Find research datasets worth reusing

opencc-by-4.0Jul 2020View details →

ESA-WOC North Atlantic Sea Surface Salinity maps from a multivariate combination of satellite and in situ surface measurements (2010-2018)

We deliver here the daily sea surface salinity level 4 (SSS L4) product developed in the framework of the  European Space Agency World Ocean Circulation project (ESA-WOC), covering the period 2010-2018. This product was obtained by adapting to a 1/10° North Atlantic grid the multidimensional optimal interpolation algorithm used within the Copernicus Marine Environment Monitoring Service to retrieve the global SSS multi-year dataset (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: MULTIOBS_GLO_PHY_REP_015_002, dataset_id: dataset-sss-ssd-rep-weekly). This algorithm interpolates SMOS observations and in situ SSS observations considering a space-time-thermal decorrelation function, estimated by including information from high-pass filtered daily SST data (Droghei et al., 2016; Buongiorno Nardelli, 2012). Here, we ingested the L3OS 2Q debiased daily valid ocean salinity values product from SMOS satellite, produced and disseminated by the Centre Aval de Traitement des Données SMOS (CATDS, 2017), OSTIA SST data (CMEMS, <a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id=SST_GLO_SST_L4_REP_OBSERVATIONS_010_011) and CORA5.2 surface salinity values (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) as input data, and used CMEMS weekly SSS dataset to build our background field (linearly interpolating it in time between the two closest analysis dates, and upsizing to the 1/10° grid through a cubic spline). All other interpolation parameters were set as in Droghei et al. (2018).               References: Buongiorno Nardelli, B.: A Novel Approach for the High-Resolution Interpolation of In Situ Sea Surface Salinity, J. Atmos. Ocean. Technol., 29(6), 867–879, doi:10.1175/JTECH-D-11-00099.1, 2012. CATDS (2017). CATDS-PDC L3OS 2Q - Debiased daily valid ocean salinity values product from SMOS satellite. CATDS (CNES, IFREMER, LOCEAN, ACRI). http://dx.doi.org/10.12770/12dba510-cd71-4d4f-9fc1-9cc027d128b0 Droghei, R., Buongiorno Nardelli, B. and Santoleri, R.: Combining in-situ and satellite observations to retrieve salinity and density at the ocean surface, J. Atmos. Ocean. Technol., 33, 1211–1223, doi:10.1175/JTECH-D-15-0194.1, 2016. Droghei, R., Buongiorno Nardelli, B. and Santoleri, R.: A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993–2016), Front. Mar. Sci., 5(March), 1–13, doi:10.3389/fmars.2018.00084, 2018. Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.

opencc-by-4.0Mar 2024View details →

Collider Bias Correction for Multiple Covariates in GWAS Using Robust Multivariable Mendelian Randomization

This repository contains the data underlying the figures in paper "Collider Bias Correction for Multiple Covariates in GWAS Using Robust Multivariable Mendelian Randomization".     The file names and sheet names in the xlsx file indicate the corresponding figures of data.  The underlying data of manhattan plots and QQ plots are in text file. For other figures, the underlying data are in the spreadsheet. In each file, column names indicate the MVMR method used to obtain the result.  For example:  In text files: The abbreviation "mPC" refers to metabolomic principle components. beta_no_correction: the SNP effect estimate without bias correction. beta_cml or beta_MVMR_cml: the standard error of SNP effect estimate after the bias correction of MVMR-cML. SE_UVMR_cml: the standard error of SNP effect estimate after the bias correction of UVMR-cML. p_value_Egger or p_value_MVMR_Egger: the p-value of SNP effect estimate after the bias correction of MVMR-Egger regression. In the spreadsheet, column names follow the same style.  The GWAS data is also available. The column names follows the plink output file. The detailed explanations are available at https://www.cog-genomics.org/plink/2.0/formats#glm_linear

openother-openMar 2022View details →

Geopotential-based Multivariate MJO Index (GMM Index)

This repository contains the data of the Geopotential-based Multivariate MJO Index (GMM Index).   About the GMM Index <ul> <li>The GMM index is an RMM-like index, derived from outgoing longwave radiation (OLR), 200hPa and 850hPa zonal wind, that can be extended to the pre-satellite era where satellite-based OLR data is unavailable.</li> <li>The short record of OLR observation limits the data length of the RMM index (<a href="https://journals.ametsoc.org/view/journals/mwre/132/8/1520-0493_2004_132_1917_aarmmi_2.0.co_2.xml">Wheeler and Hendon, 2004</a>), hindering the research of long-term variability of the Madden-Julian Oscillation (MJO) in the past century. And, the GMM index, which can be extended to the early 20th century (and even to the 19th century), is proposed to solve the problem.</li> <li>The construction method of the GMM index is the same as the RMM index, except that: <ul> <li>the OLR input is derived from upper-tropospheric geopotential data, based on the intrinsic relationship between MJO convection and upper-level geopotential (see <a href="https://doi.org/10.1007/s00382-016-3431-x">Leung and Qian, 2017</a>);</li> <li>the OLR, 200hPa and 850hPa zonal wind input of the GMM index are bandpass-filtered anomalies.</li> </ul> </li> <li>Please refer to <a href="https://doi.org/10.1007/s00382-022-06142-2">Leung et al. (2022)</a> for more detail about the calculation procedure and the theory behind it.</li> </ul>   Files <ul> <li>GMM index derived based on the ERA-Interim reanalysis <ul> <li>Location: <a href="https://github.com/jeremychleung/Geopotential-based-Multivariate-MJO-Index/blob/main/data/gmm_index_erai.csv">data/gmm_index_erai.csv</a></li> <li>Temporal coverage: 1979–2013</li> </ul> </li> <li>GMM index derived based on the ERA-20C reanalysis <ul> <li>Location: <a href="https://github.com/jeremychleung/Geopotential-based-Multivariate-MJO-Index/blob/main/data/gmm_index_era20c.csv">data/gmm_index_era20c.csv</a></li> <li>Temporal coverage: 1900–2010</li> </ul> </li> <li>GMM index derived based on the NOAA-20CRv3 reanalysis <ul> <li>Link: (to be uploaded)</li> <li>Temporal coverage: 1836–2015</li> </ul> </li> </ul>   Citation If you use the GMM index in a publication or for any other purposes, please cite <ul> <li>Leung, J.CH., Qian, W., Zhang, P. et al. Geopotential-based Multivariate MJO Index: extending RMM-like indices to pre-satellite era. Clim Dyn (2022). <a href="https://doi.org/10.1007/s00382-022-06142-2">https://doi.org/10.1007/s00382-022-06142-2</a></li> <li>Zenodo archive: <a href="https://doi.org/10.5281/zenodo.6331379">https://doi.org/10.5281/zenodo.6331379</a></li> </ul>   References <ul> <li>Leung, J.CH., Qian, W., Zhang, P. et al. Geopotential-based Multivariate MJO Index: extending RMM-like indices to pre-satellite era. Clim Dyn (2022). <a href="https://doi.org/10.1007/s00382-022-06142-2">https://doi.org/10.1007/s00382-022-06142-2</a></li> <li>Leung, J.CH., Qian, W. Monitoring the Madden–Julian oscillation with geopotential height. Clim Dyn 49, 1981–2006 (2017). <a href="https://doi.org/10.1007/s00382-016-3431-x">https://doi.org/10.1007/s00382-016-3431-x</a></li> <li>Wheeler, M.C., Hendon, H.H. An All-Season Real-Time Multivariate MJO Index: Development of an Index for Monitoring and Prediction. Mon Weather Rev 132:1917–1932 (2004). <a href="https://journals.ametsoc.org/view/journals/mwre/132/8/1520-0493_2004_132_1917_aarmmi_2.0.co_2.xml">https://journals.ametsoc.org/view/journals/mwre/132/8/1520-0493_2004_132_1917_aarmmi_2.0.co_2.xml</a></li> </ul>   Contact If you have any questions about the data, feel free to contact Dr. Jeremy Leung (<a href="mailto:chleung@pku.edu.cn">chleung@pku.edu.cn</a> or <a href="mailto:liangzx@gd121.cn">liangzx@gd121.cn</a>).

opencc-by-4.0Aug 2022View details →

Repeated and multivariate measures of perceived distance

A dataset of repeated measures of distance perception at physical distances of 7, 8, 9, 10, and 11 meters. The data are also multivariate, with five dependent measures of distance perception. This is a 5 (physical distance) x 5 (dependent measure) within-participants design with a sample size of 46. Note data is missing for 15 trials due participant and experimenter errors. The csv file has 230 rows and 7 columns. Subject: Unique identifier for each participant.  Physical Distance: Physical distance from the participant to the target cone, in meters. Blindwalk Away: Participants put on the blindfold after viewing the target. Next, participants took one step to the left and turned 180 degrees to face the opposite direction. Participants were instructed to walk forward until they had walked the original distance to the target.                   Blindwalk Toward: Participants put on the blindfold after viewing the target.  Next, participants walked forward until they thought they had reached the target cone. Triangulated BW: Participants put on the blindfold after viewing the target. Next, participants turned right 90 degrees and walked forward 5 meters. The experimenter told participants when to stop walking. Finally, participants turned to face toward the target and walked forward two steps. Verbal: Participants stated the distance between the target cone and themselves, in feet and inches.  Visual Matching: An experimenter stood next to the target cone and walked away from the cone in a straight line that was  perpendicular to the extent  between the target and the participant. Participants instructed the experimenter to stop walking when they thought that the distance between the target and the experimenter was equal to  the target distance.

opencc-by-4.0Jun 2024View details →

Dynamical System Multivariate Time Series

The Dynamical System Multivariate Time Series (DSMTS) Dataset consists of commands, external stimuli, and telemetry readings of a simulated complex dynamical system under fully nominal conditions (no outliers or anomalies). The DSMTS Dataset exhibits a set of desirable properties that make it very suitable for benchmarking Multivariate Time Series Forecasting especially for industrial processes of complex systems: <ul> <li>Multivariate (17 variables) including sensors reading and control signals. It simulates the operational behaviour of an arbitrary complex system including: <ul> <li>4 Deliberate Actuations / Control Commands sent by a simulated operator / controller, for instance, commands of an operator to turn ON/OFF some equipment.</li> <li>3 Environmental Stimuli / External Forces acting on the system and affecting its behaviour, for instance, the wind affecting the orientation of a large ground antenna.</li> <li>10 Telemetry Readings representing the observable states of the complex system by means of sensors, for instance, a position, a temperature, a pressure, a voltage, current, humidity, velocity, acceleration, etc.</li> </ul> </li> <li>5 million timestamps. Sensors readings are at 1Hz sampling frequency. </li> <li>Pure signal ideal for robustness-to-noise analysis. The simulated signals are provided without noise: while this may seem unrealistic at first, it is an advantage since users of the dataset can decide to add on top of the provided series any type of noise and choose an amplitude. This makes it well suited to test how sensitive and robust detection algorithms are against various levels of noise.</li> <li>No missing data. You can drop whatever data you want to assess the impact of missing values on your detector with respect to a clean baseline.</li> </ul>

opencc-by-4.0Aug 2024View details →

Accelerometer-Based Multivariate Time-Series Dataset for Calf Behavior Classification

AcTBeCalf Dataset Description The AcTBeCalf dataset is a comprehensive dataset designed to support the classification of pre-weaned calf behaviors from accelerometer data. It contains detailed accelerometer readings aligned with annotated behaviors, providing a valuable resource for research in multivariate time-series classification and animal behavior analysis. The dataset includes accelerometer data collected from 30 pre-weaned Holstein Friesian and Jersey calves, housed in group pens at the Teagasc Moorepark Research Farm, Ireland. Each calf was equipped with a 3D accelerometer sensor (AX3, Axivity Ltd, Newcastle, UK) sampling at 25 Hz and attached to a neck collar from one week of birth over 13 weeks. This dataset encompasses 27.4 hours of accelerometer data aligned with calf behaviors, including both prominent behaviors like lying, standing, and running, as well as less frequent behaviors such as grooming, social interaction, and abnormal behaviors. The dataset consists of a single CSV file with the following columns: <ul> <li>dateTime: Timestamp of the accelerometer reading, sampled at 25 Hz.</li> <li>calfid: Identification number of the calf (1-30).</li> <li>accX: Accelerometer reading for the X axis (top-bottom direction)*.</li> <li>accY: Accelerometer reading for the Y axis (backward-forward direction)*.</li> <li>accZ: Accelerometer reading for the Z axis (left-right direction)*.</li> <li>behavior: Annotated behavior based on an ethogram of 23 behaviors.</li> <li>segId: Segment identification number associated with each accelerometer reading/row, representing all readings of the same behavior segment.</li> </ul> * the directions are mentioned in relation to the position of the accelerometer sensor on the calf. Code Files Description The dataset is accompanied by several code files to facilitate the preprocessing and analysis of the accelerometer data and to support the development and evaluation of machine learning models. The main code files included in the dataset repository are: <ol> <li>accelerometer_time_correction.ipynb: This script corrects the accelerometer time drift, ensuring the alignment of the accelerometer data with the reference time.</li> <li>shake_pattern_detector.py: This script includes an algorithm to detect shake patterns in the accelerometer signal for aligning the accelerometer time series with reference times.</li> <li>aligning_accelerometer_data_with_annotations.ipynb: This notebook aligns the accelerometer time series with the annotated behaviors based on timestamps.</li> <li>manual_inspection_ts_validation.ipynb: This notebook provides a manual inspection process for ensuring the accurate alignment of the accelerometer data with the annotated behaviors.</li> <li>additional_ts_generation.ipynb: This notebook generates additional time-series data from the original X, Y, and Z accelerometer readings, including Magnitude, ODBA (Overall Dynamic Body Acceleration), VeDBA (Vectorial Dynamic Body Acceleration), pitch, and roll.</li> <li>genSplit.py: This script provides the logic used for the generalized subject separation for machine learning model training, validation and testing.</li> <li>active_inactive_classification.ipynb: This notebook details the process of classifying behaviors into active and inactive categories using a RandomForest model, achieving a balanced accuracy of 92%.</li> <li>four_behv_classification.ipynb: This notebook employs the mini-ROCKET feature derivation mechanism and a RidgeClassifierCV to classify behaviors into four categories: drinking milk, lying, running, and other, achieving a balanced accuracy of 84%.</li> </ol> Kindly cite one of the following papers when using this data: Dissanayake, O., McPherson, S. E., Allyndrée, J., Kennedy, E., Cunningham, P., & Riaboff, L. (2024). Evaluating ROCKET and Catch22 features for calf behaviour classification from accelerometer data using Machine Learning models. arXiv preprint arXiv:2404.18159. Dissanayake, O., McPherson, S. E., Allyndrée, J., Kennedy, E., Cunningham, P., & Riaboff, L. (2024). Development of a digital tool for monitoring the behaviour of pre-weaned calves using accelerometer neck-collars. arXiv preprint arXiv:2406.17352

opencc-by-4.0Oct 2023View details →

Multivariate analysis of FcR-mediated NK cell functions identifies unique clustering among humans and rhesus macaques - dataset

Dataset from Tuyishime M, Spreng RL, et al. Multivariate analysis of FcR-mediated NK cell functions identifies unique clustering among humans and rhesus macaques. Frontiers in Immunology 2023 doi: 10.3389/fimmu.2023.1260377

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The role of fire in the carbon dynamics of the boreal forest I. - Response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach (2003-2100).

The boreal forest contains large reserves of carbon, and across this region wildfire is a common occurrence. To improve the understanding of how wildfire influences the carbon dynamics of this region, methods were developed to incorporate the spatial and temporal effects of fire into the Terrestrial ecosystem Model (TEM). The historical role of fire on carbon dynamics of the boreal region was evaluated within the context of ecosystem responses to changing atmospheric CO2 and climate. These results show that the role of historical fire on boreal carbon dynamics resulted in a net carbon sink; however, fire plays a major role in the interannual and decadal scale variation of source/sink relationships. To estimate the effects of future fire on boreal carbondynamics, spatially and temporally explicit empirical relationships between climate andfire were quantified. Fuel moisture, monthly severity rating, and air temperature explained a significant proportion of observed variability in annual area burned. These relationships were used to estimate annual area burned for future scenarios of climate change and were coupled to TEM to evaluate the role of future fire on the carbon dynamics of the North American boreal region for the 21st Century. Simulations with TEM indicate that boreal North America is a carbon sink in response to CO2 fertilization, climate variability, and fire, but an increase in fire leads to a decrease in the sink strength. While this study highlights the importance of fire on carbon dynamics in the boreal region, there are uncertainties in the effects of fire in TEM simulations. These uncertainties are associated with sparse fire data for northern Eurasia, uncertainty in estimating carbon consumption, and difficulty in verifying assumptions about the representation of fires that occurred prior to the start of the historical fire record. Future studies should incorporate the role of dynamic vegetation to more accurately represent post-fire successional pr

openOpenDec 2008View details →

opencc-by-4.0Dec 2019View details →

R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper

This repository contains the R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper.

opencc-by-sa-4.0Feb 2016View details →

Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS - Dataset

These files are the dataset for the following paper "Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS". Authors: Prihadi Sumintadireja1, Dasapta Erwin Irawan1, Yuano Rezky2, Prana Ugiana Gio3, Anggita Agustin1

opencc-by-4.0May 2023View details →

Data availability. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence.

Data availability. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence. <ul><li>SPSS DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15).</li><li>EXCEL DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15).</li><li>Data of Project factorial.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel).</li><li>Data Project reliability.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel).</li><li>FIGURES. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (Figure 1.jpeg, Figure 2.jpeg, Figure 3 and Figure 4.jpeg).</li></ul>

Fig. 18 in The Os Navicular of Humans, Great Apes, OH 8, Hadar, and Oreopithecus: Function, Phylogeny, and Multivariate Analyses

Fig. 18. Proximal views of the right cuboid and navicular of a human (A) and a common chimpanzee (B) in a closepacked position showing the contribution of the talocuboid angle (v) to the height of the transverse arch (h). In the closepacked position the small angle shown by chimpanzees results in a higher transverse arch than normally seen in humans. The human arch, however, unlike that of chimpanzees, exhibits a fixed height. Owing to a small, irregular, and often absent cuboid facet, the human talocuboid angle could not be accurately measured.

Fig. 17 in The Os Navicular of Humans, Great Apes, OH 8, Hadar, and Oreopithecus: Function, Phylogeny, and Multivariate Analyses

Fig. 17. Contribution of navicular torsion (v) and the frontal mesoectocuneiform angle (w) to the relative set of the ectocuneiform, mesocuneiform, and the talar facet in the closepacked position as seen in line drawings of an exploded right foot of a human (A) and a gorilla (B) from a dorsodistal view. High values of the frontal mesoectocuneiform angle in humans (w) do not result in marked opposition of the second and third metatarsals given metatarsal, ectocuneiform, and mesocuneiform torsion values which correct for the imparted set. Despite similar torsion values in humans and gorillas, the metatarsal, ectocuneiform, and mesocuneiform torsion all contribute to causing more marked opposition of the second and third metatarsals in gorillas. Marked talar torsion or large frontal mesoectocuneiform angles, are also associated to a high transverse arch (see text).

Fig. 16 in The Os Navicular of Humans, Great Apes, OH 8, Hadar, and Oreopithecus: Function, Phylogeny, and Multivariate Analyses

Fig. 16. Dorsal view of the exploded left tarsus and metatarsus of a pygmy chimpanzee in the closepacked position showing the contribution of the transverse mesoectocuneiform angle (w) and the transverse cuboectocuneiform angle (v) to the divergence of the second through fourth metatarsals. Correction of the talocuboid angle by the facet sets on the cuboid and ectocuneiform results in third and fourth metatarsals that are nearly alinged (y). A relatively low transverse mesoectocuneiform angle results in a second metatarsal that is divergent from the most lateral three (x) despite a partial correction of this set by the mesocuneiform.

Fig. 15 in The Os Navicular of Humans, Great Apes, OH 8, Hadar, and Oreopithecus: Function, Phylogeny, and Multivariate Analyses

Fig. 15. Contributions of the entoectocuneiform and sagittal taloectocuneiform angles (w and v respectively) to the relative set of the first through third metatarsal in closepacked position as seen in line drawings of the exploded left foot of a human (A) and of a gorilla (B) in medial view. The large sagittal taloectocuneiform angle in gorillas imparts a dorsiflexed set to the third metatarsal and is associated with a dorsiflexed talar head, i.e. small angle of talar neck inclination (Day and Wood 1968). The gorilla entoectocuneiform angle imparts a plantar set to the entocuneiform relative to the ectocuneiform and is associated with an abducted hallux, i.e. plantar divergence of the hallux relative to second (x) and third metatarsals (y). The human taloectocuneiform and entoectocuneiform angles are associated with a plantar flexed talar head (i.e., large angle of talar neck inclination), nearly aligned first to third metatarsals, and a longitudinal plantar arch. Due to a fixed transverse arch in humans, however, the long axis of the second and third metatarsals must have a more plantar inclination than the hallux, and the value of x and y are negative. Because the major axis of the navicular's talar facet is not necessarily held vertically, the sagittal taloectocuneiform and entoectocuneiform angles may also impart some degree of medial divergence to the hallux.

Fig. 14 in The Os Navicular of Humans, Great Apes, OH 8, Hadar, and Oreopithecus: Function, Phylogeny, and Multivariate Analyses

Fig. 14. Plot of mean canonical variate scores for studied taxa. All fossils are based on single samples. The actual Mahalanobis D for all of the canonical variates separating taxa is given as the value above each connecting line (table 11). Connecting lines represent a Minimum Spanning Tree (after Rohlf, 1997). Owing to a twodimensional projection, the actual lengths of the connecting lines on the plot represent only a fraction of the D values.