Find research datasets worth reusing

opencc-by-4.0Mar 2022View details →

The SDSS DR16 / CatWISE2020 Spectro-Photometric Sample

SDSS DR16 sources with clean (ZWARNING == 0) spectra are matched with CatWISE2020 sources and sources meeting the following criteria dered_r > 12 && dered_r < 22 && dered_u != -9999.0 && dered_g != -9999.0 && dered_r != -9999.0 && dered_i != -9999.0 && dered_z != -9999.0 && w1mpro != 9999. && w2mpro != 9999. && w1snr > 5 && w2snr > 5 && blend_ext_flags == 1 have been retained. SDSS magnitudes are in the SDSS AB system, where CatWISE2020 magnitudes have been converted from the CatWISE2020 Vega system to the AB system. The "small" and "large" versions contain the very same sample (i.e. rows) but a more limited and more extensive set of properties/features (i.e. columns) respectively.

DeMeGRaS_samples_data_2022-04

DeMeGRaS_samples_data_2022-04 Layouts, fabrication steps, optical- and SEM pictures

opencc-zeroApr 2022View details →

dryad40/100

Data from: Skyline fossilized birth-death model is robust to violations of sampling assumptions in total-evidence dating

Several total-evidence dating studies under the fossilized birth-death (FBD) model have produced very old age estimates, which are not supported by the fossil record. This phenomenon has been termed "deep root attraction (DRA)". For two specific datasets, involving divergence time estimation for the early radiations of ants, bees and wasps (Hymenoptera) and of placental mammals (Eutheria), it has been shown that the DRA effect can be greatly reduced by accommodating the fact that extant species in these trees have been sampled to maximize diversity, so called diversified sampling. Unfortunately, current methods to accommodate diversified sampling only consider the extreme case where it is possible to identify a cut-off time such that all splits occurring before this time are represented in the sampled tree but none of the younger splits. In reality, the sampling bias is rarely this extreme, and may be difficult to model properly. Similar modeling challenges apply to the sampling of the fossil record. This raises the question of whether it is possible to find dating methods that are more robust to sampling biases. Here, we show that the skyline FBD (SFBD) process, where the diversification and fossil-sampling rates can vary over time in a piecewise fashion, provides age estimates that are more robust to inadequacies in the modeling of the sampling process and less sensitive to DRA effects. In the SFBD model we consider, rates in different time intervals are either considered to be independent and identically distributed, or assumed to be autocorrelated following an Ornstein-Uhlenbeck (OU) process. Through simulations and reanalyses of the Hymenoptera and Eutheria data, we show that both variants of the SFBD model unify age estimates under random and diversified sampling assumptions. The SFBD model can resolve DRA by absorbing the deviations from the sampling assumptions into the inferred dynamics of the diversification process over time. Although this means that the inferred diversification dynamics must be interpreted with caution, taking sampling biases into account, we conclude that the SFBD model represents the most robust approach available currently for addressing DRA in total-evidence dating.

Sample labialized onsets in Shiguzi Rma (Qiang)

This is a short video file of Bumtsi, a native speaker of Shiguzi Rma (Qiang) (ISO code: cng) saying the words 'water' and 'bridge' in her mother tongue. The purpose of this recording is to illustrate the labialized onsets for both these forms.

Environmental data at the sampling event level collected with Inline instruments, almanach, models and satellites during the Tara Pacific Expedition 2016-2018

The Tara Pacific expedition (2016-2018) sampled coral ecosystems around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples. The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis. Here we provide at the sampling event level, the environmental data originating from all instruments acquiring continuously during the full course of the campaign. This dataset is augmented with the addition of variables originating from almanach (local sun/moon set/rise, local zenith), from operational models obtained from Copernicus Marine Services, but also from satellite imagery (MODIS-AQUA satellite - Level 3 mapped product, 8 day average, 4km resolution) at <a href="https://oceandata.sci.gsfc.nasa.gov">https://oceandata.sci.gsfc.nasa.gov</a>. The zone corresponding to the station position and date was recovered either by taking a two pixel buffer around the given location (total zone being a 5 by 5 pixels square of 20 km side) and in order to propose an alternative measure in the inevitable case where clouds were present an alternative 12 pixels buffer was taken (total zone being a 25 by 25 pixels square of 100 km side). All data were provided as mean, standard deviation (sd) together with 0.05, 0.25, 0.5, 0.75 and 0.95 quartiles

opencc-by-4.0Jun 2022View details →

Enzymatic digestion method development for long-term stored chitinaceous planktonic samples - Data

<table> <tbody> <tr> <td>Data for  Carrillo-Barragán, Priscilla, Heather Sugden, Catherine Scott, and Clare Fitzsimmons. 2022.  “Enzymatic Digestion Method Development for Long-Term Stored Chitinaceous Planktonic Samples.”  Marine Pollution Bulletin. </td> </tr> </tbody> </table>

Sample Beiwe Dataset

This is a public release of Beiwe-generated data. The <a href="https://www.beiwe.org/">Beiwe Research Platform</a> collects high-density data from a variety of smartphone sensors such as GPS, WiFi, Bluetooth, gyroscope, and accelerometer in addition to metadata from active surveys. A description of passive and active data streams, and a documentation concerning the use of Beiwe can be found <a href="https://github.com/onnela-lab/beiwe-backend/wiki">here</a>. This data was collected from an internal test study and is made available solely for educational purposes. It contains no identifying information; subject locations are de-identified using the noise GPS feature of Beiwe. As part of the internal test study, data from 6 participants were collected from the start of March 21, 2022 to the end of March 28, 2022. The local time zone of this study is Eastern Standard Time. Each participant was notified to complete a survey at 9am EST on Monday, Thursday, and Saturday of the study week. An additional survey was administered on Tuesday at 5:15pm EST. For each survey, subjects were asked to respond to the prompt "How much time (in hours) do you think you spent at home?".

Exhaled breath condensate samples (mzML files) analysed by uHPLC-ESI-OrbitrapMS

LC-MS raw data files were converted to mzML using MSConvert (Version: 3.0.20279, ProteoWizard) - QC_pos_xxx: QC samples analysed in positive polarity (11 files) - ACOS_XXX_pos_01 to 03: Asthmatic patients (3 replicates) randomly injected (15 files) - Ctrl_XXX_pos_01 to 03: Control (15 files) - DPOC_XXX_pos_01 to 03: COPD (15 files)   EBC Samples and Clinical Assessment EBC samples were collected from 15 individuals randomly selected (five controls, five with asthma medical diagnosis, and five with COPD medical diagnosis, as assessed by the OLDER Study – Obstructive Lung Diseases in Elders). The Ethics Committee of Nova Medical School approved this study. Asthma was assigned when the patient reported respiratory symptoms, was a nonsmoker, and presented a positive reversibility test (FEV1 > 12% and 200 mL). COPD disease was attributed to those who also reported being current smokers, had a post-bronchodilator FEV1/FVC < 0.70, and had a negative reversibility test. LC–MS Analysis Samples were analyzed in triplicate by LC–MS using an Orbitrap Q Exactive Focus (Thermo Scientific) coupled to an Ultimate 3000 UHPLC (Thermo Scientific). A pooled quality control (QC) sample was used to compensate for any possible time-dependent batch effects. The QC samples were created using a small aliquot from each sample. The QC samples were reinjected at regular intervals to bracket the samples. The separation was performed using a Waters XBridge column C18 (2.1 × 150 mm, 3.5 μm particle size, P/N 186003023). The mobile phase A was water with 0.1% formic acid (v/v), and mobile phase B was acetonitrile with 0.1% formic acid (v/v) (Optima LC–MS Grade, Fisher Scientific). The gradient program was as follows: 1 min at 1% B; 1–13 min from 1 to 99% B, 13–15 min at 99% B, 15–16 min from 99 to 1% B, and 4 min at 1% B. The column temperature was maintained at 30 °C, and a flow rate of 400 μL/min was used. The Q Exactive Focus MS method consisted of several cycles of full MS scan (R = 70000) followed by three ddMS2 scans (R = 17500), with a (N)CE of 30 and in positive mode. External calibration was performed using LTQ ESI Positive Ion Calibration Solution (Thermo Scientific) and the lock mass enabled internal calibration. Data were obtained using the Xcalibur software v.4.0.27.19 (Thermo Scientific). The raw MS files, as recorded by the instrument, are available from the corresponding author upon reasonable request.

opencc-by-4.0Mar 2021View details →

Forming Impressions from Self-Truncated Samples of Traits - Interplay of Thurstonian and Brunswikian Sampling Effects

Data sets for both experiments of the article "Forming Impressions from Self-Truncated Samples of Traits - Interplay of Thurstonian and Brunswikian Sampling Effects" published in Journal of Personality and Social Psychology. Additionally norms for the trait adjectives used as stimulus materials in Experiment 2.

opencc-zeroAug 2019View details →

IODP Expedition 374 Sample report

Report includes detailed information about samples taken for testing: location/depth, type/form factor, test, request number, and database identifier.

Mean, standard deviation, and percentiles of the schema and domain scores of the German version of the Young Schema Questionnaire - Short Form 3 (YSQ-S3) in a German opportunity sample (n=1,150)

Mean, standard deviation, and percentiles of the schema and domain scores of the German version of the Young Schema Questionnaire - Short Form 3 (YSQ-S3) in a German opportunity sample (n=1,150). Details are reported in: Kriston L, Schäfer J, Jacob GA, Härter M, Hölzel LP. Reliability and validity of the German version of the Young Schema Questionnaire - Short Form 3 (YSQ-S3). Eur J Psychol Assess 2013; 29: 205-212. IMPORTANT: This is an opportunity sample that is not representative of any well-defined population. Accordingly, the values should not be used as reference or norm values for the German version of the YSQ-S3.

opencc-zeroMay 2022View details →

dryad40/100

Data from: eDNA metabarcoding of log hollow sediments and soils highlights the importance of substrate type, frequency of sampling and animal size, for vertebrate species detection

Fauna monitoring often relies on visual monitoring techniques such as camera trappings, which have biases leading to underestimates of vertebrate species diversity. Environmental DNA (eDNA) has emerged as a new source of biodiversity data that may improve biomonitoring; however, eDNA based assessments of species richness remain relatively untested in terrestrial environments. We investigated the suitability of fallen log hollow sediment as a source of vertebrate eDNA, across two sites in south-western Australia - one with a Mediterranean climate and the other semi-arid. We compared two different approaches (camera trapping and eDNA metabarcoding) for monitoring of vertebrate species, and investigated the effect of other factors (frequency of species, timing of visits, frequency of sampling, body size) on vertebrate species detectability. Metabarcoding of hollow sediments resulted in the detection of higher species richness in comparison Hollow sediment detected higher species richness (29 taxa: six birds, three reptiles and 20 mammals) to metabarcoding of soil at the entrance of the hollow (13 taxa: three birds, two reptiles and eight mammals). We detected 31 taxa in total with eDNA metabarcoding and 47 with camera traps, with 14 taxa detected by both (12 mammals and two birds). By comparing camera trap data with eDNA read abundance, we were able to detect vertebrates through eDNA metabarcoding that had visited the area up to two months prior to sample collection. Larger animals were more likely to be detected, and so were vertebrates that were identified multiple times in the camera traps. These findings demonstrate the importance of substrate selection, frequency of sampling, and animal size, on eDNA based monitoring. Future eDNA experimental design should consider all these factors as they affect detection of target taxa.

Discrete Flow Cytometry of Underway Samples from Gradients 4 (2021) Using a BD Influx Cell Sorter

The dataset consists of BD Influx-based analysis of phytoplankton populations from discrete flow cytometry data collected underway during the Gradients 2021 (Gradients 4/TN397) oceanographic research cruise in the equatorial Pacific Ocean. The analysis includes cell abundance, forward light scatter, and pigment fluorescence of individual cells, including picoeukaryotes and the cyanobacteria Prochlorococcus and Synechococcus. The analysis also includes estimates of cell size, carbon content, and biomass using forward light scatter values and Mie Theory. Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: https://github.com/fribalet/FCSplankton

Fig. 1. Map showing sampling localities during scientific expeditions carried out along the Brazilian continental shelf and slope area from 1999 in New insights gained from museum collections: new deep-sea species of Typhlotanais (Tanaidacea, Typhlotanaidae) from Brazil

Fig. 1. Map showing sampling localities during scientific expeditions carried out along the Brazilian continental shelf and slope area from 1999 to 2016. Abbreviations: ES = Espírito Santo; RJ = Rio de Janeiro.

openapache2.0May 2022View details →

Test data for jga-analysis per-sample workflow

Test data for jga-analysis per-sample workflow. Please see: - <a href="https://github.com/biosciencedbc/jga-analysis">https://github.com/biosciencedbc/jga-analysis</a> - <a href="https://github.com/biosciencedbc/jga-analysis/blob/main/per-sample/Workflows/per-sample.cwl">https://github.com/biosciencedbc/jga-analysis/blob/main/per-sample/Workflows/per-sample.cwl</a>

Time to Update the Split-Sample Approach in Hydrological Model Calibration v1.1

Time to Update the Split-Sample Approach in Hydrological Model Calibration Hongren Shen1, Bryan A. Tolson1, Juliane Mai1 1Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, Ontario, Canada Corresponding author: Hongren Shen (hongren.shen@uwaterloo.ca) Abstract Model calibration and validation are critical in hydrological model robustness assessment. Unfortunately, the commonly-used split-sample test (SST) framework for data splitting requires modelers to make subjective decisions without clear guidelines. This large-sample SST assessment study empirically assesses how different data splitting methods influence post-validation model testing period performance, thereby identifying optimal data splitting methods under different conditions. This study investigates the performance of two lumped conceptual hydrological models calibrated and tested in 463 catchments across the United States using 50 different data splitting schemes. These schemes are established regarding the data availability, length and data recentness of the continuous calibration sub-periods (CSPs). A full-period CSP is also included in the experiment, which skips model validation. The assessment approach is novel in multiple ways including how model building decisions are framed as a decision tree problem and viewing the model building process as a formal testing period classification problem, aiming to accurately predict model success/failure in the testing period. Results span different climate and catchment conditions across a 35-year period with available data, making conclusions quite generalizable. Calibrating to older data and then validating models on newer data produces inferior model testing period performance in every single analysis conducted and should be avoided. Calibrating to the full available data and skipping model validation entirely is the most robust split-sample decision. Experimental findings remain consistent no matter how model building factors (i.e., catchments, model types, data availability, and testing periods) are varied. Results strongly support revising the traditional split-sample approach in hydrological modeling. Version updates v1.1 Updated on May 19, 2022. We added hydrographs for each catchment. There are 8 parts of the zipped file attached in v1.1. You should download all of them and unzip all those eight parts together. In this update, we added two zipped files in each gauge subfolder:     (1) GR4J_Hydrographs.zip and     (2) HMETS_Hydrographs.zip Each of the zip files contains 50 CSV files. These CSV files are named with keywords of model name, gauge ID, and the calibration sub-period (CSP) identifier. Each hydrograph CSV file contains four key columns:     (1) Date time (note that the hour column is less significant since this is daily data);     (2) Precipitation in mm that is the aggregated basin mean precipitation;     (3) Simulated streamflow in m3/s and the column is named as "subXXX", where XXX is the ID of the catchment, specified in the CAMELS_463_gauge_info.txt file; and     (4) Observed streamflow in m3/s and the column is named as "subXXX(observed)". Note that these hydrograph CSV files reported period-ending time-averaged flows. They were directly produced by the Raven hydrological modeling framework. More information about the format of the hydrograph CSV files can be redirected to the <a href="http://raven.uwaterloo.ca/">Raven webpage</a>. v1.0 First version published on Jan 29, 2022. Data description This data was used in the paper entitled "Time to Update the Split-Sample Approach in Hydrological Model Calibration" by Shen et al. (2022). Catchment, meteorological forcing and streamflow data are provided for hydrological modeling use. Specifically, the forcing and streamflow data are archived in the Raven hydrological modeling required format. The GR4J and HMETS model building results in the paper, i.e., reference KGE and KGE metrics in calibration, validation and testing periods, are provided for replication of the split-sample assessment performed in the paper. Data content The data folder contains a gauge info file (CAMELS_463_gauge_info.txt), which reports basic information of each catchment, and 463 subfolders, each having four files for a catchment, including:     (1) Raven_Daymet_forcing.rvt, which contains Daymet meteorological forcing (i.e., daily precipitation in mm/d, minimum and maximum air temperature in deg_C, shortwave in MJ/m2/day, and day length in day) from Jan 1st 1980 to Dec 31 2014 in a Raven hydrological modeling required format.     (2) Raven_USGS_streamflow.rvt, which contains daily discharge data (in m3/s) from Jan 1st 1980 to Dec 31 2014 in a Raven hydrological modeling required format.     (3) GR4J_metrics.txt, which contains reference KGE and GR4J-based KGE metrics in calibration, validation and testing periods.     (4) HMETS_metrics.txt, which contains reference KGE and HMETS-based KGE metrics in calibration, validation and testing periods. Data collection and processing methods        Data source <ul> <li> Catchment information and the Daymet meteorological forcing are retrieved from the CAMELS data set, which can be found <a href="https://ral.ucar.edu/solutions/products/camels">here</a>.</li> <li> The USGS streamflow data are collected from the U.S. Geological Survey's (USGS) National Water Information System (NWIS), which can be found <a href="https://waterdata.usgs.gov/nwis/sw">here</a>.</li> <li>The GR4J and HMETS performance metrics (i.e., reference KGE and KGE) are produced in the study by Shen et al. (2022).</li> </ul>        Forcing data processing <ul> <li>A quality assessment procedure was performed. For example, daily maximum air temperature should be larger than the daily minimum air temperature; otherwise, these two values will be swapped.</li> <li>Units are converted to Raven-required ones. Precipitation: mm/day, unchanged; daily minimum/maximum air temperature: deg_C, unchanged; shortwave: W/m2 to MJ/m2/day; day length: seconds to days.</li> <li>Data for a catchment is archived in a RVT (ASCII-based) file, in which the second line specifies the start time of the forcing series, the time step (= 1 day), and the total time steps in the series (= 12784), respectively; the third and the fourth lines specify the forcing variables and their corresponding units, respectively.</li> <li>More details of Raven formatted forcing files can be found in the Raven manual (<a href="http://raven.uwaterloo.ca/">here</a>).</li> </ul>        Streamflow data processing <ul> <li>Units are converted to Raven-required ones. Daily discharge originally in cfs is converted to m3/s.</li> <li>Missing data are replaced with -1.2345 as Raven requires. Those missing time steps will not be counted in performance metrics calculation.</li> <li>Streamflow series is archived in a RVT (ASCII-based) file, which is open with eight commented lines specifying relevant gauge and streamflow data information, such as gauge name, gauge ID, USGS reported catchment area, calculated catchment area (based on the catchment shapefiles in CAMELS dataset), streamflow data range, data time step, and missing data periods. The first line after the commented lines in the streamflow RVT files specifies data type (default is HYDROGRAPH), subbasin ID (i.e., SubID), and discharge unit (m3/s), respectively. And the next line specifies the start of the streamflow data, time step (=1 day), and the total time steps in the series(= 12784), respectively.</li> </ul> GR4J and HMETS metrics  The GR4J and HMETS metrics files consists of reference KGE and KGE in model calibration, validation, and testing periods, which are derived in the massive split-sample test experiment performed in the paper. <ul> <li>Columns in these metrics files are gauge ID, calibration sub-period (CSP) identifier, KGE in calibration, validation, testing1, testing2, and testing3, respectively.</li> <li>We proposed 50 different CSPs in the experiment. "CSP_identifier" is a unique name of each CSP. e.g., CSP identifier "CSP-3A_1990" stands for the model is built in Jan 1st 1990, calibrated in the first 3-year sample (1981-1983), calibrated in the rest years during the period of 1980 to 1989. Note that 1980 is always used for spin-up.</li> <li>We defined three testing periods (independent to calibration and validation periods) for each CSP, which are the first 3 years from model build year inclusive, the first 5 years from model build year inclusive, and the full years from model build year inclusive. e.g., "testing1", "testing2", and "testing3" for CSP-3A_1990 are 1990-1992, 1990-1994, and 1990-2014, respectively.</li> <li>Reference flow is the interannual mean daily flow based on a specific period, which is derived for a one-year period and then repeated in each year in the calculation period. <ul> <li>For calibration, its reference flow is based on spin-up + calibration periods.</li> <li>For validation, its reference flow is based on spin-up + calibration periods.</li> <li>For testing, its reference flow is based on spin-up +calibration + validation periods.</li> </ul> </li> <li>Reference KGE is calculated based on the reference flow and observed streamflow in a specific calculation period (e.g., calibration). Reference KGE is computed using the KGE equation with substituting the "simulated" flow for "reference" flow in the period for calculation. Note that the reference KGEs for the three different testing periods corresponds to the same historical period, but are different, because each testing period spans in a different time period and covers different series of observed flow.</li> </ul> More details of the split-sample test experiment and modeling results analysis can be referred to the paper by Shen et al. (2022). Citation Journal Publication This study: Shen, H., Tolson, B. A., & Mai, J.(2022). Time to update the split-sample approach in hydrological model calibration. Water Resources Research, 58, e2021WR031523. <a href="https://doi.org/10.1029/2021WR031523">https://doi.org/10.1029/2021WR031523</a> Original CAMELS dataset: A. J. Newman, M. P. Clark, K. Sampson, A. Wood, L. E. Hay, A. Bock, R. J. Viger, D. Blodgett, L. Brekke, J. R. Arnold, T. Hopson, and Q. Duan (2015). Development of a large-sample watershed-scale hydrometeorological dataset for the contiguous USA: dataset characteristics and assessment of regional variability in hydrologic model performance. Hydrol. Earth Syst. Sci., 19, 209-223, <a href="http://doi.org/10.5194/hess-19-209-2015">http://doi.org/10.5194/hess-19-209-2015</a> Data Publication This study: H. Shen, B. A. Tolson, and J. Mai (2022). Time to Update the Split-Sample Approach in Hydrological Model Calibration. Zenodo. <a href="http://doi.org/10.5281/zenodo.5915374">http://doi.org/10.5281/zenodo.5915374</a> Original CAMELS dataset: A. Newman; K. Sampson; M. P. Clark; A. Bock; R. J. Viger; D. Blodgett, 2014. A large-sample watershed-scale hydrometeorological dataset for the contiguous USA. Boulder, CO: UCAR/NCAR.<a href="http:// https://dx.doi.org/10.5065/D6MW2F4D"> https://dx.doi.org/10.5065/D6MW2F4D</a>

opencc-by-4.0Apr 2021View details →

Serial Coronal Sections Of An Adult Mouse Brain - Sample Dataset

Experimental data: 88 serial coronal sections of the full brain of an adult mouse. Preparation of the sample: details to come... Imaging of the sample: details to come... 2 channels per section are present: <ul> <li>DAPI</li> <li>autofluorescence</li> </ul> This dataset is used in particular as a test dataset for the <a href="https://c4science.ch/w/bioimaging_and_optics_platform_biop/image-processing/image-to-atlas-registration/">Allen Brain Biop Aligner Fiji plugin</a>. Some sections are flipped (left / right), for workflow documentation purpose. Animal handling according to protocols approved by the Swiss animal license  VD2808.1

opencc-by-4.0Dec 2021View details →

Rapid assessment of lipidomics sample quality and quantity using attenuated total reflectance Fourier-transform infrared spectroscopy

In this work, we aimed to develop a simple lipid quality and quantification method for biological lipid extracts, as a step in lipidomics workflows, with minimal sample requirement. We chose FTIR spectroscopy with an Attenuate Total Reflectance (ATR) sampling method as it requires just 1 microliter of MS-ready sample without additional sample preparation. We validated the proposed lipidomics sample quality control workflow using a set of plasma samples (n=107, with 3-4 technical replicates) with comparison to LC-MS-based lipidomics. The following file contains the resulting spectra acquired by ATR-FTIR spectrometry for these plasma samples, standard curves and contaminated samples used for method development. Spectrometer was ambient blanked and detector cleaned between each measurement. Lipid samples were extracted by butanol-methanol (3:1) precipitation, and dried directly onto the ATR-FTIR detector. Absorbance was measured between 4,000 and 650 cm-1 wavenumbers, at a resolution of 8cm-1. Each spectra has been baseline corrected (whole spectra).