This database is the Third Small Astronomy Satellite (SAS-3) Y-Axis Pointed Observation Log. It identifies possible pointed observations of celestial X-ray sources which were performed with the y-axis detectors of the SAS-3 X-Ray Observatory. This log was compiled (by R. Kelley, P. Goetz and L. Petro) from notes made at the time of the observations and it is expected that it is neither complete nor fully accurate. Possible errors in the log are (i) the misclassification of an observation as a pointed observation when it was either a spinning or dither observation and (ii) inaccuracy of the dates and times of the start and end of an observation. In addition, as described in the HEASARC_Updates section, the HEASARC added some additional information when creating this database. Further information about the SAS-3 detectors and their fields of view can be found at: http://heasarc.gsfc.nasa.gov/docs/sas3/sas3_about.html Disclaimer: The HEASARC is aware of certain inconsistencies between the Start_date, End_date, and Duration fields for a number of rows in this database table. They appear to be errors present in the original table. Except for one entry where the HEASARC corrected an error where there was a near-certainty which parameter was incorrect (as noted in the 'HEASARC_Updates' section of this documentation), these inconsistencies have been left as they were in the original table. This database table was released by the HEASARC in June 2000, based on the SAS-3 Y-Axis pointed Observation Log (available from the NSSDC as dataset ID 75-037A-02B), together with some additional information provided by the HEASARC itself. This is a service provided by NASA HEASARC .

The SAS2RAW database is a log of the 28 SAS-2 observation intervals and contains target names, sky coordinates start times and other information for all 13056 photons detected by SAS-2. The original data came from 2 sources. The photon information was obtained from the Event Encyclopedia, and the exposures were derived from the original "Orbit Attitude Live Time" (OALT) tapes stored at NASA/GSFC. These data sets were combined into FITS format images at HEASARC. The images were formed by making the center pixel of a 512 x 512 pixel image correspond to the RA and DEC given in the event file. Each photon's RA and DEC was converted to a relative pixel in the image. This was done by using Aitoff projections. All the raw data from the original SAS-2 binary data files are now stored in 28 FITS files. These images can be accessed and plotted using XIMAGE and other columns of the FITS file extensions can be plotted with the FTOOL FPLOT. This is a service provided by NASA HEASARC .

This database is a collection of maps created from the 28 SAS-2 observation files. The original observation files can be accessed within BROWSE by changing to the SAS2RAW database. For each of the SAS-2 observation files, the analysis package FADMAP was run and the resulting maps, plus GIF images created from these maps, were collected into this database. Each map is a 60 x 60 pixel FITS format image with 1 degree pixels. The user may reconstruct any of these maps within the captive account by running FADMAP from the command line after extracting a file from within the SAS2RAW database. The parameters used for selecting data for these product map files are embedded keywords in the FITS maps themselves. These parameters are set in FADMAP, and for the maps in this database are set as 'wide open' as possible. That is, except for selecting on each of 3 energy ranges, all other FADMAP parameters were set using broad criteria. To find more information about how to run FADMAP on the raw event's file, the user can access help files within the SAS2RAW database or can use the 'fhelp' facility from the command line to gain information about FADMAP. This is a service provided by NASA HEASARC .

Output from programming code written to summarize 2018 monarch butterfly abundance from monitoring data acquired using a modified Pollard walk at custom 2017 GRTS draw sites within select monitoring areas (see SOP 2 in ServCat reference 103367 for methods) of FWS Legacy Regions 2 and 3. Areas monitored included Balcones Canyonlands (TX), Hagerman (TX), Washita (OK), Neal Smith (IA) NWRs and several locations near the town of Lamoni, Iowa and northern Missouri. Input data file is named 'FWS_2018_MM_SOP2_for_SAS.csv' and is stored in ServCat reference 136485. See SM 5 (ServCat reference 103388) for dictionary of data fields in the input data file.

The SAS2RAW database is a log of the 28 SAS-2 observation intervals and contains target names, sky coordinates start times and other information for all 13056 photons detected by SAS-2. The original data came from 2 sources. The photon information was obtained from the Event Encyclopedia, and the exposures were derived from the original "Orbit Attitude Live Time" (OALT) tapes stored at NASA/GSFC. These data sets were combined into FITS format images at HEASARC. The images were formed by making the center pixel of a 512 x 512 pixel image correspond to the RA and DEC given in the event file. Each photon's RA and DEC was converted to a relative pixel in the image. This was done by using Aitoff projections. All the raw data from the original SAS-2 binary data files are now stored in 28 FITS files. These images can be accessed and plotted using XIMAGE and other columns of the FITS file extensions can be plotted with the FTOOL FPLOT. This is a service provided by NASA HEASARC .

This database is a collection of maps created from the 28 SAS-2 observation files. The original observation files can be accessed within BROWSE by changing to the SAS2RAW database. For each of the SAS-2 observation files, the analysis package FADMAP was run and the resulting maps, plus GIF images created from these maps, were collected into this database. Each map is a 60 x 60 pixel FITS format image with 1 degree pixels. The user may reconstruct any of these maps within the captive account by running FADMAP from the command line after extracting a file from within the SAS2RAW database. The parameters used for selecting data for these product map files are embedded keywords in the FITS maps themselves. These parameters are set in FADMAP, and for the maps in this database are set as 'wide open' as possible. That is, except for selecting on each of 3 energy ranges, all other FADMAP parameters were set using broad criteria. To find more information about how to run FADMAP on the raw event's file, the user can access help files within the SAS2RAW database or can use the 'fhelp' facility from the command line to gain information about FADMAP. This is a service provided by NASA HEASARC .

Output from programming code written to summarize data describing 2018 MCSP Trial monitoring sites acquired using a SOP 1 (see ServCat reference 103364) of FWS Legacy Regions 2 and 3. Monitoring sites were selected using a custom GRTS draw conducted by USGS in 2017, within monitoring areas associated with select NWRS stations. Areas monitored included Balcones Canyonlands (TX), Hagerman (TX), Washita (OK), Neal Smith (IA) NWRs and several locations near the town of Lamoni, Iowa and private lands in northern Missouri.

Output from programming code written to summarize data describing 2017 MCSP Trial monitoring sites acquired using a SOP 1 (see ServCat reference 103364) of FWS Legacy Regions 2 and 3. 2017 monitoring sites were selected using a custom GRTS draw conducted by USGS, within monitoring areas associated with select NWRS stations. Areas monitored included Balcones Canyonlands (TX), Hagerman (TX), Washita (OK), Neal Smith (IA), Necedah (WI) NWRs and several locations near the town of Lamoni, Iowa and private lands in northern Missouri.

Output from programming code written to summarize monarch butterfly abundance from monitoring data acquired using a modified Pollard walk at custom 2017 GRTS draw sites within select monitoring areas (see SOP 2 in ServCat reference 103367 for methods) of FWS Legacy Regions 2 and 3. Areas monitored included Balcones Canyonlands (TX), Hagerman (TX), Washita (OK), Neal Smith (IA), Necedah (WI) NWRs and several locations near the town of Lamoni, Iowa and private lands in northern Missouri.

Output from programming code written to summarize immature monarch butterfly, milkweed and nectar plant abundance from monitoring data acquired using a grid of 1 square-meter quadrats at custom 2017 GRTS draw sites within select monitoring areas (see SOP 3 in ServCat reference 103368 for methods) of FWS Legacy Regions 2 and 3. Areas monitored included Balcones Canyonlands (TX), Hagerman (TX), Washita (OK), Neal Smith (IA) NWRs and several locations near the town of Lamoni, Iowa and northern Missouri. Input data file is named 'FWS_2018_MonMonSOP3DS1_forSAS.csv' and is stored in ServCat reference 137698. See SM 5 (ServCat reference 103388) for dictionary of data fields in the input data file.

This dataset contains the scrubbed chat logs from the Southeast Atmosphere Study (SAS) project, including NOMADSS (Nitrogen, Oxidants, Mercury and Aerosol Distributions, Sources and Sinks), from May 30 - July 17, 2013. The chat logs contain conversations between scientists and other field project participants regarding data collection within the SAS-NOMADSS project.

This dataset consists of 1-minute merged data from the 19 research flights with the C-130 over the Southeast U.S. between June 1 and July 15, 2013, as part of the Southeast Atmosphere Study (SAS). Merged data files have been created, combining all observations on the C-130 to a common time base for each flight. Version R5 (created Jan 21, 2015) of the merges includes all data available as of Jan 12. Time is midpoint of 60-sec average; Any measurement that overlaps the 60-sec window is included in the average; no limit on the length of overlap; weighted average is performed.

The effect of non-significant terms was estimated by adding them individually in to the final model. Minute of scan was nested within site and date and added as random factor into the model to control for the effect of repeated observations within a given site.a =  Place: air, ground, hedge, tree, house and wires, foodb =  Activity: fight, fly, forage, perchc =  Temperature in degree Cd =  Food spillage: locations without food spillage (i.e. maize, chicken food, grains, manure, hay), minor food spillage, medium degree of food spillage in several places, large degree of food spillage in the whole locatione =  Disturbance occurred during sampling (i.e. passing by car, human)f =  Number of different crops, animal foods stored at the siteg = Number of different stock in each site (i.e. horses, cows, sheep, pigs, chicken)h =  Distance to next location in mI =  Weather during the observation: foggy, strong wind or rain, normal weather (i.e. no fog, strong wind or rain).

The XMM-Newton X-ray observatory has performed repeated observations of the CDFS in 33 epochs (2001-2010) through the XMM-CDFS Deep Survey. During the X-ray observations, XMM-OM targeted the central 17x17arcmin^2^ region of the X-ray field of view, providing simultaneous optical/UV coverage of the CDFS. The resulting set of data can be taken into account to build an XMM-OM catalogue of the CDFS, filling the UV spectral coverage between the optical surveys and GALEX observations. We present the UV catalogue of the XMM-CDFS Deep Survey. Its main purpose is to provide complementary UV average photometric measurements of known optical/UV sources in the CDFS, taking advantage of the unique characteristics of the survey. The data reduction is intended also to improve the standard source detection on individual observations, by cataloguing faint sources through the stacking of their exposure images. We reprocessed the XMM-OM data of the survey and we stacked the exposures from consecutive observations using the standard SAS tools to process the data obtained during single observations. Average measurements of detections with SAS good quality flags from individual observations and from stacked images have been joined to compile the catalogue. Sources have been validated through the cross-identification within the EIS and COMBO-17 surveys. Photometric data of 1129 CDFS sources are provided into the catalogue, and optical/UV/X-ray photometric and spectroscopic information from other surveys are also included. The stacking extends the detection limits by ~1 mag in the three UV bands, contributing 30% of the catalogued UV sources. The comparison with the available measurements in similar spectral bands confirms the validity of the XMM-OM calibration. The combined COMBO-17/X-ray classification of the "intermediate" sources (e.g. optically diluted and/or X-ray absorbed AGN) is also discussed.

Multispectral radiometry resolutely quantifies canopy attributes of similarly managed monocultures over wide and varied temporal arrays. Likewise, liquid phthalocyanine-containing products are commonly applied to turfgrass as a spray pattern indicator, dormancy colorant, and/or product synergist. While perturbed multispectral radiometric characterization of putting greens within 24 h of treatment by synthetic phthalocyanine colorant has been reported, explicit guidance on subsequent use is absent from the literature. Our objective was to assess creeping bentgrass (Agrostis stolonifera L. ‘Penn G2’) putting green reflectance and growth one to 14 d following semi-monthly treatment by synthetic Cu II phthalocyanine colorant (Col) and petroleum-derived spray oil (PDSO) combination product at a 27 L ha–1 rate and/or 7.32 hg ha–1 soluble N treatment by one of two commercial liquid fertilizers. As observed in a bentgrass fairway companion study, mean daily shoot growth and canopy dark green color index (DGCI) increased with Col+PDSO complimented N treatment. Yet contrary to the fairway study results, deflated mean normalized differential red edge (NDRE) or vegetative index (NDVI) resulted from an associated Col+PDSO artifact that severely impeded near infrared (810-nm) putting green canopy reflectance. Regardless of time from Col+PDSO combination product treatment, the authors strongly discourage turfgrass scientists from employing vegetative indices that rely on 760- or 810-nm canopy reflectance when evaluating such putting green systems. Methods The requested information is described ad nauseum in the Materials & Methods section of the ‘Related Works.’

On 2. Nov., the author mistakenly uploaded a raw data file. Within, the first worksheet/tab titled MSR contained all 475 lines of MSR and vegetative index data. However, consideration for abidance of ANOVA assumptions precluded a small number of dependent variable observations, as employ of garden variety transformations were unsuccessful. Specifically, for percent reflectance of 510-, 560-, 610-, 660-, 760-, and 810-nm spectra; 2, 2, 2, 3, 3, and 4 observations were omitted as missing data, respectively. Likewise, since the dark green color index (DGCI) is calculated by 460, 560, and 660-nm reflectance, five (5) DGCI observations were conceded as missing data. Results described in the ‘Related Works’ report 510-, 560-, 610-, 660-, 760-, and 810-nm reflectance means and inference from 473-, 473-, 473-, 472-, 472-, and 471-observation datasets, respectively. No data were replaced and degree of freedom penalties were incurred in analysis reported in ‘Related Works.’ Likewise, the daily clipping yield data, dCY (2nd worksheet/tab) in the original 2 Nov. file upload, contained 150 observations. The statistical model and analysis of dCY data described in the ‘Related Works’ results report means and inference from a 148-observation dataset. The SAS output for each the reduced (n=148) and full (n=150) datasets are now included in data files. Model diagnostics on the reduced datasets, uploaded 11 Dec., 2022 meet all required assumptions. For the dCY data, the model diagnostics issue and resolution are squarely depicted in the two attached SAS outputs. The same is true for the MSR data, but SAS outputs are not attached. Motivated parties are invited to reanalyze the above-noted dependent variables using the 2 Nov. (full) and 11 Dec. (reduced) data freely available to you in ‘Data Files.’ It is strict Dryad policy that voluntarily uploaded data files not be deleted. Thus, the authors were compelled to append the two regrettably-conflicting datasets with the above explanation, today, 11 Dec. 2022. We hope you have found this explanation helpful and encourage you to forward your questions or comments to Max Schlossberg at mjs38@psu.edu.

Output (results) from programming code written to summarize red-imported fire ant (RIFA) abundance from monitoring along transects at custom 2017 GRTS draw sites within select monitoring areas (see SOP 6 in ServCat reference 103385 for methods) of FWS Legacy Regions 2 and 3. Areas monitored included Balcones Canyonlands (TX) and Hagerman (TX) NWRs. The spreadsheet labeled as SOP 6 Metrics displays the different estimates in different worksheets. Each worksheet can be used for additional analysis.

The XMM-Newton observatory is a cornerstone mission of the European Space Agency (ESA) Horizon 2000 program. This spacecraft, the largest ever flown by ESA for a scientific program, was launched on December 10, 1999, carrying a payload funded by ESA member states and the USA (NASA). The scientific payload comprises three CCD imaging cameras (European Photon Imaging Cameras, EPIC), sensitive in the 0.1-15 keV band, and two Reflecting Grating Spectrometers (RGS), sensitive in the 0.3-2.1 keV band, and characterized by a resolving power E/{Delta}E = 100 to 800. The X-ray instruments are complemented by an Optical Monitor, sensitive in the 150-650nm band, which allows simultaneous multiwavelength monitoring of celestial sources. The XMM-Newton observational program is determined on the bases of the proposals sent in response to Announcement of Opportunities, and selected by peer review panels. The XMM-Newton Science Archive (XSA) contains all the science data of all the performed observations. Its user interface (http:xmm.vilspa.esa.es/xsa) allows a user to retrieve them after the 1-year proprietary period has expired. Calibration observations are normally not covered by proprietary rights; their data being therefore public. Target of Opportunity and Discretionary Time observations have a 6-months proprietary period. For each archived observation, the XSA stores Observation Data Files (ODF) and Pipeline Processing System (PPS) products, if available, as well as the XMM-Newton EPIC serendipitous catalogue, the OM source catalogue and the Slew Survey Source Catalogue (see the catalogues documentation at http://xmm.esac.esa.int/xsa). The ODF comprises raw telemetry files, reformatted in FITS format, and contains uncalibrated information. The PPS products are a collection of top-level, validated scientific and cross-correlation products, routinely generated by the Science Survey Center, University of Leicester, UK (http://xmmssc-www.star.le.ac.uk). The content of the XSA is updated daily. The latest version of all the scientific data is made available through its interface. Systematic reprocessing of all the XMM-Newton telemetry is periodically carried on during the mission. The last bulk reprocessing was performed in 2007. The XMM-Newton observation log lists all the science observations included in the XSA. This log gives observation details and provides links to quick-look scientific products, to documents describing XMM-Newton science and calibration data, and to the SAS (Science Analysis System), a specific software package designed to reduce and analyze XMM-Newton data. Additional links in the log allows a user to start a retrieval session for the data of an observation, whenever not protected by proprietary rights. XMM-Newton HelpDesk: http://xmm.esac.esa.int/external/xmm_user_support/helpdesk.shtml

The effects of providing rest on physiological and behavioural indicators of welfare of cattle being transported by road has not been well studied in North America. New revisions to Canada’s Health of Animals Regulations Part XII: Transportation of Animals indicate un-weaned and weaned calves can be transported a maximum of 12 and 36 h, respectively, before an 8 h rest is required. Therefore, the aim of this study was to assess the effects of rest duration, after 12 and 36 h of transport, on physiological and behavioural indicators of welfare in 7–8 mo-old beef calves. Three hundred and twenty conditioned calves (258 ± 23.9 kg BW) were randomly assigned to a 2 × 4 factorial design where the main factors included transport duration: 12 h (12; n = 160) and 36 h (36; n = 160) and rest stop duration: 0 h (R0; n = 80), 4 h (R4; n = 80), 8 h (R8; n = 80) and 12 h (R12; n = 80). After the resting period, animals were transported for an additional 4 h. Blood and hair samples were taken from 12 animals per treatment prior to and after the first and the 4 h transport; and then 7 h, 2 d and 28 d after the 4 h transport. The concentrations of haptoglobin, creatine kinase, non-esterified fatty acids (NEFA), lactate, and serum and hair cortisol were determined. Standing and lying behaviour was assessed for 14 d after transport, while feeding behaviour of calves in one pen per treatment were assessed for 28 d after transportation using an electronic feed bunk monitoring system. Body weight (BW), average daily gain (ADG) and shrink (%) was assessed for all calves. The data was modeled using generalized linear mixed methods (SAS PROC GLIMMIX), where transport and time (nested in rest) were considered fixed effects and animal and pen were considered random effects. Statistically significant (p < 0.05) effects of transport were observed on BW and shrink, where 36 h-transported calves had lower (p < 0.01) BW and greater (p < 0.01) shrink than 12 h-transported calves. A transport × time (nested in rest) interaction (p < 0.01) was observed for lying percentage where, 36-R8 calves had greater (p < 0.01) lying percentage than 12-R8 calves on d 1 after transportation. The area under the curve (AUC) for NEFA was greater (p < 0.01) for 36-R0 calves than 12-R0, 36-R4, and 36-R8 calves, and greater (p < 0.01) in 36-R12 calves than 12-R12 calves. Haptoglobin AUC was greater (p = 0.05) in 36-R12 than 12-R12 calves. Overall, physiological indicators of reduced welfare were greater in calves transported for 36 than 12 h, while no clear differences were observed between rest stop groups with the exception of NEFA. Based on these results, conditioned calves benefit from shorter transport durations but there was no clear evidence that calves rested 4, 8, and 12 h following transportation experienced reduced transport related stress compared to those that were not rested (0h).

Water age and flow pathways should be related; however, it is still generally unclear how integrated catchment runoff generation mechanisms result in streamflow age distributions at the outlet. Lapides et al. (2021) combined field observations of runoff generation at the Dry Creek catchment with StorAge Selection (SAS) age models to explore the relationship between streamwater age and runoff pathways. Dry Creek is an intensively monitored catchment in the northern California Coast Ranges with a Mediterranean climate and thin subsurface critical zone. Due to limited storage capacity, runoff response is rapid (~1-2 hours), and total annual streamflow consists predominantly of saturation overland flow, based on field mapping of saturated extents and runoff thresholds. Even though SAS modeling reveals that streamflow is younger at higher wetness states, flow is still typically older than one day. Because streamflow is mostly overland flow, this means that a significant portion of overland flow must not be event-rain but instead derive from older groundwater returning to the surface, consistent with field observations of exfiltrating head gradients, return flow through macropores, and extensive saturation days after storm events. We conclude that even in a landscape with widespread overland flow, runoff pathways may be longer than anticipated, with implications for contaminant delivery and biogeochemical reactions. Our findings have implications for the assumptions built into classic hydrograph separation inferences, namely, whether overland flow consists of new water.

For this work, we translated SAS modeling code in Matlab from Benettin and Bertuzzo (2018) to Python and provide here a set of code for SAS modeling in Python and example data for Dry Creek, CA produced for the SAS modeling publication by Lapides et al. (2021).