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6,467 results for “Processing”
AR6 WG1 Plots and Processing
<p>Repository reproducing plots and processing used in AR6 WG1 made by Zebedee Nicholls, Malte Meinshausen and Jared Lewis.<br> <br> For questions and comments, please contact Zebedee Nicholls (zebedee.nicholls@climate-energy-college.org), Jared Lewis (jared.lewis@climate-resource.com) and Malte Meinshausen (malte.meinshausen@unimelb.edu.au). For full details, please see https://gitlab.com/magicc/ar6-wg1-plots-and-processing.</p>
Multiplexed fluorescence imaging based on cycles, raw and processed data.
<p>This dataset was created from a larger acquisition in order to provide an example of reasonnable size, as a companion data set to the F1000Research paper preprint DOIXXX.</p> <ul> <li>The original raw data including metadata files are included in <strong>Microscope_Output.zip.</strong></li> <li><strong>Experiment.json</strong> and<strong> channelnames.txt </strong>are the ones generated by the acquisition software. They are the only files needed when starting from one of the processed data set below.</li> <li>The deconvolution obtained with the commercial software Microvolution is also provided in <strong>bu_deconvolution.zip.</strong> To start from Step 1(Extended Depth of Field) instead of Step 0 (deconvolution), unzip this file in your output directory and rename the folder bu_deconvolution to out.</li> <li>The extended field of view 2D images created from step 0 to step 2, provided for convenince in <strong>edfonly.zip</strong></li> <li>The final files generated by trhe Multiplex processor, including the segmentation mask , are provided in<strong> finaloutput.zip</strong>. These files can be used in a specific analysis software.</li> </ul> <p> </p>
Annual Article Processing Charges (APCs) and number of gold and hybrid open access articles in Web of Science indexed journals published by Elsevier, Sage, Springer-Nature, Taylor & Francis and Wiley 2015-2018
<p><strong>Dataset of annual Article Processing Charges (APCs) for 6,252 journals from 2015 to 2018. </strong>The dataset contains annual APCs for journals indexed in the Web of Science (WoS) and published by the oligopoly of academic publishers (Elsevier, Sage, Springer-Nature, Taylor & Francis, Wiley). It also includes an estimate of the total APCs paid by the academic community based on the number of gold and hybrid articles published between 2015 and 2018. The dataset was created using publication data from WoS, OA status from Unpaywall and annual APC prices from open datasets (<a href="https://doi.org/10.5281/ZENODO.3841568">Matthias, 2020</a>; <a href="https://doi.org/10.5683/SP2/84PNSG">Morrison, 2021</a>) and historical fees retrieved via the Internet Archive Wayback Machine. </p> <p>Detailed methods and findings are reported in the following journal article</p> <p>Butler, L.-A., Matthias, L., Simard, M.-A., Mongeon, P., & Haustein, S. (2023). The Oligopoly's Shift to Open Access. How the Big Five Academic Publishers Profit from Article Processing Charges. <em>Quantitative Science Studies</em>. Preprint: <a href="https://doi.org/10.5281/zenodo.8322555">https://doi.org/10.5281/zenodo.8322555</a></p> <p><strong>Description of included files (v1):</strong></p> <p><em>APCs.csv: </em>contains the annual APCs for gold and hybrid OA journals indexed in Web of Science published by the oligopoly of academic publishers (Elsevier, Sage, Springer-Nature, Taylor & Francis, Wiley) between 2015 and 2018 including the total estimate of APCs paid per journal per year. It contains APC data for 18,846 journal-year-OA status combinations.</p> <p><em>countries.csv</em>: contains the fractionalized number of annual gold and hybrid OA articles by oligopoly publishers between 2015 and 2018 and the total estimate of fractionalized APCs paid per country per journal per year.</p> <p><em>oecd.csv</em>: contains the fractionalized number of annual gold and hybrid OA articles by oligopoly publishers between 2015 and 2018 and the total estimate of fractionalized APCs per discipline per journal per year.</p> <p><em>ReadMe.csv</em>: contains a description of the variables used in <em>APCs.csv</em>, <em>countries.csv</em> and <em>oecd.csv</em>.</p> <p> </p>
2001_2021_MODIS_Fourier Processed_1k_ER
<h4>Overview:</h4> <p>This is a set of images produced by Temporal Fourier Analysis (TFA) of MODIS data:</p> <p>NDVI: Normalised Difference Vegetation Index</p> <p>EVI: Enhanced Vegetation Index</p> <p>MIR: Middle Infra-Red</p> <p>DLST: Day-time Land Surface Temperature</p> <p>NLST: Night-time Land Surface Temperature</p> <p>The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.<br>This series of version 6 MODIS data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2001 to 2021. </p> <h4>Process:</h4> <p>Image values were extracted from MODIS imagery form 2001 to 2021. The day and night land temperature came from the 8 day MOD11A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the MOD13A2 16 day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (<a href="https://doi.org/10.1371/journal.pone.0001408">https://doi.org/10.1371/journal.pone.0001408</a>)<br>Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The E4warning study region was subset from global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.</p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <h4>File names:</h4> <p><br>The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 21 refers to the year timeline of 2001-2021.<br><br>The next two characters identify the channel:<br>03 - middle infra-red<br>07 - daytime land surface temperature<br>08 - nighttime land surface temperature<br>14 - NDVI: Normalised Difference Vegetation Index<br>15 - EVI: Enhanced Vegetation Index<br><br>The last two characters of each file name denote the output from Fourier processing:<br>a0 - mean<br>mn - minimum<br>mx - maximum<br>a1 - amplitude of annual cycle<br>a2 - amplitude of bi-annual cycle<br>a3 - amplitude of tri-annual cycle<br>p1 - phase of annual cycle<br>p2 - phase of bi-annual cycle<br>p3 - phase of tri-annual cycle<br>d1 - variance in annual cycle<br>d2 - variance in bi-annual cycle<br>d3 - variance in tri-annual cycle<br>da - combined variance in annual, bi-annual, and tri-annual cycles<br>vr - variance in raw data<br><br>Parameter Fourier Variable Image values are<br>MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000<br>LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50<br>NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000<br>NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000<br>NDVI (14) and EVI (15) VR Value * 10000<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p> <h4>Global Files can be accessed <a href="https://tinyurl.com/tfamodis0121">here</a>. </h4>
2016_2018_VIIRS_Fourier Processed_1k_ER
<h4>Overview:</h4> <p>This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data:</p> <p>NDVI: Normalised Difference Vegetation Index</p> <p>EVI: Enhanced Vegetation Index</p> <p>MIR: Middle Infra-Red</p> <p>DLST: Day-time Land Surface Temperature</p> <p>NLST: Night-time Land Surface Temperature</p> <p>The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.<br>This series of VIIRS data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2016 to 2018 and was produced to compare with next three years' time series.</p> <h4>Process:</h4> <p>Image values were extracted from VIRRS imagery from 2016 to 2018. The day and night land temperature came from the 8 day VNP21A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the VNP13A2 16-day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the time series's mean, minimum, and maximum, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (<a href="https://doi.org/10.1371/journal.pone.0001408">https://doi.org/10.1371/journal.pone.0001408</a>)<br>Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic. The E4warning study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.</p> <p>This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. </p> <p> </p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p><br><br><br><strong>File names:</strong></p> <p><br>The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 19 refers to the year timeline of 2016-2018.<br><br>The next two characters identify the channel:<br>03 - middle infra-red<br>07 - daytime land surface temperature<br>08 - nighttime land surface temperature<br>14 - NDVI: Normalised Difference Vegetation Index<br>15 - EVI: Enhanced Vegetation Index<br><br>The last two characters of each file name denote the output from Fourier processing:<br>a0 - mean<br>mn - minimum<br>mx - maximum<br>a1 - amplitude of annual cycle<br>a2 - amplitude of bi-annual cycle<br>a3 - amplitude of tri-annual cycle<br>p1 - phase of annual cycle<br>p2 - phase of bi-annual cycle<br>p3 - phase of tri-annual cycle<br>d1 - variance in annual cycle<br>d2 - variance in bi-annual cycle<br>d3 - variance in tri-annual cycle<br>da - combined variance in annual, bi-annual, and tri-annual cycles<br>vr - variance in raw data<br><br>Parameter Fourier Variable Image values are<br>MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000<br>LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50<br>NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000<br>NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000<br>NDVI (14) and EVI (15) VR Value * 10000<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p> <h4> </h4>
2020_2022_VIIRS_Fourier Processed_1k_ER
<h4>Overview:</h4> <p>This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data:</p> <p>NDVI: Normalised Difference Vegetation Index</p> <p>EVI: Enhanced Vegetation Index</p> <p>MIR: Middle Infra-Red</p> <p>DLST: Day-time Land Surface Temperature</p> <p>NLST: Night-time Land Surface Temperature</p> <p>The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.<br>This series of VIIRS data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2020 to 2022 and was produced to compare with previous three years' time series.</p> <h4>Process:</h4> <p>Image values were extracted from VIRRS imagery form 2020 to 2022. The day and night land temperature came from the 8 day VNP21A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the VNP13A2 16-day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the time series's mean, minimum, and maximum, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (<a href="https://doi.org/10.1371/journal.pone.0001408">https://doi.org/10.1371/journal.pone.0001408</a>)<br>Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic. The E4warning study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.</p> <p>This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. </p> <p> </p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p><br><br><br><strong>File names:</strong></p> <p><br>The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 19 refers to the year timeline of 2020</p> <p>-2022.<br><br>The next two characters identify the channel:<br>03 - middle infra-red<br>07 - daytime land surface temperature<br>08 - nighttime land surface temperature<br>14 - NDVI: Normalised Difference Vegetation Index<br>15 - EVI: Enhanced Vegetation Index<br><br>The last two characters of each file name denote the output from Fourier processing:<br>a0 - mean<br>mn - minimum<br>mx - maximum<br>a1 - amplitude of annual cycle<br>a2 - amplitude of bi-annual cycle<br>a3 - amplitude of tri-annual cycle<br>p1 - phase of annual cycle<br>p2 - phase of bi-annual cycle<br>p3 - phase of tri-annual cycle<br>d1 - variance in annual cycle<br>d2 - variance in bi-annual cycle<br>d3 - variance in tri-annual cycle<br>da - combined variance in annual, bi-annual, and tri-annual cycles<br>vr - variance in raw data<br><br>Parameter Fourier Variable Image values are<br>MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000<br>LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50<br>NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000<br>NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000<br>NDVI (14) and EVI (15) VR Value * 10000<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p> <h4>Files can be accessed here as well. </h4>
2012_2014_VIIRS_Fourier Processed_1k_ER
<h4>Overview:</h4> <p>This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data:</p> <p>NDVI: Normalised Difference Vegetation Index</p> <p>EVI: Enhanced Vegetation Index</p> <p>MIR: Middle Infra-Red</p> <p>DLST: Day-time Land Surface Temperature</p> <p>NLST: Night-time Land Surface Temperature</p> <p>The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.<br>This series of VIIRS data processed according to Scharlemann et al (2008), has been updated to include imagery from 2012 to 2014, and was produced to compare with next three years' time series,</p> <h4>Process:</h4> <p>Image values were extracted from VIRRS imagery from 2012 to 2014. The day and night land temperature came from the 8 day VNP21A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the VNP13A2 16-day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the time series's mean, minimum, and maximum, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (<a href="https://doi.org/10.1371/journal.pone.0001408">https://doi.org/10.1371/journal.pone.0001408</a>)<br>Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic. The E4warning study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.</p> <p>This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. </p> <p> </p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p><br><br><br><strong>File names:</strong></p> <p><br>The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 14 refers to the year timeline of 2012-2014.<br><br>The next two characters identify the channel:<br>03 - middle infra-red<br>07 - daytime land surface temperature<br>08 - nighttime land surface temperature<br>14 - NDVI: Normalised Difference Vegetation Index<br>15 - EVI: Enhanced Vegetation Index<br><br>The last two characters of each file name denote the output from Fourier processing:<br>a0 - mean<br>mn - minimum<br>mx - maximum<br>a1 - amplitude of annual cycle<br>a2 - amplitude of bi-annual cycle<br>a3 - amplitude of tri-annual cycle<br>p1 - phase of annual cycle<br>p2 - phase of bi-annual cycle<br>p3 - phase of tri-annual cycle<br>d1 - variance in annual cycle<br>d2 - variance in bi-annual cycle<br>d3 - variance in tri-annual cycle<br>da - combined variance in annual, bi-annual, and tri-annual cycles<br>vr - variance in raw data<br><br>Parameter Fourier Variable Image values are<br>MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000<br>LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50<br>NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000<br>NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000<br>NDVI (14) and EVI (15) VR Value * 10000<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p> <h4> </h4>
Process modeling, environmental and economic sustainability of the valorization of whey and eucalyptus residues for resveratrol biosynthesis
<p>Tables included in the article "Process modeling, environmental and economic sustainability of the valorization of whey and eucalyptus residues for resveratrol biosynthesis"</p>
Dataset of paper "Bioelectrochemically-improved anaerobic digestion of fishery processing industrial wastewater"
<p>Dataset of operation of a bioelectrochemically-improved anaerobic digester (AD-BES), treating real fishery processing wastewater.<br>This dataset was used to publish the paper "Bioelectrochemically-improved anaerobic digestion of fishery processing industrial wastewater" in Journal of Water Process Engineering (DOI: 10.1016/j.jwpe.2024.105848).</p>
EEG Data for: "Cortical oscillations and entrainment in speech processing during working memory load"
<p>This repository contains EEG and audio data used and described in:</p> <p><strong>Hjortkjær, J, Märcher-Rørsted, J, Fuglsang, SA, Dau, T (2018). Cortical oscillations and entrainment in speech processing during working memory load. European Journal of Neuroscience. </strong><strong>doi</strong><strong>:10.1111/ejn.13855</strong></p> <p>Please cite this article when using the data</p> <p> </p> <p>The MAT-files contain the aligned EEG and audio data for each subject (N=22). The envelopes of the speech audio (without noise) have been extracted as described in the paper. Each file (data_N.mat) contains a Matlab struct in the format of the Fieldtrip toolbox containing the following fields:</p> <p> </p> <p>data.trial: EEG and audio data for all 40 trials [channels x timepoints]</p> <ul> <li>channels 1-64: scalp EEG</li> <li>channel 65: left mastoid electrode</li> <li>channel 66: right mastoid electrode</li> <li>channel 67: horizontal EOG</li> <li>channel 68: vertical EOG for left eye</li> <li>channel 69: vertical EOG for right eye</li> <li>channel 70: audio envelopes</li> </ul> <p>data.trialinfo: Experimental condition in each trial</p> <ul> <li>1 = low noise, 1-back</li> <li>2 = low noise, 2-back</li> <li>3 = high noise, 1-back</li> <li>4 = high noise, 2-back</li> </ul> <p>data.time: Sample indices for each trial in seconds</p> <p>data.label: Name of each channel in data.trial</p> <p>data.fsample: EEG/audio sampling rate in Hz (128)</p>
Raw and processed hydro-meteorological variables of Jucar river basin for feature selection
<p>The dataset Processed data – input WQEISS.csv was employed for the input variable selection step in Zaniolo et al., 2018. It includes monthly values of 28 hydro-meteorological variables and indexes of Jucar river basin, Spain, for the period 1986-2000, namely:</p> <ul> <li>2 temporal features: day and month of the year;</li> <li>12 inputs to the Jucar State Index: average monthly storage and groundwater levels, average three months river runoff, and cumulated areal precipitation over 12 months;</li> <li>8 additional observed variables in the basin: three months average outflows from, and inflows to, the main reservoirs, and mean monthly areal temperatures;</li> <li>6 traditional drought indicators: Standardized Precipitation Index (SPI) and Standardized Precipitation and Evaporation Index (SPEI). SPI and SPEI indicators are computed on mean monthly data over the entire basin for 3, 6, and 12 months time aggregations.</li> </ul> <p>The last column of the dataset reports the target variable, i.e., the monthly nominal shortage of water conveyed to the irrigation districts simulated via AQUATOOL model. For further details on the dataset please consult Zaniolo et al., 2018, or the dedicated website <a href="http://www.nrm.deib.polimi.it/?page_id=2438">http://www.nrm.deib.polimi.it/?page_id=2438</a></p> <p>The unprocessed data used to compute indices and temporal cumulations in Processed data – input WQEISS.csv are reported in table Raw Data.csv. Public observations of rainfall, streamflows and storage levels come from the SAIH (Hydrological Automatic Information System) of the CHJ (Jucar Hydrological Confederation). Users can directly download data for the last 12 months on the dedicated webpage <a href="http://saih.chj.es/chj/saih/?f">http://saih.chj.es/chj/saih/?f</a> while previous data records are provided for free by CHJ upon request. Observations from piezometers are downloadable from the Piezometric Network Information section section of the CHJ <a href="https://www.chj.es/es-es/medioambiente/redescontrol/Paginas/Piezometr%C3%ADa.aspx">https://www.chj.es/es-es/medioambiente/redescontrol/Paginas/Piezometr%C3%ADa.aspx</a>.</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Wave 1 Questionnaires: Czech Republic
<p>This dataset was generated within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme - Grant Agreement No 649538. Work Package 7 of this project aims to test the processes in youth’s construction of active EU citizenship on various social and psychological levels. The main file contains quantitative data from the first wave of the longitudinal survey on adolescents and young adults (age 15-26). Data collection was carried out in the Czech Republic (regions Prague, South Moravian, Moravian-Silesian, Pardubicky, Vysocina) from October to December 2016. The supplementary files contain national translations of the questionnaire for the younger (15-19) and the older (20-26) subgroups.</p>
Processed features in support of Liebeskind et al (2018)
<p>Processed feature matrices used in Liebeskind et al. (2018). Supporting code: https://github.com/marcottelab/plum</p> <p>Datasets 1 - 4 correspond to those used in Figure 4:</p> <p>Dataset 1: No AP-MS, yeast CF-MS, training species: Human</p> <p>Dataset 2: AP-MS, yeast CF-MS, training species: Human</p> <p>Dataset 3: AP-MS, yeast CF-MS, training species: Human, Yeast</p> <p>Dataset 4: AP-MS, no yeast CF-MS, training species: Human, Yeast</p> <p>".train_labeled.missing_annotated.csv" files are those used for training the model and include only orthogroups for which interactions are known in the training species. These known interactions come either from gold-standard test sets, such as CORUM or EMBL's training portal, or from the fact that at least of the orthogroup pairs is missing in the focal taxon.</p> <p>".missing_annotated.csv" files were used for prediction, and include the entire feature matrices, plus known missing pairs. Note that there is no dataset 3 file. This is because data sets 2 and 3 differ only in the training species used, so dataset 3 predictions used dataset2_07302018.missing_annotated.csv as a feature matrix.</p> <p>dataset4_prediction_07302018.csv contains the predictions for all pairs on data set 4, the best performing data set that was used for all downstream analyses.</p>
Eco-evolutionary processes underlying early warning signals of population declines
<p>Datasets for the paper appearing in Journal of Animal ecology : "Eco-evolutionary processes underlying early warning signals of population declines". Also GitHub repository link :<a href="https://github.com/GauravKBaruah/ECO-EVO-EWS-DATA">https://github.com/GauravKBaruah/ECO-EVO-EWS-DATA</a></p>
Intermediate processing steps of quality-checking of Antarctic Circumnavigation Expedition (ACE) cruise track data.
<p><strong>Dataset abstract</strong></p> <p>The Antarctic Circumnavigation Expedition (ACE), undertaken in the austral summer of 2016/2017 recorded the cruise track using two independent geo-location instruments: one using GLobal NAvigation Satellite Systems (GLONASS; hereafter referred to as GLONASS) and another primarily using the Global Positioning System (GPS; hereafter referred to as the Trimble GPS). Daily log files were recorded in real-time from both instruments during the expedition and added to MySQL database tables. Following the expedition, quality-checking work has been undertaken to provide a one-second resolution set of positions for the cruise track. This dataset presents the intermediate files that were produced during the quality-checking, therefore it could be used to check the processing steps that have been undertaken, but should not be used as a final source of the cruise track data. Both the original raw data files and final quality-checked cruise track can be found in related datasets.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_INSTRUMENT_YYYY-MM-DD.csv – daily files for each instrument that were output from the database, data file, comma-separated values</li> <li>ace_INSTRUMENT_concatenated_YYYY-MM.csv – input files concatenated by month and instrument, data file, comma-separated values</li> <li>flagging_data_ace_INSTRUMENT_YYYY-MM-DD.csv – daily output files for each instrument with flagged data points, data file, comma-separated values</li> <li>track_data_combined_overall_flags_YYYY-MM.csv – instrument data combined with overall data flag for each month, data file, comma-separated values</li> <li>track_data_prioritised_YYYY-MM.csv – prioritised data files with overall data flag for each month, data file, comma-separated values</li> <li>ace_INSTRUMENT_manual_position_errors.csv – files containing the manually-observed errors, metadata, comma-separated values</li> <li>in_port.csv - dates on when the ship was stationary in port, metadata, comma-separated values</li> <li>README.txt – metadata, text file</li> <li>data_file_header.txt – metadata, text file</li> </ul> <p><strong>Dataset license</strong></p> <p>This dataset containing intermediate processing files of the ACE cruise track is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
sOCEL 2.0: A Sustainability-Enriched OCEL of a Hinge Production Process
<h2><strong>General Description</strong></h2> <p>The simulated process describes the production of hinges. It begins with a steel coil which is split into steel sheets which are heated, formed and coated before being split into the male and female parts. Subsequently, both components are assembled with a steel pin and packed. The process is describes using events and objects</p> <p>This is an artificial event log according to the <a href="https://www.ocel-standard.org/">OCEL 2.0 Standard</a> defined in Berti et al. simulated using an extended version of CPN-Tools with a connector to climatiq API. The extension to CPN-Tools and the event log simulation are results of the Bachelor Thesis project of Marco Heinisch at the Chair for Process and Data Science at RWTH Aachen University. </p> <h2><strong>Process Overview</strong></h2> <p>In this example scenario, hinges are produced in a German facility. The production process is divided into three workstations, all supervised by a single worker. The production line is started by this employee workstations for workstations every morning at 7:30. The employee pauses work for a lunch break at 12:00 pm and then continues working from 1:00 pm until 3:00 pm. The production process starts with two primary inputs: steel coils and steel pins. The steel pins, also known as steel rods, are each 3.0 cm long and are used to hold the leaves together. The steel coils hold a rolled steel strip that is approximately 0.3 centimeters thick and 3.0 centimeters wide. This steel strip is then processed into hinge leaves through the following steps.</p> <p>In the first operation of our model, one steel coil at a time is processed at the first workstation. Each coil is continuously cut into steel sheets that are 3 cm in length. As these strips are cut, we assign each resulting steel sheet an identification number. This process continues until there is only an unusable steel remainder of the steel coil, which we do not track further in this process. This remainder needs to be classified as waste in preprocessing. The amount of waste created during the production of rolled steel strips varies depending on the initial length and could be optimized. The cut steel sheets are handled in a line and heat-treated in a gas oven. A different oven technology could reduce impacts. Then, each 3.0 cm steel sheet is rolled on one side to form a barrel for the insertion of hinge pins. This operation is performed using a hydraulic press powered by electricity. In our model, we refer to these produced parts as FormedParts. Finally, in a very energy intense step, plasma coating is conducted using a specialized machine. The FormedParts are automatically collected in a transport carriage. If the carriage reaches full capacity, an alert signals the operator to manually move the batch of FormedParts to the second workstation.</p> <p>At the second workstation, <em>FormedParts </em>are shaped into alternating male and female hinge leaves (MalePart and FemalePart) using a laser cutting machine. This is achieved<br>by cutting out specific sections from each part. Notably, the method generates more waste compared to an alternative process where the male and female parts are directly cut out from the coil and then shaped further. The metal waste containing the coat is hypothetically not recyclable, which is reflected in a separate impact indicator. The waste created during this operation is not recorded, however, the weight of the workpiece object before and after the material removal is recorded. </p> <p>In the next step, the worker manually checks both FemalePart and MalePart for quality criteria, which is mass, representing various possible measurable attributes, including geometrics and material properties. If everything is within tolerance, the parts are placed on a moving band to the third workstation. If not, the part represents waste, which again is to be quantified in a preprocessing step. At the next station, an assembling machine uses one MalePart, one FemalePart, and one SteelPin to create a Hinge. The material origin of a hinge or its parts could potentially be discovered. A set of 10 hinges is automatically buffered and placed into a cardboard box by a packaging machine.</p> <h3><strong>Sustainability Data</strong></h3> <p>The event log is enhanced with sustainability-related attributes for objects and events. These attributes can be identified by their naming structure as they begin with either i, p or s and include a unit in square brackets at the end of the attribute name structure, i.e., "i_electricity[kWh]". These additional attribute are classifies into three different categories of data:</p> <ul> <li><strong>Impact Indicators</strong> ("i_"): contain information on all different impacts caused by an event, object, or process. Impact Indicators should be based on some sort of sustainability framework, such as <a href="https://link.springer.com/article/10.1007/s11367-016-1246-y">ReCiPe2016 standard</a>. In this event log, all relevant impact indicators considered in reviewed literature on manufacturing assessments are included according to the Climatiq-API specification to show the potential of automated assessment. Indicators further represent LCI-Items according to the LCA standard. However, to both indicate their relevance and a possible inability to get this data from the process directly some of the values read "??" as they have to be estimated using given impact paqrameters.</li> <li><strong>Impact Parameters </strong>("p_"): As data availability issues may lead to an inavailability to determine impact indicators directly, impact parameters can be included into the event log to support the estimation of impact indicators. These process-specific parameters include, e.g., material, mass, volume, geometric properties, and operation duration.</li> <li><strong>Impact Scores</strong> ("s_"): Impact scores describe the overall effect of a system or event on the environment in a standardised manner. Multiple impact scores for different impact categories, such as climate change or toxicity, can be calculated and represented as impact scores. This process involves aggregating and weighting individual impact indicators according to a standardized methodology, such as ReCiPe2016. Climatic API (used for the simulation of the event log) uses values for impact indicators to return impact scores. </li> </ul> <h2><strong>General Properties </strong></h2> <p>An overview of log properties is given below.</p> <table> <tbody> <tr> <th>Property</th> <th>Value</th> </tr> </tbody> <tbody> <tr> <td>Event Types</td> <td>11</td> </tr> <tr> <td>Object Types</td> <td>12</td> </tr> <tr> <td>Events</td> <td>~3850</td> </tr> <tr> <td>Objects</td> <td>~23700</td> </tr> </tbody> </table> <h2><strong>Process Simulation Design Process</strong></h2> <p>Our result is a simplified hinge production line, modeled as a Colored Petri Net (CPN) in <a href="https://cpntools.org/">CPN Tools</a>, a tool for editing and creating colored Petri nets. This model is an idealized representation of an exemplary manufacturing process. It is designed to examine and present the use case of SD in OCPM and does not realistically represent a specific manufacturing process in detail.</p> <p>The model’s design process was structured into four steps. First, the workpiece flow was outlined, identifying the sub-products used in hinge production.<br>Next, control flow elements were incorporated, adding batching and queuing behaviors as well as quality control procedures. Sustainability data relevant to this example were integrated into events and objects following the sOCEL specification. Finally, the simulation of values such as waiting times and weights now includes randomness and deviations.</p> <h2><strong><br>Simulation Model </strong></h2> <p>The repository with the CPN-Tools model as well as the CPN-Tools extension including the connector to Climatiq API can be found in the following Repositiry: <a href="https://github.com/rwth-pads/sOCEL">https://github.com/rwth-pads/sOCEL</a></p> <p>Further information on the simulated process, the simulation model and the CPN-Tools extension can be found in <a href="https://www.pads.rwth-aachen.de/go/id/bhsfqz" target="_blank" rel="noopener">Marco's thesis</a> entitled "Integrating Sustainability Data into Event Logs for Object-Centric Process Mining".</p> <h2><strong>Acknowledgements</strong></h2> <p>Funded under the Excellence Strategy of the Federal Government and the Länder<em>. </em>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC-2023 Internet of Production - 390621612. We also thank the Alexander von Humboldt (AvH) Stiftung for supporting our research.</p> <p>Special thanks are also dedicated to Marco's Family and friends Lennart, Franziska, and Maxim for their support and valuable feedback.</p>
Processed GBM DEFND-seq Data
<p>This repository contains the fragments files for the gDNA component of two DEFND-seq libraries from each of two GBM patients. The correspond scRNA-seq data and additional metadata can be found on the Gene Expression Omnibus (GEO) under accession GSE224149. The two samples correspond to GEO entries GSM7817780 and GSM7016419.</p>
pKaDatabase for Stacking Gaussian Processes to Improve pKa Predictions in the SAMPL7 Challenge
<p>A curated a database of small molecules with experimentally measured pKa values. </p> <p>This pickle file can be loaded into memory using Pandas. In the code block below we will print out the columns of the DataFrame:</p> <pre><code class="language-python">import pandas as pd df = pd.load("pKaDatabase.pkl") print(df.keys()). # print the columns</code></pre> <blockquote> <p>['deprotonated microstate ID', 'protonated microstate ID', 'deprotonated microstate smiles', 'protonated microstate smiles', 'AM1BCC partial charge (prot. atom)', 'AM1BCC partial charge (deprot. atom)', 'AM1BCC partial charge (prot. atoms 1 bond away)', 'AM1BCC partial charge (deprot. atoms 1 bond away)', 'AM1BCC partial charge (prot. atoms 2 bond away)', 'AM1BCC partial charge (deprot. atoms 2 bond away)', 'Gasteiger partial charge (prot. atom)', 'Gasteiger partial charge (deprot. atom)', 'Gasteiger partial charge (prot. atoms 1 bond away)', 'Gasteiger partial charge (deprot. atoms 1 bond away)', 'Gasteiger partial charge (prot. atoms 2 bond away)', 'Gasteiger partial charge (deprot. atoms 2 bond away)', 'Extented Hückel partial charge (prot. atom)', 'Extented Hückel partial charge (deprot. atom)', 'Extented Hückel partial charge (prot. atoms 1 bond away)', 'Extented Hückel partial charge (deprot. atoms 1 bond away)', 'Extented Hückel partial charge (prot. atoms 2 bond away)', 'Extented Hückel partial charge (deprot. atoms 2 bond away)', '∆G_solv (kJ/mol) (prot-deprot)', 'SASA (Shrake)', 'SASA (Lee)', 'Bond Order', 'Change in Enthalpy (kJ/mol) (prot-deprot)', 'pKa','href', 'num ionizable groups', 'Weight', 'pKa source']</p> </blockquote> <p> </p> <p>For more information regarding feature calculations, please read the following paper:</p> <blockquote> <p>Raddi, Robert, and Vincent Voelz. "Stacking Gaussian Processes to Improve pKa Predictions in the SAMPL7 Challenge." (2021). <a href="https://doi.org/10.26434/chemrxiv.14650302.v1">10.26434/chemrxiv.14650302.v1</a></p> </blockquote>
Arabidopsis thaliana circadian mRNA-seq gene expression processed tables from Romanowski et al., TPJ 2020.
<p>This dataset is an add-on for Romanowski et al., TPJ 2020 (https://doi.org/10.1111/tpj.14776) containing processed files for the circadian RNAseq data in tab delimited txt format.</p> <p><br> Here, you can the raw counts file, the normalized CPM values, and the full JTK result (without recalculated circadian phases, just the original ones). All genes with a read density > 0.05 in at least one timepoint were considered expressed. The read density is calculated as the amount of reads divided by the effective length of a gene (total reads / length). Genes rd file is also included.</p> <p>Some useful notes:<br> 1) Counts were assigned using ASpli and the AtRTDv2 annotation (34,212 genes).<br> 2) After filtering by rd we had a total of 18,503 expressed genes.<br> 3) 13,256 genes passed the QL F-tests.<br> 4) 9,127 genes were rhythmic according to JTK_cycle. </p> <p>For detailed protocols, please see Romanowski et al., TPJ 2020 (https://doi.org/10.1111/tpj.14776)</p> <p>The RNA-seq raw data supporting the conclusions of this article have been deposited in ArrayExpress (Kolesnikov et al., 2015) at EMBL-EBI (www.ebi.ac.uk/arrayexpress), under accession numbers E-MTAB-7933.</p> <p>All relevant custom r scripts are available at https://github.com/aromanowski/Circadian_rhythms_and_alternative_splicing</p>
Floating Car Data Collection for Processing and Benchmarking
<p>The dataset is outcome of a paper "Floating Car Data Map-matching Utilizing the Dijkstra Algorithm" accepted for 3rd International Conference on Data Management, Analytics & Innovation held in Kuala Lumpur, Malaysia in 2019.</p> <p>The floating car data (FCD representing movement of cars with their position in time) is produced by the traffic simulator software (further referred to as Simulator) published in [1] and can be used as an input for data processing and benchmarking. The dataset contains FCD of various quality levels based on the routing graph of the Czech Republic derived from Open Street Map <a href="https://www.openstreetmap.org">openstreetmap.org</a>.<br> <br> Should the dataset be exploited in scientific or other way, any acknowledgement or references to our paper [1] and dataset are welcomed and highly appreciated.</p> <p><strong>Archive contents</strong></p> <p>The archive contains following folders.</p> <p><strong>city_oneway</strong> and <strong>city_roadtrip </strong>- FCD from the city of Brno, Czech Republic where FCD is based on Origin-Destination in case of oneway and Origin-Destination-Origin in case of a road trip</p> <p><strong>intercity_oneway </strong>and <strong>intercity_roadtrip </strong>- FCD from cities of Brno, Ostrava, Olomouc and Zlin, all Czech Republic where FCD is based on Origin-Destination in case of oneway and Origin-Destination-Origin in case of a road trip</p> <p><strong>Content explanation</strong></p> <p>All four of mentioned folders contain raw FCD as they come from our Simulator, post-processed FCD enriching Simulator FCD, and obfuscated raw FCD (of both low and high obfuscation level). In the both obfuscated data sets, each measured point was moved in a random direction a number of meters given by drawing a number from a Gaussian distribution. We utilized two Gaussian distributions, one for the roads outside the city (N(0,10) for the lower and N(0,20) for the higher obfuscation level) and one for the roads inside the city (N(0,15) and N(0,30) respectively). Then some predefined number of randomly chosen points were removed (3% in our case). This approach should roughly represent real conditions encountered by FCD data as described by El Abbous and Samanta [2].</p> <p>In case of post-processed road trip data, there is one extra dataset with "cache" suffix representing the very same dataset limited to a 5-minute session memoization. This folder also contains a picture of processed FCD represented on a map.</p> <p><strong>Data format</strong><br> Standard UTF-8 encoded CSV files, separated by a semicolon with the following columns:</p> <p><strong>RAW</strong></p> <p><em>Header</em></p> <p>session_id;timestamp;lat;lon;speed;bearing;segment_id</p> <p><em>Data</em></p> <p>session_id: (Type: unsigned INT) - session (car) identifier<br> timestamp: (Type: datetime) - timestamp in UTC<br> lat: (Type: unsigned long) - latitude as used in Google maps<br> lon: (Type: unsigned long) - longitude as used in Google maps<br> speed: (Type: unsigned INT) - actual speed in kmh<br> bearing: (Type: unsigned INT) - actual bearing in angles 0-360<br> segment_id: (Type: unsigned long) - unique edge identifier</p> <p><strong>POST-PROCESSED</strong></p> <p><em>Header</em></p> <p><br> gid;car_id;point_time;lat;lon;segment_id;speed_kmh;speed_avg_kmh;distance_delta_m;distance_total_m;speedup_ratio;duration;segment_changed;duration_segment;moved;duration_move;good;duration_good;bearing;interpolated</p> <p><em>Data</em></p> <p>gid: (Type: unsigned long) - global identifier of a record<br> car_id: (Type: unsigned INT) - session (car) identifier<br> point_time: (Type: datetime) - timestamp with timezone<br> lat: (Type: unsigned long) - latitude as used in Google maps<br> lon: (Type: unsigned long) - longitude as used in Google maps<br> segment_id: (Type: unsigned long) - unique edge identifier<br> speed: (Type: unsigned INT) - actual speed in kmh<br> speed_avg_kmh: (Type: unsigned long) - actual average speed of a car in kmh<br> distance_delta_m: (Type: unsigned long) - actual distance delta in metres<br> distance_total_m: (Type: unsigned long) - actual total distance of a car in metres<br> speedup_ratio: (Type: unsigned long) - actual speed-up ratio of a car<br> duration: (Type: time) - actual duration of a car<br> segment_changed: (Type: boolean) - signals if actual segment of a car differs from the previous one<br> duration_segment: (Type: time) - actual duration on a segment of a car<br> moved: (Type: boolean) - signals if actual position of a car differs from the previous one<br> duration_move:(Type: time) - actual duration of a car since moving<br> good: signals if actual record values satisfies all data constraints (all true as derived from Simulator)<br> duration_good: actual duration of a car since when all constraints conditions satisfied<br> bearing: (Type: unsigned INT) - actual bearing in angles 0-360<br> interpolated: (Type: boolean) - signals if actual segment identifier is calculated (all false as derived from Simulator)</p> <p><strong>References</strong><br> <br> [1] <em>V. Ptošek, J. Ševčík, J. Martinovič, K. Slaninová, L. Rapant, and R. Cmar, </em><em>Real-time</em><em> traffic simulator for self-adaptive navigation system validation, Proceedings of EMSS-HMS: Modeling & </em><em>Simulation</em><em> in Logistics, Traffic & Transportation, 2018.</em></p> <p>[2] <em>A. El </em><em>Abbous</em><em> and N. Samanta. A </em><em>modeling</em><em> of GPS error </em><em>distri-butions</em><em>, In proceedings of 2017 European Navigation Conference (ENC), 2017.</em></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.