Files descriptions:

All csv files refer to results from the different models (PAMM, AARs, Linear models, MRPPs) on each iteration of the simulation. One row being one iteration. "results_perfect_detection.csv" refers to the results from the first simulation part with all the observations."results_imperfect_detection.csv" refers to the results from the first simulation part with randomly thinned observations to mimick imperfect detection.

ID_run: identified of the iteration (N: number of sites, D_AB: duration of the effect of A on B, D_BA: duration of the effect of B on A, AB: effect of A on B, BA: effect of B on A, Se: seed number of the iteration).PAMM30: p-value of the PAMM running on the 30-days survey.PAMM7: p-value of the PAMM running on the 7-days survey.AAR1: ratio value for the Avoidance-Attraction-Ratio calculating AB/BA.AAR2: ratio value for the Avoidance-Attraction-Ratio calculating BAB/BB.Harmsen_P: p-value from the linear model with interaction Species1*Species2 from Harmsen et al. (2009).Niedballa_P: p-value from the linear model comparing AB to BA (Niedballa et al. 2021).Karanth_permA: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species A (Karanth et al. 2017).MurphyAB_permA: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). MurphyBA_permA: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). Karanth_permB: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species B (Karanth et al. 2017).MurphyAB_permB: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). MurphyBA_permB: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021).

"results_int_dir_perf_det.csv" refers to the results from the second simulation part, with all the observations."results_int_dir_imperf_det.csv" refers to the results from the second simulation part, with randomly thinned observations to mimick imperfect detection.ID_run: identified of the iteration (N: number of sites, D_AB: duration of the effect of A on B, D_BA: duration of the effect of B on A, AB: effect of A on B, BA: effect of B on A, Se: seed number of the iteration).p_pamm7_AB: p-value of the PAMM running on the 7-days survey testing for the effect of A on B.p_pamm7_AB: p-value of the PAMM running on the 7-days survey testing for the effect of B on A.AAR1: ratio value for the Avoidance-Attraction-Ratio calculating AB/BA.AAR2_BAB: ratio value for the Avoidance-Attraction-Ratio calculating BAB/BB.AAR2_ABA: ratio value for the Avoidance-Attraction-Ratio calculating ABA/AA.Harmsen_P: p-value from the linear model with interaction Species1*Species2 from Harmsen et al. (2009).Niedballa_P: p-value from the linear model comparing AB to BA (Niedballa et al. 2021).Karanth_permA: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species A (Karanth et al. 2017).MurphyAB_permA: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). MurphyBA_permA: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). Karanth_permB: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species B (Karanth et al. 2017).MurphyAB_permB: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). MurphyBA_permB: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021).

Scripts files description:1_Functions: R script containing the functions: - MRPP from Karanth et al. (2017) adapted here for time efficiency. - MRPP from Murphy et al. (2021) adapted here for time efficiency. - Version of the ct_to_recurrent() function from the recurrent package adapted to process parallized on the simulation datasets. - The simulation() function used to simulate two species observations with reciprocal effect on each other.2_Simulations: R script containing the parameters definitions for all iterations (for the two parts of the simulations), the simulation paralellization and the random thinning mimicking imperfect detection.3_Approaches comparison: R script containing the fit of the different models tested on the simulated data.3_1_Real data comparison: R script containing the fit of the different models tested on the real data example from Murphy et al. 2021.4_Graphs: R script containing the code for plotting results from the simulation part and appendices.5_1_Appendix - Check for similarity between codes for Karanth et al 2017 method: R script containing Karanth et al. (2017) and Murphy et al. (2021) codes lines and the adapted version for time-efficiency matter and a comparison to verify similarity of results.5_2_Appendix - Multi-response procedure permutation difference: R script containing R code to test for difference of the MRPPs approaches according to the species on which permutation are done.

Data has been processed by NODC to the NODC standard Bathythermograph (MBT) (C128) format. The C128 format is used for temperature-depth profile data obtained using the mechanical bathythermograph (MBT) instrument. The maximum depth of MBT observations is approximately 285 m. Therefore, MBT data are useful only in studying the thermal structure of the upper layers of the ocean. Cruise information, date, position, and time are reported for each observation. The data record comprises pairs of temperature-depth values. Temperature data in this file are recorded at uniform 5 m depth intervals.

The dataset contains information on the prevailing weather conditions on the roads produced by the Finnish Transport Agency's road weather system. There are nearly 500 weather and weather-observing road weather stations along the roads. Most stations are located in the coastal region and southern Finland. Road weather stations provide data every 10-15 minutes from various road surface sensors on road surface conditions and meteorological sensors on the prevailing weather. Due to the location of the road weather stations, the reliability of the weather sensor data and the comparability of the data between the stations are weaker than with the weather observation stations of the Finnish Meteorological Institute, which are located in the most meteorologically representative locations. At road weather stations, the main focus is on weather measurement and the stations are therefore positioned using different methods than the weather stations.

The dataset contains data produced by the Finnish Transport Agency’s road weather system on the weather and weather on the roads. Along the roads there are almost 500 roads and weather observing the weather. The largest number of stations are located in the coastal region and southern Finland. Road weather stations provide information every 10-15 minutes from different road surface sensors about the conditions on the road surface and the weather from meteorological sensors. Due to the location of the road meteorological stations, the reliability of weather sensor data and the comparability of data between stations is weaker than the data of the Finnish Meteorological Institute’s weather observation stations, which are located in the most meteorologically representative locations. The main focus of road weather stations is the measurement of the weather and the stations are therefore positioned using different methods than weather observation stations. The dataset contains data produced by the Finnish Transport Agency’s road weather system on the weather and weather on the roads. Along the roads there are almost 500 roads and weather observing the weather. The largest number of stations are located in the coastal region and southern Finland. Road weather stations provide information every 10-15 minutes from different road surface sensors about the conditions on the road surface and the weather from meteorological sensors. Due to the location of the road meteorological stations, the reliability of weather sensor data and the comparability of data between stations is weaker than the data of the Finnish Meteorological Institute’s weather observation stations, which are located in the most meteorologically representative locations. The main focus of road weather stations is the measurement of the weather and the stations are therefore positioned using different methods than weather observation stations.

The dataset contains data produced by the Finnish Transport Agency’s road weather system on the weather and weather on the roads. Along the roads there are almost 500 roads and weather observing the weather. The largest number of stations are located in the coastal region and southern Finland. Road weather stations provide information every 10-15 minutes from different road surface sensors about the conditions on the road surface and the weather from meteorological sensors. Due to the location of the road meteorological stations, the reliability of weather sensor data and the comparability of data between stations is weaker than the data of the Finnish Meteorological Institute’s weather observation stations, which are located in the most meteorologically representative locations. The main focus of road weather stations is the measurement of the weather and the stations are therefore positioned using different methods than weather observation stations.

Data has been processed by NODC to the NODC standard Bathythermograph (MBT) (C128) format. The C128 format is used for temperature-depth profile data obtained using the mechanical bathythermograph (MBT) instrument. The maximum depth of MBT observations is approximately 285 m. Therefore, MBT data are useful only in studying the thermal structure of the upper layers of the ocean. Cruise information, date, position, and time are reported for each observation. The data record comprises pairs of temperature-depth values. Temperature data in this file are recorded at uniform 5 m depth intervals.

Observed and modeled data shown in figure 2b-c.

This dataset is associated with the following publication: Mao, J., A. Carlton, R. Cohen, W. Brune, S. Brown, G. Wolfe, J. Jimenez, H. Pye, N.L. Ng, L. Xu, V.F. McNeill, K. Tsigaridis, B. McDonald, C. Warneke, A. Guenther, M. Alvarado, J. de Gouw, L. Mickley, E. Liebensperger, R. Mathur, C. Nolte, R. Portmann, N. Unger, M. Tosca, and L. Horowitz. Southeast Atmosphere Studies: learning from model-observation syntheses. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 18: 2615-2651, (2018).

At 85 stations in the German Bitght underwater videos were recorded with Kongsberg Color Zoom Camera and GOPRO 3+ Black Edition to ground truth sidescan sonar backscatter data. The R/V Heincke was drifting while data acquisition.

This proposal intends to follow the evolution of the R Aqr jet. The same observations will also tell us about the changing structure of the nebula in the symbiotic binary R Aqr. Together with our earlier observations these new observations will provide the data to do proper motion studies of the jet that is one of the most prominent features of Aqr. FOC images will be taken in the UV and visible O III bands for comparison with previous FOC images taken by the PI. They will provide a homogeneous data set which can then serve as observational basis for modeling that system. R Aqr is the nearest symbiotic system comma and the only one comma where FOC on HST can provide spatial resolution down to the core of the system. This data will provide a 6 year baseline for studying the evolution of the binary system at a spatial resolution unattainable anywhere else for these key spectral regions.

This dataset contains CO, O3 and Shipboard Meteorology data collected on board the R/V Hakuho Maru from 29 January to 7 March 2012 from Callao, Peru to Tokyo, Japan, as part of the Equatorial Pacific Ocean and Stratospheric/Tropospheric Atmosphere Study (EqPOS) project.

Data has been processed by NODC to the NODC standard Bathythermograph (MBT) (C128) format. The C128 format is used for temperature-depth profile data obtained using the mechanical bathythermograph (MBT) instrument. The maximum depth of MBT observations is approximately 285 m. Therefore, MBT data are useful only in studying the thermal structure of the upper layers of the ocean. Cruise information, date, position, and time are reported for each observation. The data record comprises pairs of temperature-depth values. Temperature data in this file are recorded at uniform 5 m depth intervals.

Standardized data on large-scale and long-term patterns of species richness are critical for understanding the consequences of natural and anthropogenic changes in the environment. The North American Breeding Bird Survey (BBS) is one of the largest and most widely used sources of such data, but so far, little is known about the degree to which BBS data provide accurate estimates of regional richness. Here we test this question by comparing estimates of regional richness based on BBS data with spatially and temporally matched estimates based on state Breeding Bird Atlases (BBA). We expected that estimates based on BBA data would provide a more complete (and therefore, more accurate) representation of regional richness due to their larger number of observation units and higher sampling effort within the observation units. Our results were only partially consistent with these predictions: while estimates of regional richness based on BBA data were higher than those based on BBS data, estimates of local richness (number of species per observation unit) were higher in BBS data. The latter result is attributed to higher land-cover heterogeneity in BBS units and higher effectiveness of bird detection (more species are detected per unit time). Interestingly, estimates of regional richness based on BBA blocks were higher than those based on BBS data even when differences in the number of observation units were controlled for. Our analysis indicates that this difference was due to higher compositional turnover between BBA units, probably due to larger differences in habitat conditions between BBA units and a larger number of geographically restricted species. Our overall results indicate that estimates of regional richness based on BBS data suffer from incomplete detection of a large number of rare species, and that corrections of these estimates based on standard extrapolation techniques are not sufficient to remove this bias. Future applications of BBS data in ecology and conservation, and in particular, applications in which the representation of rare species is important (e.g., those focusing on biodiversity conservation), should be aware of this bias, and should integrate BBA data whenever possible.

Methods Overview

This is a compilation of second-generation breeding bird atlas data and corresponding breeding bird survey data. This contains presence-absence breeding bird observations in 5 U.S. states: MA, MI, NY, PA, VT, sampling effort per sampling unit, geographic location of sampling units, and environmental variables per sampling unit: elevation and elevation range from (from SRTM), mean annual precipitation & mean summer temperature (from PRISM), and NLCD 2006 land-use data.

Each row contains all observations per sampling unit, with additional tables containing information on sampling effort impact on richness, a rareness table of species per dataset, and two summary tables for both bird diversity and environmental variables.

The methods for compilation are contained in the supplementary information of the manuscript but also here:

Bird data

For BBA data, shapefiles for blocks and the data on species presences and sampling effort in blocks were received from the atlas coordinators. For BBS data, shapefiles for routes and raw species data were obtained from the Patuxent Wildlife Research Center (https://databasin.org/datasets/02fe0ebbb1b04111b0ba1579b89b7420 and https://www.pwrc.usgs.gov/BBS/RawData).

Using ArcGIS Pro© 10.0, species observations were joined to respective BBS and BBA observation units shapefiles using the Join Table tool. For both BBA and BBS, a species was coded as either present (1) or absent (0). Presence in a sampling unit was based on codes 2, 3, or 4 in the original volunteer birding checklist codes (possible breeder, probable breeder, and confirmed breeder, respectively), and absence was based on codes 0 or 1 (not observed and observed but not likely breeding). Spelling inconsistencies of species names between BBA and BBS datasets were fixed. Species that needed spelling fixes included Brewer’s Blackbird, Cooper’s Hawk, Henslow’s Sparrow, Kirtland’s Warbler, LeConte’s Sparrow, Lincoln’s Sparrow, Swainson’s Thrush, Wilson’s Snipe, and Wilson’s Warbler. In addition, naming conventions were matched between BBS and BBA data. The Alder and Willow Flycatchers were lumped into Traill’s Flycatcher and regional races were lumped into a single species column: Dark-eyed Junco regional types were lumped together into one Dark-eyed Junco, Yellow-shafted Flicker was lumped into Northern Flicker, Saltmarsh Sparrow and the Saltmarsh Sharp-tailed Sparrow were lumped into Saltmarsh Sparrow, and the Yellow-rumped Myrtle Warbler was lumped into Myrtle Warbler (currently named Yellow-rumped Warbler). Three hybrid species were removed: Brewster's and Lawrence's Warblers and the Mallard x Black Duck hybrid. Established “exotic” species were included in the analysis since we were concerned only with detection of richness and not of specific species.

The resultant species tables with sampling effort were pivoted horizontally so that every row was a sampling unit and each species observation was a column. This was done for each state using R version 3.6.2 (R© 2019, The R Foundation for Statistical Computing Platform) and all state tables were merged to yield one BBA and one BBS dataset. Following the joining of environmental variables to these datasets (see below), BBS and BBA data were joined using rbind.data.frame in R© to yield a final dataset with all species observations and environmental variables for each observation unit.

Environmental data

Using ArcGIS Pro© 10.0, all environmental raster layers, BBA and BBS shapefiles, and the species observations were integrated in a common coordinate system (North_America Equidistant_Conic) using the Project tool. For BBS routes, 400m buffers were drawn around each route using the Buffer tool. The observation unit shapefiles for all states were merged (separately for BBA blocks and BBS routes and 400m buffers) using the Merge tool to create a study-wide shapefile for each data source. Whether or not a BBA block was adjacent to a BBS route was determined using the Intersect tool based on a radius of 30m around the route buffer (to fit the NLCD map resolution). Area and length of the BBS route inside the proximate BBA block were also calculated. Mean values for annual precipitation and summer temperature, and mean and range for elevation, were extracted for every BBA block and 400m buffer BBS route using Zonal Statistics as Table tool. The area of each land-cover type in each observation unit (BBA block and BBS buffer) was calculated from the NLCD layer using the Zonal Histogram tool.

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This dataset consists of 100,000 observations from the Data Release (DR) 18 of the Sloan Digital Sky Survey (SDSS). Each observation is described by 42 features and 1 class column classifying the observation as either:

• a STAR
• a GALAXY
• a QSO (Quasi-Stellar Object) or a Quasar.

You can read more about the features below:

• Objid, Specobjid - Object Identifiers
• ra - J2000 Right Ascension
• dec - J2000 Declination
• redshift - Final Redshift of the celestial object
• u, g, r, i, and z - better of DeV/Exp magnitude fit for u, g, r, i, and z. u, g, r, i, and z correspond to the five photometric bands namely ultraviolet band, green band, red band, infrared band, and near infrared band respectively.
• run - Run number
• rerun - Rerun number
• camcol - Camera column
• field - Field number

The run number refers to a specific period in which the SDSS observes a part of the sky. SDSS is divided into several runs, each lasting for a certain amount of time, which are then combined to cover an extensive portion of the sky. The rerun number refers to the reprocessing of the data obtained.

In each run, multiple charge-coupled device (CCD) cameras are arranged into a column which are responsible for imaging a specific portion of the sky. camcol refers to the camera column number which imaged a specific observation. A field is a specific portion of the sky that is imaged during a single exposure of the telescope. The entire sky is divided into a portion of fields and the field number column refers to the field or portion of the sky from which an observation was obtained.

• plate - Plate number
• fiberID - Optical Fiber ID

A number of physical glass plates are mounted on the telescope, each containing a number of optical fibers corresponding to a specific position in the sky. When light hits these optical fibers, it is sent to spectrographs for analysis. plate number and fiberID refer to the number of the plate and the ID of the optical fiber responsible for gathering light from the celestial object respectively.

• mjd - Modified Julian Date

Modified Julian Date represents the number of days that have passed since midnight Nov. 17, 1858. It is used in SDSS to keep track of the time of each observation.

• petroRad_u, petroRad_g, petroRad_r, petroRad_i, and petroRad_z - Petrosian Radii for the five photometric bands u (ultraviolet), g (green), r (red), i (infrared), and z (near-infrared) respectively.

The petrosian radius is a measure of the size of a galaxy, and it is calculated using the petrosian flux profile. The petrosian flux profile measures how the brightness of an object varies with distance from its center. The petrosian radius is defined as the distance from the galaxy's center where the ratio of the local surface brightness to the average surface brightness reaches a certain predefined value. The local surface brightness refers to the brightness of a specific small region or pixel on the surface of an extended object. It is a measure of how much light is detected from that particular region. The average surface brightness, on the other hand, represents the mean or average brightness measured over the entire surface of the extended object. It is the total amount of light received from the object divided by its total area.

These parameters help in characterizing the properties of celestial objects, especially when studying their morphologies, sizes, and how they evolve over time.

• petroFlux_u, petroFlux_g, petroFlux_r, petroFlux_i, and petroFlux_z - Petrosian Fluxes for the five photometric bands u (ultraviolet), g (green), r (red), i (infrared), and z (near-infrared) respectively. These features describe the total amount of light emitted from the celestial objects.

These parameters help in studying the photometric properties of the celestial objects, particularly in analyzing the brightness, colors, and spectral energy distribution of the objects. By using petrosian fluxes in different bands, astronomers can obtain a comprehensive view of an object's light emission across the electromagnetic spectrum.

• petroR50_u, petroR50_g, petroR50_r, petroR50_i, and petroR50_z - Petrosian half-light radii for the five photometric bands u (ultraviolet), g (green), r (red), i (infrared), and z (near-infrared) respectively. PetroR50 is a measure of the radius at which half of the total light (or flux) emitted from a celestial object is enclosed with the petrosian aperture. The petrosian aperture is defined based on the petrosian radius, which is a measure of the size of the celestial object. The petrosian aperture allows a...

The scripts in this folder weer used to combine all call statistic files per day into one file, resulting in nine files containing all call statistics per data. The script ‘merging_dataset.R’ was used to combine all days worth of call statistics and create subsets of two frequency ranges (18-32 and 32-96). The script ‘camera_data’ was used to combine all camera and observation data.

Summary of terrestrial environmental monitoring and observation effort by networks associated with three Australian National Research Infrastructures from 2010 to the present organised by State/Territory, IBRA region, feature type and year.\r \r Metadata records were aggregated from biodiversity survey events from the Atlas of Living Australia (ALA, https://ala.org.au/), marine observations collected by the Integrated Marine Observing System (IMOS, https://imos.org.au/) and site-based monitoring and survey efforts by the Terrestrial Ecosystem Research Network (TERN, https://tern.org.au/). See Aggregated Data: Environmental Monitoring and Observations Effort for information on the metadata included.\r \r To find out more about this dataset visit: https://ecoassets.org.au/data/summary-data-monitoring-and-observations-effort-by-terrestrial-ecoregion/\r

Direct observations of the oceans acquired on oceanographic research ships operated across the international community support fundamental research into the many disciplines of ocean science and provide essential information for monitoring the health of the oceans. A comprehensive knowledge base is needed to support the responsible stewardship of the oceans with easy access to all data acquired globally. In the United States, the multidisciplinary shipboard sensor data routinely acquired each year on the fleet of coastal, regional and global ranging vessels supporting academic marine research are managed by the Rolling Deck to Repository (R2R, rvdata.us) program. With over a decade of operations, the R2R program has developed a robust routinized system to transform diverse data contributions from different marine data providers into a standardized and comprehensive collection of global-ranging observations of marine atmosphere, ocean, seafloor and subseafloor properties that is openly available to the international research community. In this article we describe the elements and framework of the R2R program and the services provided. To manage all expeditions conducted annually, a fleet-wide approach has been developed using data distributions submitted from marine operators with a data management workflow designed to maximize automation of data curation. Other design goals are to improve the completeness and consistency of the data and metadata archived, to support data citability, provenance tracking and interoperable data access aligned with FAIR (findable, accessible, interoperable, reusable) recommendations, and to facilitate delivery of data from the fleet for global data syntheses. Findings from a collection-level review of changes in data acquisition practices and quality over the past decade are presented. Lessons learned from R2R operations are also discussed including the benefits of designing data curation around the routine practices of data providers, approaches for ensuring preservation of a more complete data collection with a high level of FAIRness, and the opportunities for homogenization of datasets from the fleet so that they can support the broadest re-use of data across a diverse user community.

At 113 stations in the German Bitght underwater videos were recorded with Kongsberg Color Zoom Camera to ground truth sidescan sonar backscatter data. The R/V Heincke was drifting while data acquisition.

The dataset contains information on the prevailing weather conditions on the roads produced by the Finnish Transport Agency's road weather system. There are nearly 500 weather and weather-observing road weather stations along the roads. Most stations are located in the coastal region and southern Finland. Road weather stations provide data every 10-15 minutes from various road surface sensors on road surface conditions and meteorological sensors on the prevailing weather. Due to the location of the road weather stations, the reliability of the weather sensor data and the comparability of the data between the stations are weaker than with the weather observation stations of the Finnish Meteorological Institute, which are located in the most meteorologically representative locations. At road weather stations, the main focus is on weather measurement and the stations are therefore positioned using different methods than the weather stations.

Comparison of variable selection results of different models for Brithwt dataset.

This dataset describes observations made of humidity by sensors distributed in Ballarat.\r The information was collected in real time by the sensors.\r The intended use of the information is to inform the public of the historical measured observations of humidity in Ballarat.\r The dataset is typically updated every 15 minutes.\r The City of Ballarat is not an official source of weather information. These observations are provided to the public for informative purposes only. Use other channels for official meteorological observations and forecasts.