Find research datasets worth reusing

opencc-by-4.0Oct 2022View details →

Contaminations on Lidar Sensor Covers: Performance Degradation including Fault Detection and Modeling as Potential Applications

Data description of contamination measurements with lidar sensors RIEGL LD05-A20 and Ouster OS1-64 Photos of the measurement setup We provide photos of the measurement setup and the contaminations applied in the folder "/photos".   Riegl LD05-A20 data The data of the Riegl LD05-A20 can be found in the folder "riegl_LD05-A20_full_waveforms" - one file per experiment. An example notebook for reading the data is provided in "/notebooks/example_riegl_LD05-A20.ipynb". Note that the files contain only the prominent peaks of the full waveform calculated by the V08Wave software provided by RIEGL. If you are interested in the entire full waveforms, please contact the authors.   Ouster OS1-64 The data of the Ouster OS1-64 can be found in the folder "ouster_OS1-64_point_clouds" - one folder per experiment. An example notebook for reading the data is provided in "/notebooks/example_ouster_OS1-64.ipynb". The python package pointcloudset (https://github.com/virtual-vehicle/pointcloudset) and its documentation is suggested for further data analytics of the point cloud data.

opencc-by-4.0Jul 2022View details →

Sequencing summaries from pfhrp2 detection and characterization using the MinION sequencer

This dataset comprises sequencing summaries generated by the MinKnow software during sequencing runs related to the project "Portable and cost-effective genetic detection and characterization of Plasmodium falciparum hrp2 using the MinION sequencer."  This dataset was used in the generation of figures for the manuscript related to this project, and can be used with the Jupyter Notebook for figure generation provided in the project's <a href="https://github.com/sjsabin/minion_hrp2_project">github</a>.

opencc-by-4.0Dec 2021View details →

Dataset for the study "Can Anthipatella wollastoni be detected in Multibeam Echo Sounder multi-detect data?"

Dataset for the study "Can Anthipatella wollastoni be detected in Multibeam Echo Sounder multi-detect data?", currently under review at Frontiers in Remote Sensing.   The files include: Photos.zip: GoPro Photos with ground truthing of two ridges with the occurrence of Black Corals. Photos have been geo-located using the coordinates of the onboard Multibeam System and correcting a time offset. Refer to the paper for details. Sound velocities: Sound velocity casts using a Base-X shallow water profiler used to correct the multibeam echo sounder. MD_manual_edit: Shape file including the position of Multi-Detects after the manual cleaning. The MD point objects have been joined with information from the local bathymetry and slope. Note that associated intensity values are erroneous due to a bug in the recording MBES firmware. MBES: Includes Norbit s7k (version 3) raw files of the multibeam echo sounder data (Norbit iwbms-e, Serial number #12). Files in folders with "MD" were used for the Multi-Detect study. Files with "MFE" include multifrequency data. We failed to locate the black corals in multi-frequency backscatter data. Multi-detect data were recorded with a swath width of 100° split into 512 beams. The multifrequency data contains 190 and 370 kHz information. Spreading correction was set to 40, absorption set to 107 dB/km (calculated for the 390 kHz frequency  with a mean water temperature of 16°C, a salinity of 35. Actual temperature according to diver information was 24°C at the surface, 23° until 50 m and 21° until 80 m). All offsets have been accounted for during the survey. The data is located using an RTK correction (refer to paper for further details).

opencc-by-4.0Jul 2022View details →

Flood Detection Using GRACE Terrestrial Water Storage and Extreme Precipitation

The supplementary figures contain 2380 precipitation-type flood events recorded by the DFO compared with  results derived based on GRACE and MODIS. "DFO-GRACE-MODIS" folder means comparison among flood derived from DFO, GRACE and MODIS while "DFO-GRACE" folder means comparison between DFO and GRACE due to lack of MODIS-derived flood inundation. As for detailed information, please refer to the corresponding paper.

opencc-by-4.0Jul 2022View details →

Machine learning methods detect arm movement impairments in a patient with parieto-occipital lesion using only early kinematic information.

This depository contains the data and codes of the paper:  Bosco, A., Bertini C., Filippini M., Foglino C., Fattori P (2022). Machine learning methods detect arm movement impairments in a patient with parieto-occipital lesion using only early kinematic information. Journal of Vision, in press.  Data are provided in mat-file, codes is provided in m-files (Matlab).

opencc-zeroJul 2022View details →

Data from: Signal detection shapes ornament allometry in functionally convergent Caribbean Anolis and Southeast Asian Draco lizards

Visual ornaments have long been assumed to evolve hyper-allometry as an outcome of sexual selection. Yet growing evidence suggests many sexually-selected morphologies can exhibit other scaling patterns with body size, including hypo-allometry. The large conspicuous throat fan, or dewlap, of arboreal Caribbean Anolis lizards was one ornament previously thought to conform to the classical expectation of hyper-allometry. We re-evaluated this classic example alongside a second arboreal group of lizards that has also independently evolved a functionally equivalent dewlap, the Southeast Asian Draco lizards. Across multiple closely-related species in both genera, the Anolis and Draco dewlaps were either isometric or had hypo-allometric scaling patterns. In the case of the Anolis dewlap, variation in dewlap allometry was predicted by the distance of conspecifics and the light environment in which the dewlap was typically viewed. Signal efficacy therefore appears to have driven the evolution of hypo-allometry in what was originally thought to be a sexually-selected ornament with hyper-allometry. Our findings suggest that other elaborate morphological structures used in social communication might similarly exhibit isometric or hypo-allometric scaling patterns because of environmental constraints on signal detection.

openmit-licenseJul 2022View details →

Early Detection of Nucleation Events from Solution in LC-TEM by Machine Learning

These data are images taken with a liquid cell transmission electron microscope and annotation data of particles. See <a href="https://github.com/hiroyasukatsuno/Early-Detection-of-Nucleation-Events-LC-TEM">this page (GitHub)</a>.

Research data supporting "Nanozyme-catalysed CRISPR assay for preamplification-free detection of non-coding RNAs"

Raw research data supporting: Broto, M., Kaminski, M.M., Adrianus, C. et al. Nanozyme-catalysed CRISPR assay for preamplification-free detection of non-coding RNAs. Nat. Nanotechnol. (2022). https://doi.org/10.1038/s41565-022-01179-0

opencc-by-4.0Apr 2022View details →

Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation

SNV of 23 parental barley inbreds of the double round robin population (DRR) (<a href="https://doi.org/10.1111/pbi.13746">https://doi.org/10.1111/pbi.13746</a>) used in the publication "Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation". SV, INDELs, and additional data are available via figshare (https://doi.org/10.6084/m9.figshare.16802473).

Dataset for publication 'Automated computed tomography based parasitoid detection in mason bee rearings'

Dataset corresponding to the publication Automated computed tomography based parasitoid detection in mason bee rearings published at PLOS One. This repository contains the training, validation and test data.

In Search of the Edge: A Bayesian Exploration of the Detectability of Red Edges in Exoplanet Reflection Spectra

This repository contains surface albedos for a paper submitted to AAS journals under the same title. Here we include a a realistic Earth-like surface albedo and the raw albedo files used for it's calculation.

Detecting and reducing heterogeneity of error in acoustic classification: Data

<ol> <li>Passive acoustic monitoring can be an effective method for monitoring species, allowing the assembly of large audio datasets, removing logistical constraints in data collection, and reducing anthropogenic monitoring disturbances. However, the analysis of large acoustic datasets is challenging, and fully automated machine-learning processes are rarely developed or implemented in ecological field studies. One of the greatest uncertainties hindering the development of these methods is spatial generalisability – can an algorithm trained on data from one place be used elsewhere?</li> <li>We demonstrate that heterogeneity of error across space is a problem that could go undetected using common classification accuracy metrics. Secondly, we develop a method to assess the extent of heterogeneity of error in a random forest classification model for six Amazonian bird species. Finally, we propose two complementary ways to reduce heterogeneity of error, by (i) accounting for it in the thresholding process and (ii) using a secondary classifier that uses contextual data.</li> <li>We found that using a thresholding approach that accounted for heterogeneity of precision error reduced the coefficient of variation of the precision score from a mean of 0.61±0.17 (SD) to 0.41±0.25 in comparison to the initial classification with threshold selection based on F-score. The use of a secondary, contextual classification with thresholding selection accounting for heterogeneity of precision reduced it further still, to 0.16±0.13, and was significantly lower than the initial classification in all but one species. Mean average precision scores increased, from 0.66±0.4 for the initial classification, to 0.95±0.19, a significant improvement for all species.</li> <li>We recommend assessing - and if necessary correcting for - heterogeneity of precision error when using automated classification on acoustic data to quantify species presence as a function of an environmental, spatial or temporal predictor variable.</li> </ol>

Data for: Development of a method for the measurement of human scent samples using comprehensive two-dimensional gas chromatography with mass detection

This dataset was used for development of a method for the measurement of human scent samples using comprehensive two-dimensional gas chromatography with mass detection [<a href="https://doi.org/10.1016/j.forsciint.2016.09.011">https://doi.org/10.1016/j.forsciint.2016.09.011</a>]. The dataset contains chromatograms of a model mixture of human scent and chromatograms of the human scent of one volunteer measured on different column setups. Each sample was processed in ChromaToF(version 4.72.0.0) by LECO corp. The processing step was executed at the signal-to-noise (SN) ratio levels 100, 300, and 500 (human scent samples chromatograms).

Datasets for "A Detection of the Sea Level Fingerprint of Greenland Ice Sheet Melt" Coulson et al., 2022, Science

Sea surface height (SSH) change altimetry-derived datasets and model predictions for: "A Detection of the Sea Level Fingerprint of Greenland Ice Sheet Melt", Submitted to Science January 2022. Sophie Coulson, Sönke Dangendorf, Jerry X. Mitrovica, Mark E. Tamisiea, Linda Pan, David T. Sandwell. Email: slcoulson@lanl.gov, sdangendorf1@tulane.edu See README for file details.

Data: Detecting preservation and reintroduction sites for endangered plant species using a two-step modelling and field approach

To withstand the surge of species loss worldwide, (re)introduction of endangered plant species has become an increasingly common technique in conservation biology. Successful (re)introduction plans, however, require identifying sites that provide the optimal ecological conditions for the target species to thrive. In this study, we propose a two-step approach to identify appropriate (re)introduction sites. The first step involves modelling the niche and distribution of the species with bioclimatic and topographical predictors, both at continental and at national scales. The second step consists of refining these bioclimatic predictions by analysing stationary ecological parameters, such as soil conditions, and relating them to population-level fitness values. We demonstrate this methodology using Swiss populations of the lady's slipper orchid (Cypripedium calceolus L., Orchidaceae), for which conservation plans have existed for years but have generally been unfruitful. Our workflow identified sites for future (re)introductions based on the species requirements for mid-to-sunny light conditions and specific soil physico-chemical properties, such as basic to neutral pH and low soil organic matter content. Our findings show that by combining wide-scale bioclimatic modelling with fine scale field measurements it is possible to carefully identify the ecological requirements of a target species for successful (re)introductions.

Increasing efficiency and reducing bias in the detection of seed-dispersal interactions based on mist-netted birds

Efficient and unbiased sampling of ecological interactions is essential to our understanding of the functions they mediate. Seed dispersal by frugivorous birds is a key mutualism for plant regeneration and community dynamics. Mist-netting is one of the most widely used methods to sample avian seed dispersal through the identification of seeds in droppings of captured birds kept inside cloth bags. However, birds may drop seeds on the ground before being extracted from the net, leading to a fraction of missing information due to ineffective sampling. Worryingly, this fraction could be unevenly distributed across bird and plant species, leading to sampling biases. Here, we assess the effectiveness of using a 1- m wide mesh below mist nets to sample seeds dropped by entangled birds. We used data from birds mist-netted during one-year-round. We sampled nearly 50% of interaction events and 75% of dispersed seeds on the mesh band below the mist nets (i.e. lost information without this optimization). The proportion of seeds sampled on the mesh bands was not evenly distributed among bird species but strongly related to bird size, ranging from 57-63% in warblers to 84-94% in thrushes. Moreover, the proportion of seeds sampled on the mesh was negatively related to seed size, although this relationship was weaker. We also evaluated accumulation curves of species and pairwise interactions with increasing sampling effort, both with and without using the mesh bands. The number of seed species sampled increased by 21% when using the mesh bands and the number of pairwise interactions by 36%. Our findings provide strong evidence on how inefficient and biased traditional mist-netting can be for sampling community-wide seed-dispersal interactions. We thus urge the use of mesh bands in future studies to increase sampling effectiveness and avoid biases, which will ultimately improve our understanding of the seed dispersal function.

Discrete fire events, their severity, and their ignitions, as derived from MODIS MCD 14ML active-fire detection data for Indonesia, 2002-2019

1. PUBLICATION CORRESPONDING TO THESE DATA Sloan, Sean*; Locatelli, Bruno; Andela, Niels; Cattau, Megan E.; Gaveau, David; Tacconi, Luca. 2022 'Declining Severe Fire Activity on Managed Lands in Equatorial Asia'. Communications Earth & Environment. DOI: 10.1038/s43247-022-00522-6. *Corresponding author email: sean.sloan@viu.ca 2. ABSTRACT OF THE DATA The GIS data and corresponding attribute data described here pertain to discrete fire events, their severity, and their ignitions, as derived on the basis of daily MODIS Collection 6 MCD14ML active-fire detections (AFDs). Data on fire events and their ignitions are provided separately, as two data files. These data files on fire events and ignitions may however be linked to each other by the data user. Fire-event severity is quantified per fire event and reported in the data file for fire events. A fire event is a cluster of MODIS Collection 6 MCD14ML active-fire detections (AFDs) wherein each AFD has a spatial (<=1-km) and temporal (<=4-day) proximity to another AFD in the same fire event, inferring thus a relational co-occurrence amongst AFDs in time and space. In other words, a fire event is considered a likely occasion of burning wherein all constituent AFDs are related to each other in time and space, either directly (as for proximate AFDs) or indirectly (as in the case of a large area of fire activity that spread progressively over time and space from an initial source). Each fire event has a designated ignition AFD, being the AFD of the fire event with the earliest detection date. A given fire event can have more than one ignition AFD if the ignitions all share same earliest detection date. The ignition AFD(s) is the nominal initial source of the burning described by the corresponding fire event. All other, non-ignition AFDs of a fire event are deemed its 'propagation' AFDs, since these AFDs follow from the ignitions, temporally and spatially. See Figure 4 in the publication by Sloan et al. for an illustration of the geography of fire events and their ignition AFDs. Fire events and their ignitions were derived from standard science-quality MODIS Collection 6 MCD 14ML AFD data, commonly referred to as fire 'hotspot' data. Data were detected by both the Terra and Aqua satellite sensors daily for Indonesia between July 2002 and December 2019. Information on these input data are provide by the two citations below. The publication of Sloan et al. provides methodological details on how the MODIS Collection 6 MCD 14ML AFD data were processed into discrete fire events and ignitions. EarthData. MODIS Collection 6 Active-Fire Detections standard scientific data (MCD14ML), NASA EarthData, https://earthdata.nasa.gov/firms (2019). Giglio, L., Schroeder, W. & Justice, C. O. The Collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment 178, 31-41, (2016). 3. DATA FILES Two data files are distributed here – one for discrete fire events, and another for the ignition AFDs of each fire event. The data files are provided in a GIS-compatible format, and also as a generic text format, as described below. 3.1 GIS VERSION Data files in GIS-compatible format are provided as 'feature classes' within an ArcGIS file geodatabase 'Sloan_MODIS_FireEvents_Ignitions_2002_2019.gdb'. These data files can be viewed and manipulated using either ArcGIS Desktop or ArcGIS Pro software. There is one feature class for fire events, and another file for ignitions. Sloan_MODIS_FireEvents_Ignitions_2002_2019.gdb\nfire4_all_spatial_fire_2002_2019_joins_sp_LC This file pertains to fire events. All AFDs of a given fire event are included, without differentiation as to whether the AFDs are ignition AFDs or other (propagation) AFDs. Fire events are assigned unique ID values and basic attribute data. Sloan_MODIS_FireEvents_Ignitions_2002_2019.gdb\nfire4_all_spatial_fire_2002_2019_igs_sp_LC This file pertains to ignitions. Only ignition AFDs are included for a given fire event. Fire events corresponding to the ignitions are assigned unique ID values and basic attribute data. 3.2 CSV TEXT VERSION Both data files are also supplied as comma-separated value (CSV) text files for viewing and manipulation in non-GIS software, such as Excel, text editors, or any statistical software. The text files can also be read into various GIS software. CSV-formatted files have the same file name and attribute fields as the corresponding GIS-formatted data files. These CSV-formatted data files (as well as the GIS-formatted data files) include attribute data on the latitude and the longitude of each AFD. Attribute field names are included as the first row of values in a CSV file. No 'text qualifiers' like quotations (" ") or inverted commas ('') are used to designate text/string values within the CSV file. Text values appear directly between commas in the CSV data file, e.g., …,Kalimantan_Southern,… . Note two points of caution for working with these CSV data: i) Microsoft Excel may be used for a partial view of the data file nfire4_all_spatial_fire_2002_2019_joins_sp_LC.csv, but it is not recommended for working with this data file. This is because the number of records/rows in this csv file slightly exceeds that maximum that may be read by Excel, which is just over 1 million. This limitation does not apply to the other csv file, however. ii) The GIS-formatted data files employ 'null values' in their attribute tables, and so the corresponding 'values' in the CSV-formatted data files are similarly null. For null values, no value whatsoever is ascribed, not even 0. In the syntax of a CSV file (apparent upon opening the file in any text editor like Microsoft Notepad), a null value is denoted by two consecutive commas without any value, text, or space between them. If a CSV file were opened in Excel, a cell assigned a null value would be blank, not 0 or otherwise. This denotes the correct transcription of the GIS-formatted data. This feature will not impede the correct reading of these CSV data by whatever software. Users are made aware of this feature merely to ensure the proper input of these data into whatever software. 4. DATA STRUCTURE / GEOGRAPHY The GIS-formatted data files are 'point data', i.e., they map the geography of AFDs as individual 'points', in keeping with how these MODIS MCD14ML AFD data were originally structured. For the GIS-formatted data files, each record/row in its corresponding attribute tables corresponds geographically to single AFD 'point', regardless of whether that AFD belongs to a fire event comprised of many AFDs. In the parlance of GIS files, the files depict 'single-part' point features. The unique ID field [nfireID2] serves to denote the fire event to which a given AFD belongs. Similarly, for the CSV-formatted data files, each record/row of values corresponds to a single AFD. There are 1,232,377 records for the data file 'nfire4_all_spatial_fire_2002_2019_joins_sp_LC'. There are 720795 records for the data file 'nfire4_all_spatial_fire_2002_2019_igs_sp_LC'. 5. ATTRIBUTE FIELDS In the data files, while some attribute fields pertain to the individual AFD as the unit of observation (e.g., the land-cover class coincident with the AFD), other attribute fields correspond to the larger 'fire event' to which the individual AFD belongs (e.g., the total duration of fire activity for the fire event). Accordingly, for certain attribute fields pertaining to the fire event as a whole, their values will appear 'duplicated' in the data file amongst those individual AFDs (records) that constitute the fire event in question. Whether a given attribute field pertains to the individual AFD or to its constituent fire event is denoted below for each field. Each AFD is assigned a unique ID field denoting its constituent fire event, [nfireID2]. This field is consistent between both data files, so that attribute data for a given fire event may be 'matched' to attribute data for its corresponding ignition AFD(s), and vice versa, on the basis of the common value of the field [nfireID2]. Note that many attributes below are as originally defined/measured by the input MCD 14ML data, or are derived directly thereof. 5.1 DATASET nfire4_all_spatial_fire_2002_2019_joins_sp_LC <table class="MsoTableGrid"> <tbody> <tr> <td> Field Name </td> <td> Geography of Attribute Value </td> <td> Definition </td> </tr> <tr> <td> OID </td> <td> AFD </td> <td> Object ID value. Unique values for each AFD (record) in the GIS-formatted data file when viewed in ArcGIS. Field values are -1 in the CSV-formatted data file. </td> </tr> <tr> <td> Peat </td> <td> AFD </td> <td> Denotes whether the AFD occurs on peatlands (value=1) as defined in Sloan et al. </td> </tr> <tr> <td> MIN_CONFIDE </td> <td> Fire Event </td> <td> The minimum detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. </td> </tr> <tr> <td> MAX_CONFIDE </td> <td> Fire Event </td> <td> The maximum detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. </td> </tr> <tr> <td> MEAN_CONFIDE </td> <td> Fire Event </td> <td> The mean detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. </td> </tr> <tr> <td> STD_CONFIDE </td> <td> Fire Event </td> <td> The standard deviation of detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. </td> </tr> <tr> <td> SUM_FRP </td> <td> Fire Event </td> <td> The sum total of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. </td> </tr> <tr> <td> MEAN_FRP </td> <td> Fire Event </td> <td> The mean of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. </td> </tr> <tr> <td> MIN_FRP </td> <td> Fire Event </td> <td> The minimum of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. </td> </tr> <tr> <td> MAX_FRP </td> <td> Fire Event </td> <td> The maximum of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. </td> </tr> <tr> <td> MEAN_LATITUD </td> <td> Fire Event </td> <td> The mean latitude of all AFDs in the fire event. </td> </tr> <tr> <td> MEAN_LONGITU </td> <td> Fire Event </td> <td> The mean longitude of all AFDs in the fire event. </td> </tr> <tr> <td> MIN_ACQ_DAT </td> <td> Fire Event </td> <td> The minimum acquisition date of all AFDs in the fire event (i.e., the ignition AFD detection date). </td> </tr> <tr> <td> MAX_ACQ_DAT </td> <td> Fire Event </td> <td> The maximum acquisition date of all AFDs in the fire event (i.e., the ignition AFD detection date). </td> </tr> <tr> <td> MIN_acq_yer </td> <td> Fire Event </td> <td> The year in which the earliest AFD of the fire event was detected. </td> </tr> <tr> <td> MIN_acq_mnt </td> <td> Fire Event </td> <td> The month in which the earliest AFD of the fire event (i.e., ignition AFD) was detected. Months are coded numerically, e.g., 1=January, 12=December. </td> </tr> <tr> <td> MAX_acq_mnt </td> <td> Fire Event </td> <td> The month in which the latest AFD of the fire event was detected. Months are coded numerically, e.g., 1=January, 12=December. </td> </tr> <tr> <td> MIN_yday </td> <td> Fire Event </td> <td> The detection day of year of the earliest AFD of the fire event, i.e., ignition AFD. Day of year is denoted numerically, where 1=January 1 and 365=December 31. </td> </tr> <tr> <td> MAX_yday </td> <td> Fire Event </td> <td> The detection day of year of the latest AFD of the fire event, i.e., ignition AFD. Day of year is denoted numerically, where 1=January 1 and 365=December 31. </td> </tr> <tr> <td> RANGE_yday </td> <td> Fire Event </td> <td> The number of days of duration of the fire event, defined as [MAX_yday]-[MIN_yday] </td> </tr> <tr> <td> FIRST_subst_r </td> <td> AFD </td> <td> Text labels denoting the Indonesian island/region of data processing, e.g., Kalimantan, Papua. </td> </tr> <tr> <td> AF_Count </td> <td> Fire Event </td> <td> Number of AFDs in the fire event. </td> </tr> <tr> <td> nfireID2 </td> <td> Fire Event </td> <td> The unique ID of the fire event to which the AFD belongs. </td> </tr> <tr> <td> Island </td> <td> AFD </td> <td> Numerical values coding for major Indonesian islands/region: 1000000=Sumatra; 2000000=Kalimantan; 3000000=Sulawesi; 4000000=Papua. </td> </tr> <tr> <td> FRP_Days </td> <td> Fire Event </td> <td> The severity of the fire event, as defined by Sloan et al., equal to [Sum_FRP] * ([Range_yday]+1). </td> </tr> <tr> <td> IG_Count </td> <td> Fire Event </td> <td> Number of ignition AFDs in the fire event </td> </tr> <tr> <td> CCI_LC2002 </td> <td> </td> <td> The land-cover class coincident with the AFD. The class is coded by a numerical value as per the left-most column in the table in Section 5.3. The class is that observed for the calendar year in which the AFD occurred, where the year #### is denoted in the field name 'CCI_LC####'. The land-cover data source is as described in Section 5.3. </td> </tr> <tr> <td> CCI_LC2003 </td> <td> </td> </tr> <tr> <td> CCI_LC2004 </td> <td> </td> </tr> <tr> <td> CCI_LC2005 </td> <td> </td> </tr> <tr> <td> CCI_LC2006 </td> <td> </td> </tr> <tr> <td> CCI_LC2007 </td> <td> </td> </tr> <tr> <td> CCI_LC2008 </td> <td> </td> </tr> <tr> <td> CCI_LC2009 </td> <td> </td> </tr> <tr> <td> CCI_LC2010 </td> <td> </td> </tr> <tr> <td> CCI_LC2011 </td> <td> </td> </tr> <tr> <td> CCI_LC2012 </td> <td> </td> </tr> <tr> <td> CCI_LC2013 </td> <td> </td> </tr> <tr> <td> CCI_LC2014 </td> <td> </td> </tr> <tr> <td> CCI_LC2015 </td> <td> </td> </tr> <tr> <td> CCI_LC2016 </td> <td> </td> </tr> <tr> <td> CCI_LC2017 </td> <td> </td> </tr> <tr> <td> CCI_LC2018 </td> <td> </td> </tr> <tr> <td> CCI_LC2019 </td> <td> </td> </tr> <tr> <td> Region </td> <td> AFD </td> <td> Text labels denote whether the AFD occurs within one of the two focal regions of Sloan et al.: Southern Kalimantan, or Central South Sumatra. </td> </tr> <tr> <td> POINT_X </td> <td> AFD </td> <td> Longitude, given as decimal degrees with WGS84 datum. </td> </tr> <tr> <td> POINT_Y </td> <td> AFD </td> <td> Latitude, given as decimal degrees with WGS84 datum. </td> </tr> <tr> <td> POINT_Z </td> <td> -- </td> <td> Ignore. </td> </tr> <tr> <td> POINT_M </td> <td> -- </td> <td> Ignore. </td> </tr> </tbody> </table> 5.2 DATASET nfire4_all_spatial_fire_2002_2019_igs_sp_LC <table class="MsoTableGrid"> <tbody> <tr> <td> Field Name </td> <td> Geography of Attribute Value </td> <td> Definition </td> </tr> <tr> <td> OID </td> <td> AFD </td> <td> Object ID value. Unique values for each AFD (record) in the GIS-formatted data file when viewed in ArcGIS. Field values are -1 in the CSV-formatted data file. </td> </tr> <tr> <td> IG_Count </td> <td> Fire Event </td> <td> Number of ignition AFDs in the fire event. </td> </tr> <tr> <td> MIN_acq_yer </td> <td> Fire Event </td> <td> The year in which the earliest AFD of the fire event was detected. </td> </tr> <tr> <td> MIN_acq_mnt </td> <td> Fire Event </td> <td> The month in which the earliest AFD of the fire event (i.e., ignition AFD) was detected. Months are coded numerically, e.g., 1=January, 12=December. </td> </tr> <tr> <td> FIRST_subst_r </td> <td> AFD </td> <td> Text labels denoting the Indonesian island/region of data processing, e.g., Kalimantan, Papua. </td> </tr> <tr> <td> MIN_yday </td> <td> Fire Event </td> <td> The detection day of year of the earliest AFD of the fire event, i.e., ignition AFD. Day of year is denoted numerically, where 1=January 1 and 365=December 31. </td> </tr> <tr> <td> nfireID2 </td> <td> Fire Event </td> <td> The unique ID of the fire event to which the AFD belongs. </td> </tr> <tr> <td> CCI_LC2002 </td> <td> AFD </td> <td> The land-cover class coincident with the AFD. The class is coded by a numerical value as per the left-most column in the table in Section 5.3. The class is that observed for the calendar year in which the AFD occurred, where the year #### is denoted in the field name 'CCI_LC####'. The land-cover data source is as described in Section 5.3. </td> </tr> <tr> <td> CCI_LC2003 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2004 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2005 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2006 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2007 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2008 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2009 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2010 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2011 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2012 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2013 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2014 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2015 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2016 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2017 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2018 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2019 </td> <td> AFD </td> </tr> <tr> <td> Region </td> <td> AFD </td> <td> Text labels denote whether the AFD occurs within one of the two focal regions of Sloan et al.: Southern Kalimantan, or Central South Sumatra. </td> </tr> <tr> <td> Peat </td> <td> AFD </td> <td> Denotes whether the AFD occurs on peatlands (value=1) as defined in Sloan et al. </td> </tr> <tr> <td> POINT_X </td> <td> AFD </td> <td> Longitude, given as decimal degrees with WGS84 datum. </td> </tr> <tr> <td> POINT_Y </td> <td> AFD </td> <td> Latitude, given as decimal degrees with WGS84 datum. </td> </tr> <tr> <td> POINT_Z </td> <td> -- </td> <td> Ignore. </td> </tr> <tr> <td> POINT_M </td> <td> -- </td> <td> Ignore. </td> </tr> </tbody> </table> 5.3. LAND-COVER ATTRIBUTE DATA As noted in Section 5.1 and Section 5.2, the nominal values of the attribute fields 'CCI_LC####' correspond to column 1 of the table below. These hierarchical values, and their corresponding land-cover classes labels in column 2 of the table, pertain to the land-cover classification of the Copernicus Climate Change Initiative Land-Cover Product of the European Space Agency. This classification has an annual temporal resolution and 300-meter spatial resolution. Pertinent citations for these land-cover data are below: ESA. Annual land-cover product, 1992 to 2019/present, based on MERIS 300-m and ancillary SPOT, AVHRR, Sentinel-3 and PROB-V satellite data. European Space Agency (ESA) European Centre for Medium-Range Weather Forecasts (ECMFW) Copernicus Climate Change Service (C3S) Climate Change Initiative (CCI), <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview</a>; <a href="http://maps.elie.ucl.ac.be/CCI/viewer/download.php">http://maps.elie.ucl.ac.be/CCI/viewer/download.php</a>; <a href="http://www.esa-landcover-cci.org/">http://www.esa-landcover-cci.org/</a> (2020). Pérez-Hoyos, A., Rembold, F., Kerdiles, H. & Gallego, J. Comparison of global land cover datasets for cropland monitoring. Remote Sensing 9, (2017). Columns 3 and 4 in the table below illustrate how the original land-cover classes of the Copernicus Climate Change Initiative Land-Cover Product were reclassified for analysis in Sloan et al. <table class="MsoNormalTable"> <tbody> <tr> <td> 1. CCI-LC Class Value </td> <td> 2. CCI-LC Class Label </td> <td> 3. New Class Value for Sloan et al. </td> <td> 4. New Class Label for Sloan et al. </td> </tr> <tr> <td> 0 </td> <td> No Data </td> <td> 0 </td> <td> No Data </td> </tr> <tr> <td> 10 </td> <td> Cropland, rainfed </td> <td> 1 </td> <td> Cleared/Cultivated </td> </tr> <tr> <td> 11 </td> <td> Herbaceous cover </td> <td> 1 </td> <td> Cleared/Cultivated </td> </tr> <tr> <td> 12 </td> <td> Tree or shrub cover </td> <td> 1 </td> <td> Cleared/Cultivated </td> </tr> <tr> <td> 20 </td> <td> Cropland, irrigated or post‐flooding </td> <td> 1 </td> <td> Cleared/Cultivated </td> </tr> <tr> <td> 30 </td> <td> Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%) </td> <td> 2 </td> <td> Mosaic Cropland </td> </tr> <tr> <td> 40 </td> <td> Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%) </td> <td> 3 </td> <td> Mosaic Natural Veg </td> </tr> <tr> <td> 50 </td> <td> Tree cover, broadleaved, evergreen, closed to open (>15%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 60 </td> <td> Tree cover, broadleaved, deciduous, closed to open (>15%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 61 </td> <td> Tree cover, broadleaved, deciduous, closed (>40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 62 </td> <td> Tree cover, broadleaved, deciduous, open (15‐40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 70 </td> <td> Tree cover, needleleaved, evergreen, closed to open (>15%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 71 </td> <td> Tree cover, needleleaved, evergreen, closed (>40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 72 </td> <td> Tree cover, needleleaved, evergreen, open (15‐40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 80 </td> <td> Tree cover, needleleaved, deciduous, closed to open (>15%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 81 </td> <td> Tree cover, needleleaved, deciduous, closed (>40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 82 </td> <td> Tree cover, needleleaved, deciduous, open (15‐40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 90 </td> <td> Tree cover, mixed leaf type (broadleaved and needleleaved) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 100 </td> <td> Mosaic tree and shrub (>50%) / herbaceous cover (<50%) </td> <td> 5 </td> <td> Mosaic Shrubland </td> </tr> <tr> <td> 110 </td> <td> Mosaic herbaceous cover (>50%) / tree and shrub (<50%) </td> <td> 5 </td> <td> Mosaic Shrubland </td> </tr> <tr> <td> 120 </td> <td> Shrubland </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 121 </td> <td> Evergreen shrubland </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 122 </td> <td> Deciduous shrubland </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 130 </td> <td> Grassland </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 140 </td> <td> Lichens and mosses </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 150 </td> <td> Sparse vegetation (tree, shrub, herbaceous cover) (<15%) </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 151 </td> <td> Sparse tree (<15%) </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 152 </td> <td> Sparse shrub (<15%) </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 153 </td> <td> Sparse herbaceous cover (<15%) </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 160 </td> <td> Tree cover, flooded, fresh or brakish water </td> <td> 7 </td> <td> Flooded Vegetation </td> </tr> <tr> <td> 170 </td> <td> Tree cover, flooded, saline water </td> <td> 7 </td> <td> Flooded Vegetation </td> </tr> <tr> <td> 180 </td> <td> Shrub or herbaceous cover, flooded, fresh/saline/brakish water </td> <td> 7 </td> <td> Flooded Vegetation </td> </tr> <tr> <td> 190 </td> <td> Urban areas </td> <td> 8 </td> <td> Other </td> </tr> <tr> <td> 200 </td> <td> Bare areas </td> <td> 8 </td> <td> Other </td> </tr> <tr> <td> 201 </td> <td> Consolidated bare areas </td> <td> 8 </td> <td> Other </td> </tr> <tr> <td> 202 </td> <td> Unconsolidated bare areas </td> <td> 8 </td> <td> Other </td> </tr> <tr> <td> 210 </td> <td> Water bodies </td> <td> 8 </td> <td> Other </td> </tr> </tbody> </table>

opencc-by-4.0Aug 2020View details →

Companion data of Detection, Evaluation and Mitigation of Resource Affinity and Communication Contention Problems in a Task-Based Runtime over Heterogeneous Clusters

This is the companion data repository for the paper entitled Detection, Evaluation, and Mitigation of Resource Affinity and Communication Contention Problems in a Task-Based Runtime over Heterogeneous Clusters by Lucas Leandro Nesi and Lucas Mello Schnorr. The manuscript has been accepted for publication in the <a href="http://wscad.sbc.org.br/current/index.html">WSCAD 2020</a>.

opencc-by-4.0Sep 2022View details →

Data release for "Measurements of muon-antineutrino and muon-neutrino+muon-antineutrino charged-current cross-sections without detected pions nor protons on water and hydrocarbon at mean antineutrino energy of 0.86 GeV"

This data release is associated with the publication "Measurement of charged-current cross-sections on water and hydrocarbon without detected pions nor protons using the T2K anti-neutrino beam at an off-axis angle 1.5 degrees". It is available in <a href="https://doi.org/10.1093/ptep/ptab014">Progress of Theoretical and Experimental Physics</a> and <a href="https://arxiv.org/abs/2004.13989">arXiv:2004.13989 [hep-ex]</a>. The data release contains: <ul> <li>The "histograms.root" file contains several histograms related to the cross-sections. <ul> <li>flux_numubar_* -> 1D histogram with the flux prediction at the WAGASCI module or the Proton Module of the T2K experiment.</li> <li>flux_numu_* -> 1D histogram with the flux prediction at the WAGASCI module or the Proton Module of the T2K experiment.</li> <li>Err_numubar_* -> 1D TGraphAsymmErrors with the measured flux-integrated numubar cross-sections and their uncertainties.</li> <li>Err_numu_numubar_* -> 1D TGraphAsymmErrors with the measured flux-integrated numu+numubar cross-sections and their uncertainties.</li> <li>xsec_numubar_* -> 1D histogram with the predicted flux-integrated numubar cross-sections by NEUT (5.3.3).</li> <li>xsec_numu_numubar_* -> 1D histogram with the predicted flux-integrated numu+numubar cross-sections by NEUT (5.3.3).</li> </ul> </li> <li>The "Covariance_Matrix_Numubar.root" file contains the covariance matrix for the flux-integrated numubar cross-sections, considering all the uncertainties.</li> <li>The "Covariance_Matrix_Numu+Numubar.root" file contains the covariance matrix for the flux-integrated numu+numubar cross-sections, considering all the uncertainties.</li> <li>The "flux" file contains the (anti-)muon neutrino flux prediction at the WAGASCI module or the Proton Module of the T2K experiment</li> </ul>