br /> column 5: distance modulus at {ell}_0=5deg,<br /> column 6: gradient distance modulus, d(DM)/d({ell})<br /> columns 7-11: uncertainty in isochrone magnitudes, {sigma}_iso_m,<br /> where m=[g,r,i,z,y,]</p> <p>PM_chains.txt:<br /> column 1: fraction of stars associated with the field population, 1-f<br /> column 2: natural logarithm of the width of the stream in the<br /> galactic latitude direction, ln({sigma}_b)<br /> column 3: natural logarithm of the width of the field population in the<br /> galactic latitude direction, ln({sigma}_p_b)<br /> column 4: A_p<br /> column 5: B_p<br /> column 6: ln({sigma}_pm)<br /> column 7: ln({sigma}_p_pm)<br /> column 8: <{mu}_{ell}><br /> column 9: d({mu}_{ell})/d({ell})<br /> column 10: <{mu}_b><br /> column 11: d({mu}_b)/d({ell})<br /> column 12: <{mu}_p_{ell}><br /> column 13: d({mu}_p_{ell})/d({ell})<br /> column 14: <{mu}_p_b><br /> column 15: d({mu}_p_b)/d({ell})<br /> column 16: {ell}_min<br /> column 17: {ell}_max<br /> column 18: A<br /> column 19: B<br /> column 20: C</p> <p> </p>
Griffith Creek stream temperature and stream discharge
<p>This repository contains data sets and scripts used in the analysis for the following article:</p><p>Moore RD, Guenther SM, Gomi T, Leach JA. Headwater stream temperature response to forest harvesting: Do lower flows cause greater warming? <i>Hydrological Processes</i>, doi 10.10002/hyp.15025 </p><p>The research was supported by funds from the Natural Sciences and Engineering Research Council of Canada and Forest Renewal British Columbia.</p><p> </p><p><strong>File descriptions</strong></p><p><a href="https://zenodo.org/api/records/10071956/draft/files/griff_mike_tmmm.csv/content"><i>griff_mike_tmmm.csv</i></a></p><p>Time series of daily minimum, mean and maximum stream temperatures for Mike Creek (control stream) and Griffith Creek (treatment stream). There are three monitoring sites on Griffith Creek: just above a weir (0 m), and 100 m and 200 m upstream of the weir. The variable names include the stream name (Mike or Griff), the summary value (Min, Mean or Max) and, for Griffith Creek, the location (0, 100, 200).</p><p><a href="https://zenodo.org/api/records/10071956/draft/files/q_dly_rdm.csv/content"><i>q_dly_rdm.csv</i></a></p><p>Time series of daily mean streamflow at a weir in L/s, as computed from stage and a rating curve. See Moore et al. (2023) for details of the rating curve derivation.</p><p><a href="https://zenodo.org/api/records/10071956/draft/files/mkrf_open_met_dly.csv/content"><i>mkrf_open_met_dly.csv</i></a></p><p>Time series of daily mean solar radiation (W/m^2), mean daily and maximum air temperature (degrees C), and total rainfall (mm) measured at an open site southeast of Griffith Creek.</p><p><a href="https://zenodo.org/api/records/10071956/draft/files/01_paired-catchment-analysis_tmax_20230929.r/content"><i>01_paired-catchment-analysis_tmax_20230929.r</i></a></p><p><a href="https://zenodo.org/api/records/10071956/draft/files/02_figures_tmax_20230929.r/content"><i>02_figures_tmax_20230929.r</i></a></p><p><a href="https://zenodo.org/api/records/10071956/draft/files/03_test_ushape_peakfinder_20230927.r/content"><i>03_test_ushape_peakfinder_20230927.r</i></a></p><p>Scripts used to conduct the analyses and generate figures and tabular output. The leading number indicates the order in which the scripts should be run. </p><p> </p><p> </p><p> </p>
HyG: A hydraulic geometry dataset derived from historical stream gage measurements across the conterminous United States
<p>Regional- and continental-scale models predicting variations in the magnitude and timing of streamflow are important tools for forecasting water availability as well as flood inundation extent and associated damages. Such models must define the geometry of stream channels through which flow is routed. These channel parameters, such as width, depth, and hydraulic resistance, exhibit substantial variability in natural systems. While hydraulic geometry relationships have been extensively studied in the United States, they remain unquantified for thousands of stream reaches across the country. Consequently, large-scale hydraulic models frequently take simplistic approaches to channel geometry parameterization. Over-simplification of channel geometries directly impacts the accuracy of streamflow estimates, with knock-on effects for water resource and hazard prediction.</p> <p>Here, we present a hydraulic geometry dataset derived from long-term measurements at U.S. Geological Survey (USGS) stream gages across the conterminous United States (CONUS). This dataset includes (a) at-a-station hydraulic geometry parameters following the methods of Leopold and Maddock (1953), (b) at-a-station Manning's n calculated from the Manning equation, (c) daily discharge percentiles, and (d) downstream hydraulic geometry regionalization parameters based on HUC4 (Hydrologic Unit Code 4). This dataset is referenced in Heldmyer et al. (2022); further details and implications for CONUS-scale hydrologic modeling are available in that article (https://doi.org/10.5194/hess-26-6121-2022). </p> <p><strong>At-a-station Hydraulic Geometry</strong></p> <p>We calculated hydraulic geometry parameters using historical USGS field measurements at individual station locations. Leopold and Maddock (1953) derived the following power law relationships:</p> <p>\(w={aQ^b}\)</p> <p>\(d=cQ^f\)</p> <p>\(v=kQ^m\)</p> <p>where Q is discharge, w is width, d is depth, v is velocity, and a, b, c, f, k, and m are at-a-station hydraulic geometry (AHG) parameters. We downloaded the complete record of USGS field measurements from the USGS NWIS portal (https://waterdata.usgs.gov/nwis/measurements). This raw dataset includes 4,051,682 individual measurements from a total of 66,841 stream gages within CONUS. Quantities of interest in AHG derivations are Q, w, d, and v. USGS field measurements do not include d--we therefore calculated d using d=A/w, where A is measured channel area. We applied the following quality control (QC) procedures in order to ensure the robustness of AHG parameters derived from the field data:</p> <ol> <li>We considered only measurements which reported Q, v, w and A.</li> <li>For each gage, we excluded measurements older than the most recent five years, so as to minimize the effects of long-term channel evolution on observed hydraulic geometry relationships.</li> <li>We excluded gages for which measured Q disagreed with the product of measured velocity and measured area by more than 5%. Gages for which \( Q\neq vA\) are often tidally influenced and therefore may not conform to expected channel geometry relationships.</li> <li>Q, v, w, and d from field measurements at each gage were log-transformed. We performed robust linear regressions on the relationships between log(Q) and log(w), log(v), and log(d). AHG parameters were derived from the regressed explanatory variables. <ol> <li>We applied an iterative outlier detection procedure to the linear regression residuals. Values of log-transformed w, v, and d residuals falling outside a three median absolute deviation (MAD) envelope were excluded. Regression coefficients were recalculated and the outlier detection procedure was reapplied until no new outliers were detected.</li> <li>Gages for which one or more regression had p-values >0.05 were excluded, as the relationships between log-transformed Q and w, v, or d lacked statistical significance.</li> <li>Gages were omitted if regressed AHG parameters did not fulfill two additional relationships derived by Leopold and Maddock: \(b+f+m=1{\displaystyle \pm }0.1\) and \(a{\displaystyle \times }c{\displaystyle \times }k=1{\displaystyle \pm }0.1\).</li> </ol> </li> <li>If the number of field measurements for a given gage was less than 10, either initially or after individual measurements were removed via steps 1-4, the gage was excluded from further analysis.</li> </ol> <p>Application of the QC procedures described above removed 55,328 stream gages, many of which were short-term campaign gages at which very few field measurements had been recorded. We derived AHG parameters for the remaining 11,513 gages which passed our QC.</p> <p><strong>At-a-station Manning's n</strong></p> <p>We calculated hydraulic resistance at each gage location by solving Manning's equation for Manning's n, given by</p> <p>\(n = {{R^{2/3}S^{1/2}} \over v}\)</p> <p>where v is velocity, R is hydraulic radius and S is longitudinal slope. We used smoothed reach-scale longitudinal slopes from the NHDPlusv2 (National Hydrography Dataset Plus, version 2) ElevSlope data product. We note that NHDPlusv2 contains a minimum slope constraint of 10<sup>-5</sup> m/m--no reach may have a slope less than this value. Furthermore, NHDPlusv2 lacks slope values for certain reaches. As such, we could not calculate Manning's n for every gage, and some Manning's n values we report may be inaccurate due to the NHDPlusv2 minimum slope constraint. We report two Manning's n values, both of which take stream depth as an approximation for R. The first takes the median stream depth and velocity measurements from the USGS's database of manual flow measurements for each gage. The second uses stream depth and velocity calculated for a 50th percentile discharge (Q<sub>50</sub>; see below). Approximating R as stream depth is an assumption which is generally considered valid if the width-to-depth ratio of the stream is greater than 10<span>—</span>which was the case for the vast majority of field measurements. Thus, we report two Manning's n values for each gage, which are each intended to approximately represent median flow conditions.</p> <p><strong>Daily discharge percentiles</strong></p> <p>We downloaded full daily discharge records from 16,947 USGS stream gages through the NWIS online portal. The data includes records from both operational and retired gages. Records for operational gages were truncated at the end of the 2018 water year (September 30, 2018) in order to avoid use of preliminary data. To ensure the robustness of daily discharge percentiles, we applied the following QC:</p> <ol> <li>For a given gage, we removed blocks of missing discharge values longer than 6 months. These long blocks of missing data generally correspond to intervals in which a gage was temporarily decommissioned for maintenance.</li> <li>A gage was omitted from further analysis if its discharge record was less than 10 years (3,652 days) long, and/or less than 90% complete (>10% missing values after removal of long blocks in step 1.</li> </ol> <p>We calculated discharge percentiles for each of the 10,871 gages which passed QC. Discharge percentiles were calculated at increments of 1% between Q<sub>1</sub> and Q<sub>5</sub>, increments of 5% (e.g. Q<sub>10</sub>, Q<sub>15</sub>, Q<sub>20</sub>, etc.) between Q<sub>5</sub> and Q<sub>95</sub>, increments of 1% between Q<sub>95</sub> and Q<sub>99</sub>, and increments of 0.1% between Q<sub>99</sub> and Q<sub>100</sub> in order to provide higher resolution at the lowest and highest flows, which occur much less frequently.</p> <p><strong>HG Regionalization</strong></p> <p>We regionalized AHG parameters from gage locations to all stream reaches in the conterminous United States. This downstream hydraulic geometry regionalization was performed using all gages with AHG parameters in each HUC4, as opposed to traditional downstream hydraulic geometry--which involves interpolation of parameters of interest to ungaged reaches on individual streams. We performed linear regressions on log-transformed drainage area and Q at a number of flow percentiles as follows:</p> <p>\(log(Q_i) = \beta_1log(DA) + \beta_0\)</p> <p>where Q<sub>i</sub> is streamflow at percentile i, DA is drainage area and \(\beta_1\) and \(\beta_0\) are regression parameters. We report \(\beta_1\), \(\beta_0\) , and the r<sup>2</sup> value of the regression relationship for Q percentiles Q<sub>10</sub>, Q<sub>25</sub>, Q<sub>50</sub>, Q<sub>75</sub>, Q<sub>90</sub>, Q<sub>95</sub>, Q<sub>99</sub>, and Q<sub>99.9</sub>. Further discussion and additional analysis of HG regionalization are presented in Heldmyer et al. (2022).</p> <p><strong>Dataset description</strong></p> <p>We present the HyG dataset in a comma-separated value (csv) format. Each row corresponds to a different USGS stream gage. Information in the dataset includes gage ID (column 1), gage location in latitude and longitude (columns 2-3), gage drainage area (from USGS; column 4), longitudinal slope of the gage's stream reach (from NHDPlusv2; column 5), AHG parameters derived from field measurements (columns 6-11), Manning's n calculated from median measured flow conditions (column 12), Manning's n calculated from Q50 (column 13), Q percentiles (columns 14-51), HG regionalization parameters and r<sup>2</sup> values (columns 52-75), and geospatial information for the HUC4 in which the gage is located (from USGS; columns 76-87). Users are advised to exercise caution when opening the dataset. Certain software, including Microsoft Excel and Python, may drop the leading zeros in USGS gage IDs and HUC4 IDs if these columns are not explicitly imported as strings.</p> <p> </p> <p><strong>Errata</strong></p> <p>In version 1, drainage area was mistakenly reported in cubic meters but labeled in cubic kilometers. This error has been corrected in version 2.</p>
A scoping review on bovine tuberculosis highlights the need for novel data streams and analytical approaches to curb zoonotic diseases
<p>The following data and scripts are part of the manuscript titled 'A scoping review on bovine tuberculosis highlights the need for novel data streams and analytical approaches to curb zoonotic diseases' which is currently going through the peer-review process and has already been published as a preprint. Please read the README.txt file for information on the files uploaded.</p>
Data from: Effects of dispersal and geomorphology on riparian seedbanks and vegetation in a boreal stream
<p>SiteData: information that describes 20 riparian zones along Svartån, a boreal free-flowing stream, indicated per LocationID (column A). Coordinates are given in SWEREF 99 TM (column B and C) and degrees of longitude and latitude (column D and E). RPD refers to River Process Domain and takes one of three forms: lake, rapid or slow-flowing. Side of stream indicates plot placement when looking towards downstream. Data collection is described in the paper linked to below. </p> <p> </p> <p>LitterData: information that describes species lists of litter seedbanks from 20 riparian sites. Litter samples were taken in an unstandardised manner at each location. LocationID refers to locations as described in file SiteData, RPD refers to River Process Domain and takes one of three forms: lake, rapid or slow-flowing.</p> <p> </p> <p>SeedData: information that describes the soil seedbank composition from 20 riparian sites. LocationID refers to locations as described in file SiteData, RPD refers to River Process Domain and takes one of three forms: lake, rapid or slow-flowing. Layer refers to samples that are taken from from layer 0-1 cm in the soil, 1-5 cm or from 5-10 cm deep. Data collection is described in the paper linked to below. </p> <p> </p> <p>VegetationData: information that describes vegetation composition from 20 riparian sites. LocationID refers to locations as described in file SiteData, RPD refers to River Process Domain and takes one of three forms: lake, rapid or slow-flowing. Abundance is indicated following the categories in Table 1. Data collection is described in the paper linked to below. </p> <p> </p> <p>Table 1. Vegetation cover classes.</p> <table> <tbody> <tr> <td> <p><strong>Code</strong></p> </td> <td> <p><strong>Cover (%)</strong></p> </td> </tr> <tr> <td> <p>0</p> </td> <td> <p>0</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p><1</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>1-3</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>3-5</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>5-15</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>15-25</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>25-50</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>50-75</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p>75-100</p> </td> </tr> </tbody> </table> <p> </p> <p>For more information, help or collaboration, please contact Jacqueline.Hoppenreijs@kau.se. If you use the data here in your work or research, please cite the publication appropriately.</p>
Code to reproduce the analyses of "Multiple stressors alter greenhouse gas concentrations in streams through local and distal processes"
<p>Streams are significant contributors of greenhouse gases (GHG) to the atmosphere, and the increasing number of stressors degrading freshwaters may exacerbate this process, posing a threat to climatic stability. However, it is unclear whether the influence of multiple stressors on GHG concentrations in streams results from increases of in-situ metabolism (i.e., local processes) or from changes in upstream and terrestrial GHG production (i.e., distal processes). Here, we hypothesize that the mechanisms controlling multiple stressor effects vary between <span>carbon dioxide (</span>CO<sub>2</sub>) and <span>methane (</span>CH<sub>4</sub>), with the latter being more influenced by changes in local stream metabolism, and the former mainly responding to distal processes. To test this hypothesis, we measured stream metabolism and the concentrations of CO<sub>2</sub> (<em>p</em>CO<sub>2</sub>) and CH<sub>4</sub> (<em>p</em>CH<sub>4</sub>) in 50 stream sites that encompass gradients of <span>nutrient enrichment, oxygen depletion, thermal stress, riparian degradation and discharge</span>. Our results indicate that these stressors had additive effects on stream metabolism and GHG concentrations, with stressor interactions explaining limited variance. Nutrient enrichment was associated with higher stream heterotrophy and <em>p</em>CO<sub>2</sub>, whereas <em>p</em>CH<sub>4</sub> increased with oxygen depletion and water temperature. Discharge was positively linked to primary production, respiration and heterotrophy but correlated negatively with <em>p</em>CO<sub>2.</sub> Our models indicate that CO<sub>2</sub>-equivalent concentrations can more than double in streams that experience high nutrient enrichment and oxygen depletion, as compared to those with oligotrophic and oxic conditions. Structural equation models revealed that the effects of nutrient enrichment and discharge on <em>p</em>CO<sub>2</sub> were related to distal processes rather than local metabolism. In contrast, <em>p</em>CH<sub>4</sub> responses to nutrient enrichment, discharge and temperature were related to both local metabolism and distal processes. Collectively, our study illustrates <span>potential climatic feedbacks resulting from freshwater degradation and </span>provides insight into the processes mediating stressor impacts on the production of GHG in streams.</p>
Streamflow, precipitation, soil moisture, and ephemeral stream nitrogen data for St. Croix, USVI
<p>These datasets were collected from two ephemeral stream sites within the Salt River watershed on St. Croix, USVI using high frequency (15-minute) sensors. The stream nitrogen data were collected via grab samples and were analyzed with a benchtop spectrophotometer. The data were collected to better understand the influence of precipitation and soil moisture conditions on stream nitrogen concentrations. </p>
STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: Multi-Perspective Sensing - Automotive Environment
<p>This dataset contains the files corresponding to which results have been included in the journal paper. The full description of the conducted trials and data structure is mentioned in the attached PDF document.</p> <p>The trials were conducted at the University of Birmingham using distributed radar sensors installed on the mobile laboratory. The data will be used to develop algorithms to extract the information needed for high-resolution multi-modal and multi-perspective sensing.</p> <p>The experiments were performed with automotive radars operating in the 79 GHz band to investigate the Doppler and imaging capabilities of these radars.</p> <p>This report describes the measurement scenarios and data structure of INRAS Radarlog (76 GHz – 81 GHz) used for the data collection campaign.</p> <p>Contact: a.a.a.pirkani@bham.ac.uk, anum.apirkani@gmail.com, or m.s.gashinova@bham.ac.uk</p>
Distinguishing between high entropy bit streams
<p>This dataset contains the curated files, classified by type and extension so that other researchers can compute their features and replicate outcomes. </p> <p> </p> <p>A total of 5 datasets (i.e., one according to each file size denoted as 64, 128, 256, 512, and 1024) each one consisting of exactly 50% encrypted and 50% compressed files.</p> <p>-The encryption algorithms used to generate the files were:</p> <p>AES(128 / 192 / 256) and Camelia(128 / 192 / 256</p> <p> </p> <p>-In the case of compressed files:</p> <p>ZIP RAR BZIP2 GZIP</p> <p> </p> <p>-Different source files were considered to generate the encrypted and compressed files. </p> <p>COCO Dataset (http://cocodataset.org/home) <br> Microsoft Research (https://www.microsoft.com/en-us/research/project/rgb-d-dataset-7-scenes/)<br> ArXiv (https://arxiv.org/)<br> Project Gutenberg (https://www.gutenberg.org/)<br> Several classical music symphonies in MP3 format <br> YouTube-8M dataset<br> Binaries extracted from system32 in Win10 x64 and sbin from Ubuntu 16.04</p> <p> </p> <p> </p> <p> </p> <p> </p>
Data and script: Catchment scale deforestation increases the uniqueness of subtropical stream communities
<p>These are datasets on benthic diatom and insect communities sampled in 100 streams along a gradient of land use intensification, ranging from streams in pristine forests to agricultural catchments in southeast subtropical Brazil. Data also include information on instream and land-use variables.</p> <p>In addition to the datasets, we also provide the R codes used to investigate whether compositional uniqueness (LCBD) and species contribution to beta diversity (SCBD) of stream diatoms and insects can be predicted by instream and land-use characteristics and by species traits and taxonomic relatedness, respectively. </p>
HYDRO-CSI, Project 1.2: In-stream hydrology. Part 1: Groundwater measurements
<p>The continuous exchange of water between surface water and groundwater is a key environmental process controlling the transport and the fate of nutrients, solutes and pollutants in river networks. The dynamics of the near-stream groundwater has a non-negligeable role on controlling flow direction and solutes exchange between the stream water with the adjacent groundwater, however it is rarely considered in solute transport experiments. Despite the amount of individual studies, we are still uncertain about how the physical processes controlling in-stream solutes transport change with different hydrologic conditions and how these processes can be inferred by modelling outcomes.</p> <p>In this project we investigated groundwater and stream interactions in order to characterize the physical processes that control the water and solute exchange in the river corridor and their variability over time. To do so, we drilled 36 wells in the near-stream domain, and 7 piezometers in the stream channel. We observed the water level and electrical conductivity every 15 min at 22 of the 36 wells with a water level sensor (Orpheus Mini, OTT, Kempten, Germany, resolution of 1 mm and accuracy of ±0.05% FS) over a period of 32 months (July 2018 - March 2021).</p> <p>The dataset includes the following files:</p> <p>> "Raw groundwater measurements.xlsx" <br> This file includes groundwater table elevation measured as depth from the upper limit of the well (time step of 15 minutes, Orpheus Mini, OTT, Kempten, Germany, resolution of 1 mm and accuracy of ±0.05% FS). Every excel file includes also Electrical Conductivity measurements (μS/cm) and Voltage (V) of the instruments. Every sheet in this .xlsx file reports measurement for one sensor in the specific observation-well where it was placed.</p> <p>> "Groundwater elevation data - wells metadata and fixed groundwater table elevation.xlsx"<br> This file includes the raw groundwater table measurements measured via the OTT, information on the well network, elevation and location of the observation wells, location of subsurface layers, and suggested correction of the groundwater table measurements for short periods with missing data.</p> <p>> "Hand-measurements and metadata.xlsx"<br> This file includes the list of in-situ inspections and hand-measurements of the groundwater table conducted over the entire observation period in every well and piezometer of the groundwater-monitoring well network. Every sheet includes details on the instruments measuring the groundwater table, their offset with hand-measured data, information on their re-calibration, and calculation of the groundwater elevation above the reference plane after each hand-measurement.</p>
773782 COASTAL EU Project: stream data from SW Messinia, Greece
<p>In the framework of COASTAL Project (<a href="https://h2020-coastal.eu/">https://h2020-coastal.eu/</a>) , the Institute of Oceanography/HCMR coordinates the Multi-Actor Lab for the SW Messinia case study. The water quality of six small rivers was studied and evaluated to assess the environmental status of area. This kind of evaluation follows the European Water Framework Directive 2000/60/EU, which includes biotic and environmental characteristics, with emphasis on the Biological Quality Elements.</p> <p>Sampling and measurements were collected in seven periods, including macro-invertebrate fauna, physicochemical parameters and diatoms. The first one took place in October 2018, the second one in December 2018, the third in April 2019, the fourth in August 2019, the fifth in November - December 2019, the sixth in December 2020 and the last one in December 2021.</p>
773782 COASTAL EU Project: stream data from SW Messinia, Greece
<p>In the framework of COASTAL Project (<a href="https://h2020-coastal.eu/">https://h2020-coastal.eu/</a>) , the Institute of Oceanography/HCMR coordinates the Multi-Actor Lab for the SW Messinia case study. The water quality of six small rivers was studied and evaluated to assess the environmental status of area. This kind of evaluation follows the European Water Framework Directive 2000/60/EU, which includes biotic and environmental characteristics, with emphasis on the Biological Quality Elements.</p> <p>Sampling and measurements were collected in seven periods, including macro-invertebrate fauna, physicochemical parameters and diatoms. The first one took place in October 2018, the second one in December 2018, the third in April 2019, the fourth in August 2019, the fifth in November - December 2019, the sixth in December 2020 and the last one in December 2021.</p>
Data and script: Reduced enumeration effort, but not coarse taxonomic resolution, is sufficient to represent beta diversity patterns of stream benthic diatoms
<p>This is a dataset on benthic diatom communities sampled in 90 riffles (the local communities) within nine near-pristine subtropical streams (each stream represented a metacommunity) in southeast subtropical Brazil. </p> <p>In addition to the dataset, we also provide the R code used to investigate whether reduced enumeration efforts (i.e., subsets of counted valves per sample) and the identification to the genus level are sufficient to recover patterns in the species composition and in beta diversity of benthic diatom metacommunities.</p>
Nonlinear spectral analysis of ion acoustic solitons arising from a streaming charged object using the numerical inverse scattering transform data
<p>Data files used in the publication: "Nonlinear spectral analysis of ion acoustic solitons arising from a streaming charged object using the numerical inverse scattering transform", submitted to Physics of Plasma August 2022. To be used in conjunction with analysis software KVIST.</p> <p>KVIST can be found at:</p> <ul> <li>https://doi.org/10.5281/zenodo.7017043</li> <li>https://github.com/Planetary-Surfaces-and-Spacecraft-Lab/KVIST</li> </ul> <p>Data files generated with:</p> <p>Truitt, A. (2020). Simulation of Forced Korteweg De Vries Equation as Applied to Small Orbital Debris. Digital Repository at the University of Maryland. https://doi.org/10.13016/FOR0-XJYD</p>
Data from: Development of Single Nucleotide Polymorphism (SNP) Panel for determination of environmental influence on genome for wild Columbia River redband trout (Oncorhynchus mykiss gairdnerii) in Southwest Idaho streams
<p>DNA were derived from fin tissue samples taken from individual trout captured from Little Jacks Creek, Big Jacks Creek , and Duncan Creek of the Owyhee mountains and Keithly Creek and Upper Mann Creek in the Hitt mountains of Western Idaho, United States. Fin tissues were collected from individual trout from each stream during monthly sampling events in June through October 2020. </p> <p><em>DNA Extraction:</em> Extraction of DNA from caudal fin tissues were performed using Quick-DNA Miniprep Plus purification kits (Zymo Research Inc.©). Small sections of fin tissue (≤ 25 mg) were collected from each sample. This was mixed with a digesting solution comprised of ultra-pure water, solid tissue buffer (Zymo Research Inc.©) and proteinase K. All tissues were digested in sealed microcentrifuge tubes for at minimum 3 h at 55°C in a water bath. We then aliquoted 100 µL of digestion supernatant and combined with 200 µL of genomic binding buffer (Zymo Research Inc.©). DNA was eluted in 50, 75, and 100 µL of elution buffer to determine which volume provided sufficient DNA concentration for genotyping. After it was determined all quantities produced suitable concentrations, going forward, 50 µL of elution buffer used.</p> <p><em>Genotyping:</em> Following extraction, genotyping-in-thousands sequencing took place at the Hagerman National Fish Hatchery’s genetics research facility with the assistance of the Columbia River Intertribal Fish Commission (CRTFC). Genotyping protocols were as described in Campbell et al. (2015) and summarized below. First, samples were prepared for amplification via PCR by combining DNA extracts with a Qiagen Plus multiplex master mix and a species-specific pooled primer mix. This step added the Illumina sequencing primer sites to amplicons. Following the creation of the PCR cocktail, thermocycling was conducted for amplification. Amplified samples were then diluted 20-fold. Diluted samples were transferred to new 96-well PCR plates where two genetic indexes and barcodes provides a unique set of tagging primers to each well and plate. Tagged plates then underwent a second PCR step. After the second PCR, all DNA were transferred to Charm Biotech normalization plates where DNA was bound to wells, washed, and finally eluted. After normalization, all DNA was pooled together and a purification step using magnetized beads in two steps to selectively remove fragments of DNA that are both too large and too small for sequencing. Following purification, each plate was quantified via qPCR using Life Technologies QuantStudio 6 Flex Instrument (Life Technologies). Finally, sequencing was performed using an Illumina HiSeq 1500 instrument.</p> <p><strong>Ancillary peer-reviewed manuscripts:</strong><br> <em>Genotyping protocols</em><br> Campbell NR, Harmon SA, Narum SR. 2015. Genotyping-in-Thousands by sequencing (GT-seq): A cost effective SNP genotyping method based on custom amplicon sequencing. Mol Ecol Resour, 15: 855-867. https://doi.org/10.1111/1755-0998.12357<br> <em>SNP loci reference</em><br> Collins EE, Hargrove JS, Delomas TA, Narum SR. 2020. Distribution of genetic variation underlying adult migration timing in steelhead of the Columbia River basin. Ecology and Evolution, 10(17): 9486-9502. https://doi.org/10.1002/ece3.6641 </p> <p><strong>Data Use</strong>:<br> <em>License</em>: <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a> <br> <em>Recommended Citation</em>: Wooding AP, Narum SR, Pradhan DS. 2022. Data from: Development of Single Nucleotide Polymorphism (SNP) Panel for determination of environmental influence on genome for wild Columbia River redband trout (Oncorhynchus mykiss gairdnerii) in Southwest Idaho streams (0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7055582</p> <p>Funding for this project is provided by US National Science Foundation and Idaho EPSCoR through award: OIA-1757324 </p>
The role of place cues in voluntary stream segregation for cochlear implant listeners
<p>Data generated for the study "The role of place cues in voluntary stream segregation for cochlear implant listeners" - <a href="https://doi.org/10.1177/2331216517750262">https://doi.org/10.1177/2331216517750262</a></p> <p>The files "Experiment_1.txt" and "Experiment_2.txt" contain the data from the first and second experiments, respectively.</p> <p>List of variables:</p> <ul> <li>Subject: Listener's ID</li> <li>Electrode: Stimulation electrode for the distractor stream. The target stream was always presented on electrode 11.</li> <li>Rate: Stimulation pulse rate.</li> <li>ABpairs: Number of AB duplets in the sequence.</li> <li>Hrate: Hit rate</li> <li>FArate: False alarm rate</li> <li>dprime: d' score</li> <li>d_se: Standard error of the d' score</li> <li>IOmodel: 1 for ideal observer model estimates and 0 for listener's d' scores</li> </ul>
nextGEMS cycle3 datasets: statistical summaries for streamed data from climate simulations
<p>This Zenodo holds the datasets used in the paper "Statistical summaries for streamed data from climate simulations" by Katherine Grayson. All the data comes from the nextGEMS cycle 3 and has been regridded for plotting purposes with resolution given in the title of each data set. The wind speed data set has been made by taking the square root of the squared and summed v10 and u10 components respectively. All data has been retrieved and regridded through the AQUA reader on the Levante supercomputer, developed as part of the Destination Earth initative. The source code to create all the figures using this data can be found in https://github.com/kat-grayson/one_pass_algorithms_paper/tree/main </p>
Environmental and techno-economic assessment on the valorization of vine-side streams to produce resveratrol
<p>Tables included in the article "Environmental and techno-economic assessment on the valorization of vine-side streams to produce resveratrol"</p>
ScienceDex guides
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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.