Find research datasets worth reusing

opencc-by-4.0Jun 2023View details →

In vivo rat brain for Ultrasound Localization Microscopy: raw and beamformed data.

Datasets provided for Open Platform for Ultrasound Localization Microscopy: Performance Assessment of Localization Algorithms. Abstract: Ultrasound Localization Microscopy (ULM) is an ultrasound imaging technique that relies on the acoustic response of sub-wavelength ultrasound scatterers to map the microcirculation with an order of magnitude increase in resolution. Initially demonstrated in vitro, this technique has matured and sees implementation in vivo for vascular imaging of organs, and tumors in both animal models and humans. The performance of the localization algorithm greatly defines the quality of vascular mapping. We compiled and implemented a collection of ultrasound localization algorithms and devised three datasets in silico and in vivo to compare their performance through 18 metrics. We also present two novel algorithms designed to increase speed and performance. By openly providing a complete package to perform ULM with the algorithms, the datasets used, and the metrics, we aim to give researchers a tool to identify the optimal localization algorithm for their usage, benchmark their software and enhance the overall image quality in the field while uncovering its limits. This article provides all materials and post-processing scripts and functions. Methods: 200.000 ultrasound images have been acquired in vivo on a rat brain with skull removal at 1000 Hz with a 15 MHz linear probe. This dataset contains raw radiofrequency data (RF) and beamformed images (IQ) of the brain vascularization with flowing microbubbles (ultrasound contrast agent). Article to be cited: Heiles, Chavignon, Hingot, Lopez, Teston and Couture. <a href="http://doi.org/10.1038/s41551-021-00824-8">Performance benchmarking of microbubble-localization algorithms for ultrasound localization microscopy, Nature Biomedical Engineering, 2022, (doi.org/10.1038/s41551-021-00824-8)</a>. Related processing scripts and codes: <a href="https://github.com/AChavignon/PALA">github.com/AChavignon/PALA</a> Related datasets: <a href="https://doi.org/10.5281/zenodo.4343435">doi.org/10.5281/zenodo.4343435</a> Acknowledgments: We thank Cyrille Orset (INSERM UMR-S U1237, Physiopathology and Imaging of Neurological Disorders, GIP Cyceron, BB@C, Caen, France) for animals’ preparation and perfusion of contrast agent and the biomedical imaging platform CYCERON (UMS 3408 Unicaen/CNRS, Caen, France).

Monipar Database: smartwatch movement data to monitor motor competency in subjects with Parkinson's disease

Movement data was collected through smartwatches to monitor motor competence in subjects with Parkinson's Disease (PD). The data set collected for the Monipar study consists of triaxial acceleration data from 21 subjects with PD and 7 healthy control subjects when performing a set of physical exercises while wearing an off-the-shelf smartwatch. Each participant performed the complete set of eight exercises once a week, commonly on the same day and at a similar time. Three Matlab files are provided that contain the raw data of the experimental subgroups: (1) Supervised, (2) Remote, and (3) Healthy control. Additionally, two Matlab files are provided containing the Tremor Labels for selected subjects in the experimental subgroups: (1) Supervised and (2) Remote.While the implementation of the experimental protocol for collecting movement data followed a consistent approach for all participants, three distinct experimental subgroups were established:Remote group: This subgroup consisted of individuals diagnosed with Parkinson's disease (PD) who completed the experimental protocol at their regular PD association.Supervised group: This subgroup comprised PD patients who underwent the experimental protocol under circumstances similar to the remote group. Additionally, clinical scoring (MDS-UPDRS) is reported for this group in the file "MONIPAR SUBJECTS DATA.xlsx"Healthy control group: This subgroup consisted of healthy participants who performed exercises under the supervision of research project team members.Data was collected using a sample rate of 50Hz and expressed in m/s^2.Check the "Monipar_README.txt" file for details about this dataset. Further details are contained in the following reference -- if you use this dataset, please cite:Sigcha, L., Polvorinos-Fernández, C., Costa, N., Costa, S., Arezes, P., Gago, M., ... & Pavón, I. "Monipar: Movement data collection tool to monitor motor symptoms in Parkinson's disease using smartwatches and smartphones". Frontiers in Neurology, 14, 1326640. <a href="https://doi.org/10.3389/fneur.2023.1326640">https://doi.org/10.3389/fneur.2023.1326640</a>References:Sigcha, L. et al. (2022). Bradykinesia Detection in Parkinson's Disease Using Smartwatches' Inertial Sensors and Deep Learning Methods. Sensors 11, 3879Sigcha, L. et al. (2021). Automatic Resting Tremor Assessment in Parkinson's Disease Using Smartwatches and Multitask Convolutional Neural Networks. Sensors 21, 291.Funding:This research was funded by the following projects:(1) "Tecnologías Capacitadoras para la Asistencia, Seguimiento y Rehabilitación de Pacientes con Enfermedad de Parkinson". Centro Internacional sobre el envejecimiento, CENIE (código 0348_CIE_6_E) Interreg V-A España-Portugal (POCTEP).(2) FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.

opencc-by-4.0Jun 2023View details →

Detecting cosmic voids via maps of geometric optics parameters

<ul> <li>lensing-ddbb4ac.pdf  - research data in pdf format</li> <li>void_matches*.dat - plain text results files corresponding to Table 3 and Figures 2, 4, 6, 8.</li> <li>lensing-ddbb4ac-journal.tar.gz - source package for producing the article pdf, together with the reproducibility package, but without the git history; appropriate for ArXiv</li> <li>lensing-ddbb4ac-git.bundle - git source package that can be unbundled with 'git clone lensing-e4f7af0-git.bundle' and used for reproducibility: to download data, do calculations, analyse them, plot them and produce the research data pdf</li> <li>software-ddbb4ac.tar.gz - this should contain all the software, apart from a minimal POSIX-compatible system and LaTeX packages, needed for compiling and installing the software used in producing this work</li> <li>lensing-ddbb4ac-snapshot.tar.gz - source files of the project; these should be enough, provided that external software packages can be downloaded, to reproduce the full project</li> </ul> The authors grant a perpetual, non-exclusive licence to distribute this pdf preprint. All the other materials here are free-licensed, as stated in the individual files and packages.

ΔG-RDKit: Solvation Free Energy Database

We present the full database of the article "Explainable Supervised Machine Learning Model to Predict Solvation Free Energy". This is the database used for a ML model, containing a variety of solvent-solute pairs with known experimental solvation free energy ΔGsolv values. Data entries were collected from two separate databases. The <a href="https://link.springer.com/article/10.1007/s10822-014-9747-x">FreeSolv</a> library, with 642 experimental aqueous ΔGsolv determinations and the <a href="https://mediatum.ub.tum.de/1452571?v=1">Solv@TUM</a> database with 5597 entries for non-aqueous solvents. Both databases were selected given their wide-scale of solute/solvents pairs, amassing 6239 experimental values across light and heavy-atom solutes with a diverse solvent structure and with small value uncertainties. Experimental ΔGsolv values range from -14 to 4 kcal mol-1 and each solute/solvent pair is represented by their chemical family, SMILES string and InChlKey. We generated 213 chemical descriptors for every solvent and solute in each entry using <a href="http://http://www.rdkit.org/">RDKit</a> software, version 2022.09.4, running on top of Python 3.9. Descriptors were calculated from the “MolFromSmiles” function in “RDKIT.Chem” as descriptors with non-numerical values were removed. The descriptors encode significant chemical information and are used to present physicochemical characteristics of compounds, building a relationship between structure and ΔGsolv. Through Machine Learning regression algorithms, our models were able to make ΔGsolv predictions with high accuracy, based on the information encoded in each chemical feature.

opencc-by-4.0Dec 2022View details →

Titanium Alloys Database for Medical Applications

The new 2.0 version (12.7.2023) includes the following modifications: 247 biocompatible Ti alloys; the table shows only literature data; a Jupyter notebook provides the calculated data. In this database, 238 titanium alloys were collected, almost entirely of biocompatible alloying elements. The primary motivation behind creating such a database is to establish a foundation for designing new alloys using machine learning methods. The database can assist researchers, engineers, and biomedical professionals in developing titanium alloys for various medical applications, thereby improving health outcomes and driving advancements in biomaterials and biomedical engineering. For more information read the paper at: <a href="https://doi.org/10.30544/MMD5"> https://doi.org/10.30544/MMD5 </a> NOTE: To avoid misunderstandings, please cite both the database and the published article when citing this database. We invite other authors to contribute to the updating of this database (send at least 20 new alloys to appear as co-author)

Technical Leverage Analysis in the Python Ecosystem

Technical Leverage Analysis in the Python Ecosystem This dataset is the original dataset used in the publication [1]. It includes 21205 distinct package versions from the top 600 Python packages.  An online demo for computing the proposed metrics for real-world software libraries is also available under the following URL: https://techleverage.eu/. This work has been partially funded by the EU under the H2020 Program AssureMOSS (Grant n. 952647).  [1] DOI: 10.1007/s10664-023-10355-2

Dataset of the manuscript "Assessing the influence of Eisenia andrei on the decomposition of Casuarina equisetifolia litter in vermicompost."

Data generated during an experiment of decomposition of Casuarina equisetifolia litter by the application of vermicompost (VC) or the combination vermicompost + the earthworm Eisenia andrei (E). Six files are included: "readme.csv" is a file where we explain the meaning of each column (and in which units is expressed) in each of the other five files. "earthworm_N_biomass.csv" is a table with the number of Eisenia andrei individuals and the total earthworm fresh weight in each of the experimental units we sampled "FTIR_spectra.csv" is a file with the raw spectral data we obtained from the litter by Fourier Transform Infrared spectroscopy combined with Attenuated Total Reflectance (FTIR-ATR). First column indicate the wavenumber (cm-1) and the other columns indicate the absorbance values of each litter sample for each wavenumber. "litter_chemical_composition.csv" is a file with the raw data of the concentrations of different chemical elements measured in C. equisetifolia litter collected at different decomposition times. "litter_mass_loss.csv" contains the dry weight data of the litter at time 0 and after each collection time, as well as the percentage of litter mass loss with time. . "mesofaunal_com.csv" are the numbers of individuals of several groups of mesofaunal organisms (collembolans, mites, and others) we recovered in each of our experimental units.

opencc-by-4.0Mar 2023View details →

Data on a citation context analysis focusing on natural sciences and social sciences and humanities

This dataset contains data on citation context analysis between natural sciences (NS) and social sciences and humanities (SSH). In particular, the data were created through manual coding of each citation between papers related to SDG7 (renewable energy) and SDG13 (climate change) and papers cited by them. This dataset consists of 9 files, associated with the article: Nishikawa, K. How and why are citations between disciplines made? A citation context analysis focusing on natural sciences and social sciences and humanities. Scientometrics (2023). <a href="https://doi.org/10.1007/s11192-023-04664-y">https://doi.org/10.1007/s11192-023-04664-y</a>   The files are numbered as follows: <ul> <li>00 – README</li> <li>01 – Data by citation pair for SDG7 (original)</li> <li>02 – Data by citation pair for SDG13 (original)</li> <li>03 – Data by mention location for SDG7 (original)</li> <li>04 – Data by mention location for SDG13 (original)</li> <li>05 – Data by citation pair for SDG7 (additional)</li> <li>06 – Data by citation pair for SDG13 (additional)</li> <li>07 – Data by mention location for SDG7 (additional)</li> <li>08 – Data by mention location for SDG13 (additional)</li> </ul> See README for more information.

Wind Stress, Wind Stress Curl, and Upwelling Velocities in the Northwest Atlantic (80-45W, 30-45N) during 1980-2019

This dataset contains three netcdf files that pertain to monthly, seasonal, and annual fields of surface wind stress, wind stress curl, and curl-derived upwelling velocities over the Northwest Atlantic (80-45W, 30-45N) covering a forty year period from 1980 to 2019. Six-hourly surface (10 m) wind speed components from the Japanese 55-year reanalysis (JRA-55; Kobayashi et al., 2015) were processed from 1980 to 2019 over a larger North Atlantic domain of 100W to 10E and 10N to 80N. Wind stress was computed using a modified step-wise formulation, originally based on (Gill, 1982) and a non-linear drag coefficient (Large and Pond, 1981), and later modified for low speeds (Trenberth et al., 1989). See Gifford (2023) for more details.    After the six-hourly zonal and meridional wind stresses were calculated, the zonal change in meridional stress (curlx) and the negative meridional change in zonal stress (curly) were found using NumPy’s gradient function in Python (Harris et al., 2020) over the larger North Atlantic domain (100W-10E, 10-80N). The curl (curlx + curly) over the study domain (80-45W, 10-80N) is then extracted, which maintain a constant order of computational accuracy in the interior and along the boundaries for the smaller domain in a centered-difference gradient calculation.  The monthly averages of the 6-hour daily stresses and curls were then computed using the command line suite climate data operators (CDO, Schulzweida, 2022) monmean function. The seasonal (3-month average) and annual averages (12-month average) were calculated in Python using the monthly fields with NumPy (NumPy, Harris et al., 2020).  Corresponding upwelling velocities at different time-scales were obtained from the respective curl fields and zonal wind stress by using the Ekman pumping equation of the study by Risien and Chelton (2008; page 2393). Please see Gifford (2023) for more details.    The files each contain nine variables that include longitude, latitude, time, zonal wind stress, meridional wind stress, zonal change in meridional wind stress (curlx), the negative meridional change in zonal wind stress (curly), total curl, and upwelling. Units of time begin in 1980 and are months, seasons (JFM etc.), and years to 2019. The longitude variable extends from 80W to 45W and latitude is 30N to 45N with uniform 1.25 degree resolution.   Units of stress are in Pascals, units of curl are in Pascals per meter, and upwelling velocity is described by centimeters per day. The spatial grid is a 29 x 13 longitude x latitude array.  Filenames:  monthly_windstress_wsc_upwelling.nc: 480 time steps from 80W to 45W and 30N to 45N. seasonal_windstress_wsc_upwelling.nc: 160 time steps from 80W to 45W and 30N to 45N. annual_windstress_wsc_upwelling.nc: 40 time steps from 80W to 45W and 30N to 45N.

Dataset for "The magnetized (2+1)-dimensional Gross-Neveu model at finite density"

We perform a lattice study of the (2+1)-dimensional Gross-Neveu model in a background magnetic field B and at non-zero chemical potential μ. The complex-action problem arising in our simulations using overlap fermions is under control. For B=0 we observe a first-order phase transition in μ even at non-vanishing temperatures. Our main finding, however, is that the rich phase structure found in the limit of infinite flavor number Nf is washed out by the fluctuations present at Nf=1. We find no evidence for inverse magnetic catalysis, i.e., the decrease of the order parameter of chiral symmetry breaking with B for μ close to the chiral phase transition. Instead, the magnetic field tends to enhance the breakdown of chiral symmetry for all values of μ below the transition. Moreover, we find no trace of spatial inhomogeneities in the order parameter. We briefly comment on the potential relevance of our results for QCD. If you use this data, please cite the corresponding paper: https://doi.org/10.48550/arXiv.2304.14812 (or better the not-yet-existing published version)

Synthetic and real EEG datasets for closed-loop neuroscience

The dataset is made primarily for the task of real-time low latency filtering of the EEG data in the closed loop neuroscience experiments and for EEG forecasting task. The dataset consists of a real data and 5 options of the synthetic data of varying difficulty.The real dataset consists of 25 people involved into the P4 alpha neurofeedback training. Its total size is about 16.3 hours. A more detailed instruction for this file is provided in the file Real dataset instructions.txt.Synthetic data is generated in 5 different ways: sine wave with white noise, sine wave with pink noise, narrow-band filtered pink noise sample with pink noise, state-space model with white noise and state-space model with pink noise. Each of these datasets has about 34.5 hours of data. It is generated similarly to (Wodeyar et al, 2021). A more detailed instruction for the synthetic dataset can be found in the file Synthetic datasets instructions.txt.  In LowLatencyEEGFiltering.zip one can find a code for the models used in our paper for low-latency filtering with this data.NOTE: Code is also published in the following GitHub repository: https://github.com/ivsemenkov/LowLatencyEEGFiltering If you use our data or code please cite: https://www.doi.org/10.1088/1741-2552/acf7f3

Large SEM-BSE images of hydrated alite of ages from 1 day up to 1 year

This dataset contains 8-Bit SEM-BSE images of commercially available tricalcium silicate (C3S; alite, MIII polymorph; Vustah, Czech Republic). The alite was mixed with a water/binder ratio of 0.5. The paste was the cast in small sealed containers, which were submersed with water. The specimens were stored at 22 ± 2°C. After the desired hydration times (1, 7, 14, 28, 84, 365 days) the hydration was stopped by immersing the prisms in isopropanol and drying them at 60°C for 12 hours. The dried prisms were then embedded in low viscosity epoxy resin and mechanically polished using diamond paste with a grain size down to 0.25 µm. Finally, the specimens were coatet with a thin layer of carbon to avoid charging. The images were acquired at 10 kV (7 days, smaller image), 12 kV (7 - 365 days) and 15 kV (1 days) using a CBS (concentric backscatter, 14-365 days) and a ABS (1 and 7 days) detector within a Thermofischer Helios G4 UX. Table 1: Basic information like resolution, size and phase composition of the images. <table> <tbody> <tr> <td>file</td> <td>age</td> <td>size</td> <td>size</td> <td>area</td> <td>pores</td> <td>hydrates</td> <td>clinker</td> </tr> <tr> <td> </td> <td>in days</td> <td>in px</td> <td>in µm</td> <td>in mm²</td> <td>area-%</td> <td>area-%</td> <td>area-%</td> </tr> <tr> <td>C3S 1d.tif</td> <td>1</td> <td>21179 x 21495</td> <td>749.9 x 749.9</td> <td>0.56</td> <td>38.5</td> <td>38</td> <td>23.9</td> </tr> <tr> <td>C3S 7d.tif</td> <td>7</td> <td>19433 x 19320</td> <td>390.9 x 390.9</td> <td>0.15</td> <td>32.2</td> <td>48.5</td> <td>19.4</td> </tr> <tr> <td>C3S 7d_2.tif</td> <td>7</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>28.5</td> <td>52.8</td> <td>19.1</td> </tr> <tr> <td>C3S 14d.tif</td> <td>14</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>22.2</td> <td>62.7</td> <td>15.4</td> </tr> <tr> <td>C3S 28d.tif</td> <td>28</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>16.9</td> <td>74.9</td> <td>8.3</td> </tr> <tr> <td>C3S 84d.tif</td> <td>84</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>21.7</td> <td>72.7</td> <td>5.7</td> </tr> <tr> <td>C3S 365d.tif</td> <td>365</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>14.8</td> <td>83.2</td> <td>2.0</td> </tr> </tbody> </table> The proportions of pores, hydrates and unhydrated clinker shown in Table 1 are the result of manual thresholding of denoised versions of these images and may therefore differ to own measurements. The scaling is backed into the file and can be read using ImageJ/Fiji. The unstitched files are provded as 7z archives. The sub-images were arranged in a 10 x 10 grid, with the exception of the 7 days image, which was arranged in a 9x9 grid. The pixel scaling of these files is the same as in the larger files. The unstitched files for the 1 day specimen can be provided on request. Internal note These files are included in the following MAPS datasets: <ul> <li>2019_04_15 FK C3S 1d</li> <li>2019_04_23 C3S 7d 15 BIB</li> <li>2023_05_24 C32-C2S 14-84 d</li> <li>2023_06_08 C2S-C3S 28d-1year</li> <li>2023_07_18 C3S 7d, C2S 1d, 7d, 3C3S-1C2S 7d</li> </ul> Changelog <ul> <li>2023-08-03, V1.1 Added new dataset (C3S 7d_2.tif).</li> <li>2024-02-07, V1.1 modified description (error in hydrate/C3S measurement for the 14 days dataset)</li> </ul>

opencc-by-4.0Aug 2023View details →

Monthly and Annual contour lines of the zero and the positive maximum of the Wind Stress Curl over Western North Atlantic during 1980-2019 and the Gulf Stream path during 1993-2019.

This dataset includes multiple fields: (i) files for monthly and annual fields for the max curl line and the zero curl line at 0.1 degree longitudinal resolutions; (ii) files for monthly and annual GS path obtained from Altimetry and originally processed by Andres (2016) at 0.1 degree longitudinal resolution. The maximum curl line (MCL) and the zero curl line (ZCL) calculations are briefly described here and are based on the original wind data (at 1.25 x 1.25 degree) provided by the Japanese reanalysis (JRA-55; Kobayashi et al., 2015) and available at https://zenodo.org/record/8200832 (Gifford et al. 2023). For details see Gifford, 2023.  The wind stress curl (WSC) fields used for the MCL and ZCL calculations extend from 80W to 45W and 30N to 45N at the 1.25 by 1.25-degree resolution.  The MCL is defined as the maximum WSC values greater than zero within the domain per 1.25 degree longitude. As such, it is a function of longitude and is not a constant WSC value unlike the zero contour. High wind stress curl values that occurred near the coast were not included within this calculation. After MCL at the 1.25 resolution was obtained the line was smoothed with a gaussian smoothing and interpolated on to a 0.1 longitudinal resolution. The smoothed MCL lines at 0.1 degree resolution are provided in separate files for monthly and annual averages (2 files). Similarly, 2 other files (monthly and annual) are provided for the ZCL.     Like the MCL, the ZCL is a line derived from 1.25 degree longitude throughout the domain under the condition that it's the line of zero WSC. The ZCL is constant at 0 and does not vary spatially like the MCL. If there are more than one location of zero curl for a given longitude the first location south of the MCL is selected. Similar to the MCL, the ZCL was smoothed with a gaussian smoothing and interpolated on to a 0.1 longitudinal resolution.    The above files span the years from 1980 through 2019. So, the monthly files have 480 months starting January 1980, and the annual files have 40 years of data. The files are organized with each row being a new time step and each column being a different longitude. Therefore, the monthly MCL and ZCL files are each 480 x 351 for the 0.1 resolution data. Similarly, the annual files are 40 x 351 for the 0.1 degree resolution data.   Note that the monthly MCLs and ZCLs are obtained from the monthly wind-stress curl fields. The annual MCLs and ZCLs are obtained from the annual wind-stress curl fields. Since the monthly curl fields preserves more atmospheric mesoscales than the annual curl fields, the 12-month average of the monthly MCLs and ZCLs will not match with the annual MCLs and ZCLs derived from the annual curl field.  The annual MCLs and ZCLs provided here are obtained from the annual curl fields and representative metrics of the wind forcing on an annual time-scale.  Furthermore, the monthly Gulf Stream axis path (25 cm isoheight from Altimeter, reprocessed by Andres (2016) technique) from 1993 through 2019 have been made available here. A total of 324 monthly paths of the Gulf Stream are tabulated. In addition, the annual GS paths for these 27 years (1993-2019) of altimetry era have been put together for ease of use. The monthly Gulf Stream paths have been resampled and reprocessed for uniqueness at every 0.1 degree longitude from 75W to 50W and smoothed with a 100 km (10 point) running average via matlab. The uniqueness has been achieved by using Consolidator algorithm (D’Errico, 2023).  Each monthly or annual GS path has 251 points between 75W to 50W at 0.1 degree resolution.

Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Yearly time-series (2000-2011)

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.General DescriptionThe monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:<ul><li>Monthly time-series: Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li>Yearly time-series: Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li>Long-term data (2000-2022): Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul>Data Details<ul><li>Time period: 2000–2011</li><li>Type of data: Water vapor column above the ground (0.001cm)</li><li>How the data was collected or derived: Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li>Statistical methods used: Four statistics were derived: standard deviation, percentiles 25, 50, and 75.</li><li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)</li><li>Spatial resolution: 1/120 d.d. = 0.008333333 (1km)</li><li>Image size: 43,200 x 17,924</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:<ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul>Name conventionTo ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:<ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20000101 = 2000-01-01</li><li>Time reference end time: 20111231 = 2011-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>

Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Monthly time-series (2012-2014)

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.General DescriptionThe monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:<ul><li>Monthly time-series: Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li>Yearly time-series: Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li>Long-term data (2000-2022): Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul>Data Details<ul><li>Time period: 2012–2014</li><li>Type of data: Water vapor column above the ground (0.001cm)</li><li>How the data was collected or derived: Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li>Statistical methods used: Four statistics were derived: mean, standard deviation, smoothed mean, smoothed standard deviation.</li><li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)</li><li>Spatial resolution: 1/120 d.d. = 0.008333333 (1km)</li><li>Image size: 43,200 x 17,924</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:<ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul>Name conventionTo ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:<ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20120101 = 2012-01-01</li><li>Time reference end time: 20141231 = 2014-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>

Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Yearly time-series (2012-2022)

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.General DescriptionThe monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:<ul><li>Monthly time-series: Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li>Yearly time-series: Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li>Long-term data (2000-2022): Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul>Data Details<ul><li>Time period: 2012–2022</li><li>Type of data: Water vapor column above the ground (0.001cm)</li><li>How the data was collected or derived: Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li>Statistical methods used: Four statistics were derived: standard deviation, percentiles 25, 50, and 75.</li><li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)</li><li>Spatial resolution: 1/120 d.d. = 0.008333333 (1km)</li><li>Image size: 43,200 x 17,924</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:<ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul>Name conventionTo ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:<ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20120101 = 2012-01-01</li><li>Time reference end time: 20221231 = 2022-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>

Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Long-term data (2000-2022)

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.General DescriptionThe monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:<ul><li>Monthly time-series: Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li>Yearly time-series: Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li>Long-term data (2000-2022): Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul>Data Details<ul><li>Time period: 2000–2022</li><li>Type of data: Water vapor column above the ground (0.001cm)</li><li>How the data was collected or derived: Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li>Statistical methods used: Four statistics were derived: standard deviation, percentiles 25, 50, and 75.</li><li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)</li><li>Spatial resolution: 1/120 d.d. = 0.008333333 (1km)</li><li>Image size: 43,200 x 17,924</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:<ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul>Name conventionTo ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:<ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20000101 = 2000-01-01</li><li>Time reference end time: 20221231 = 2022-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>

Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Monthly time-series (2018-2020)

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.General DescriptionThe monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:<ul><li>Monthly time-series: Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li>Yearly time-series: Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li>Long-term data (2000-2022): Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul>Data Details<ul><li>Time period: 2018–2020</li><li>Type of data: Water vapor column above the ground (0.001cm)</li><li>How the data was collected or derived: Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li>Statistical methods used: Four statistics were derived: mean, standard deviation, smoothed mean, smoothed standard deviation.</li><li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)</li><li>Spatial resolution: 1/120 d.d. = 0.008333333 (1km)</li><li>Image size: 43,200 x 17,924</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:<ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul>Name conventionTo ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:<ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20180101 = 2018-01-01</li><li>Time reference end time: 20201231 = 2020-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>

opencc-by-4.0Aug 2023View details →

Princeton Ethiopian, Eritrean, and Egyptian Miracles of Mary (PEMM) Project

The Princeton Ethiopian, Eritrean, and Egyptian Miracles of Mary digital humanities project (PEMM) is a comprehensive resource for the miracle stories about the Virgin Mary in Ethiopia, Eritrea, and Egypt, and preserved in Gəˁəz parchment manuscripts between 1300 and the present. Directed by Prof. Wendy Laura Belcher and then managed by Evgeniia Lambrinaki, PEMM was launched in March 2018, using as its base the miracle story identifications William F. Macomber made in the 1980s. Dataset. PEMM 2.0 includes the data collected by the project in Google Sheets from its inception to July 4, 2023. This date marked the end of our use of Google Sheets as our database and the end of Jeremy Brown's full-time involvement with the project (when he moved to be the cataloger of Ethiopic manuscripts at HMML). This data includes 1,002 identified stories (or 940 separate stories) (called Canonical Stories); 549 stories translated into English (288 stories translated by PEMM team; 223 stories translated and published by others) and another 200 stories summarized; 676 fully cataloged manuscripts (with another 334 identified, but awaiting digitization) (in Gəˁəz and a few in Arabic) (called Manuscripts); 51,690 stories documented in those manuscripts (called Story Instances); 21,403 typed Gəˁəz incipits (unique first lines) for those stories; and 2,547 paintings with 4,205 scenes in 262 manuscripts (called Paintings). The manuscripts come from 92 repositories and libraries around the world (called Collections) and the stories were composed in Ethiopia, Eritrea, and Egypt (and probably Nubia, although not confirmed), as well as Europe and the Levant (called Story Origins). Database. The PEMM Project began by using Google Sheets as a lightweight relational database. To learn about this innovative digital humanities approach by Princeton’s CDH’s, read the “Is a Spreadsheet a Database?” (February 21, 2021) article by PEMM lead developer, Rebecca Sutton Koeser. Due to our extremely large dataset (7 Google sheets in one workbook, each with at least 40 columns, and one with 50,000 rows, with dozens of complex formulas linking the fields in the various sheets), Google Sheets would repeatedly hang up. So, in July we migrated all our data to an Aurora PostgreSQL database, accessing it with a content management system called Directus. However, this Zenodo dataset represents the data as it last appeared in Google Sheets. Website. The current PEMM website (not yet its web application and data portal) is at https://pemm.princeton.edu. We will launch the full web application and data portal in mid-fall 2023. Team. PEMM was created in collaboration with Princeton’s Center for Digital Humanities (mainly with Rebecca Sutton Koesser, Jean Bauer, and Nicholas Budak, but with additional support from Gissoo Doroudian, Rebecca Munson [of beloved memory], and Kevin McElwee); directed by Prof. Wendy Laura Belcher; managed primarily by Evgeniia Lambrinaki into mid-2022 and then by Blaine Kebede; contributed to by catalogers Jeremy Brown, Mehari Worku, Dawit Muluneh, Solomon Gebreyes, Vitagrazia Pisani, Ekaterina Pukhovaia, and Steve Delamarter; web programmed by Henok Alem, who was assisted by Pak Hei Li, Ayomikun M. Gbadamosi, and Marew Masresha; edited by Taylor Eggan, assisted by Bret Windhauser; assisted by Hanni Makonnen for geolocating; typed by volunteers (including Mihret Melaku, Tariku Abas Sherif, Beimnet Beyene Kassaye, Annabel S. Lemma, Tsega-ab Hailemichael, Chiara Lombardi, and Ellen Perleberg); and translatated and/or summarized by Princeton undergraduates (including Lauren D. Johnson, Sana Khan, Jason O. Seavey, Leia R. Walker, Nati Arbelaez Solano, Daniel Somwaru, Mika J. Hyman, Grace Matthews, Allie V. Mangel, Ellen Li, Elliot Galvis). Support at Princeton is provided by Michael Franz and Amanda M. Arcamone. Partners. Among its board members are Elias Wondimu, Melaku Terefe, Solomon Gebreyes, Eyob Derillo, Meron Gebreananaye, Sofanit T. Abebe, Habte Michael Kidane, Hagos Abrha, Mussie Berhe, Woldesemait Teklehaymanot, and Alessandro Bausi. Among PEMM’s institutional partners are Beta Maṣāḥǝft: Manuscripts of Ethiopia and Eritrea at the Hiob Ludolf Centre for Ethiopian Studies of the Universität Hamburg, created and directed by Principal Investigator Alessandro Bausi; Hill Museum & Manuscript Library, led by Father Columba Stewart; and the British Library, Asian and African Collections, with Eyob Derillo as cataloger. Internal Funding. PEMM’s first and second phase were made possible by the Princeton Center for Digital Humanities, directed by Meredith Martin, and its team of Natalia Ermolaev, Rebecca Sutton Koeser, Gissoo Doroudian, Rebecca Munson (of beloved memory), Nick Budak, and Kevin McElwee. The second phase was supported by a CDH Research Partnership grant. The third phase was funded by the Princeton Humanities Council, executive directed by Kathleen Crown, through the David A. Gardner Innovation Grants for New Projects in the Humanities, and the University Committee on Research in the Humanities and Social Sciences. Other important funders throughout were the Princeton Department of African American Studies, directed by the Eddie S. Glaude, as well as the Program in Gender and Sexuality Studies (directed by Wallace Best), the Program in African Studies (directed by Emmanuel Kreike and now Chika Okeke-Agulu), the Center for the Study of Religion (directed by Jonathan Gold), and the Department of Comparative Literature (directed by Thomas Hare). External funding. PEMM’s fourth phase was made possible by two major grants from the National Endowment for the Humanities, awarded for work from fall 2021 through summer 2024. In the 1970s, NEH provided funding for the Ethiopian Manuscript Microfilm Library (EMML), which microfilmed thousands of manuscripts in Ethiopia, which serve as the backbone for the PEMM project. Today, the NEH Scholarly Editions and Scholarly Translations Grant funds the team of experienced researchers with rare language skills to catalog stories in parchment manuscripts, translate stories into English, and write short introductions to them. The NEH Digital Humanities Advancement Grant funds a public-facing open-access web application and data portal to share the stories in, images about, translations of, and scholarship on this crucial body of medieval African literature and to build upon our innovative prototype tool for searching in Gəˁəz.