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CBP01 Variable distance line-transect sampling of bird population numbers in different habitats on Konza Prairie
Records of bird species based on line transect sampling, giving perpendicular distance of sighting from the transect line on 16 separate transects. Bird surveys were conducted 2-4 times per year in January, April, June, and October for a 29-year period from 1981 to 2009. Transects were designed to determine bird communities and population numbers associated with tallgrass prairie habitats with different experimental treatments (fire frequency, grazed by bison vs. ungrazed), riparian habitats on forest edge, and gallery forests dominated by oak woodland.
ABG-IMRHT and Ames-2000K IR line lists for N2O as reported in "Accurate N2O IR Line Lists with Consistent Empirical Line Positions: ABG-IMRHT and Ames-2000K"
<p><strong>[2025-06-17, v2.0</strong>, updated from v1.1 (<a href="https://doi.org/10.5281/zenodo.14834513">10.5281/zenodo.14834513</a>)]<br>(1). This upgraded version is recommended for analysis involving B1b or ABG(-IMRHT) related line lists, or E' > 10,000 cm<sup>-1</sup>. <br>(2). Energy level lists computed on the B1b PES and related 296K ABG-IMRHT line intensity and line lists, and B1b-based 1000-3000K line list files have been updated, along with n2olist.f90.v1.4. <br>(3). The full 296 K IR line lists of <strong>all 12 stable isotopologues</strong> are provided in "n2o.296K.ABG-IMRHT.20250602.with.broadening.dat.v5corrected.100pct-abundance.xz", in which the line intensities assume 100% abundance for every isotopologue.<br>(4). Please note that the B1b PES-based energy levels in "empirical.corrections.for.ABG-IMRHT.zip" are still valid, but the B1b PES based energy levels are not updated in the files inside that .zip. </p> <p>[<strong>Link to <a href="https://www.sciencedirect.com/science/article/pii/S0022407325001645">the N2O paper</a> </strong>published at JQSRT (2025) 343, 109502, part of VSI:HITRAN2024, doi:<a href="https://doi.org/10.1016/j.jqsrt.2025.109502">10.1016/j.jqsrt.2025.109502</a>]</p> <p>[Updated from v1.0 (<a href="https://doi.org/10.5281/zenodo.14174307">10.5281/zenodo.14174307</a>). The J=151-210 transitions of <sup>14</sup>N<sub>2</sub><sup>16</sup>O were missing from v1.0 files ]</p> <p><strong>1. Second generation of Ames-296K IR line list for "natural" Nitrous Oxide (N<sub>2</sub>O), denoted ABG-IMRHT</strong>, which was computed from Ames-B1b PES refinement (using Benjamin Schröder's ab initio PES Comp I, doi:10.1515/zpch-2015-0622) and 2023 dmsG-10K accurately fitted from CCSD(T)/aug-cc-pV(T,Q,5)Z dipoles. In this major upgrade to Ames-296K N<sub>2</sub>O line list (10.1080/00268976.2023.2232892 and 10.5281/zenodo.7888194), rovibrational energy levels computed from NOSL-296 EH model were adopted to match and replace ~100,000 <sup>14</sup>N<sub>2</sub><sup>16</sup>O levels. More consistent empirical corrections are determined for multiple isotopologues from comparison with RITZ (IAO), MARVEL (ExoMol), HITRAN, and JPL datasets. The ABG-IMRHT line list provides the most reliable and consistent IR intensity predictions and accurate line positions in the range of 0 - 10,000 cm<sup>-1</sup>. All 12 stable isotopologues are included. </p> <p><strong>2. Ames-2000K</strong> IR line list provides complete, reliable and consistent IR predictions in the 0 - 15,000 cm<sup>-1</sup> range. It includes transitions of 12 isotopologues. Their intensities are scaled by corresponding terrestrial "natural" abundances. Ames-2000K is a composite list, including 4 component lists, to achieve better accuracy and reliability needs at both shorter and longer wavelengths: </p> <ul> <li>#1. a hot line list of <sup>14</sup>N<sub>2</sub><sup>16</sup>O, computed on Ames-1 PES and 2023 dmsG-wgt2d, J'<210, E'<25,000 cm<sup>-1</sup>, T=1000 / 1500 / 2000 / 3000 K. It occupies 24 GB in compressed .xz format. </li> <li>#2. 1000 K line lists of #2-#12 minor isotopologues, computed on Ames-1 PES and DMS, J'<150, E'<0.125 au - zpe (iso 2-6) or 0.08 -0.10 au - zpe (iso 7-12), S<sub>1000K </sub>> 10<sup>-34</sup> cm/molecule, size-reduction with 99.9% intensity conservation in cm<sup>-1</sup> bins. </li> <li>#3. a hot line list of <sup>14</sup>N<sub>2</sub><sup>16</sup>O, computed on Ames-B1b PES and 2023 dmsG-10Kcm<sup>-1</sup>, J'<150, E'<16,000 cm<sup>-1</sup>, T=1000 / 1500 / 2000 / 3000 K.</li> <li>#4. ABG-IMRHT IR line list at 296 K, J'<150, E'<16,000 cm<sup>-1</sup>, with best empirical line positions and highly consistent intensity predictions up to 10,000 cm<sup>-1</sup>. Coverage beyond 10,000 cm<sup>-1 </sup>is limited to strong lines.</li> </ul> <p><strong>3. List of files:</strong> (decompress .xz files first, "xz -dkf -T0 file.xz")</p> <ul> <li>IAO_N2O_levels.tar.xz: rovibrational energy levels of 6 N<sub>2</sub>O isotopologues, as computed using global Effective Hamiltonian (EH) models developed by Dr. Sergey Tashkun from IAO (Institute of Atmospheric Optics, Tomsk, Russia, <a href="https://www.iao.ru/">https://www.iao.ru/</a>). <br><br></li> <li>N2O.Ames-B1b.PES.and.Ames-2023.DMS.zip: Ames-B1b PES subroutine & coefficient file, see the note in n2opes2.f90; Ames 2023 dmsG subroutine and coefficients for fits up to 10K/12K/15K/17K/20K/25K cm<sup>-1</sup>, along with fitting residuals and compared to Ames-1 style (dmsC) coeffs and residuals. The geometry set has ~80 points in each 100 cm<sup>-1</sup>. <br><br></li> <li>n2olist.f90.v1.4: the main Fortran program for Ames-2000K generation, customizable, see the note at its beginning. <br>[ default Ames-2000K = Ames-1 (12 iso) + [B1b (446) + ABG-IMRHT (12 iso)] if (E'<15,000 cm-1 & J<=150) ]<br><br></li> <li>empirical.corrections.for.ABG-IMRHT.zip: subroutine and paired/corrected energy level lists used in the empirical correction of energy levels computed on B1b PES [<em>this file is not updated from v1.1 to v2.0</em>]<br><br></li> <li>n2o.partition.1-4000K.iso.1-12.scaled: partition sum for 12 isotopologues, input file required by n2olist.f90, excluding g_n<br>n2o.partition.1-4000K.iso.1-12.scaled.with.degeneracy.txt: same as above, g_n included, see the note inside<br><br></li> <li>list.of.n2o.xz.files : list of .xz files to read/regenerate from, under subdir "xz", input file required by n2olist.f90<br>ames.n2o.xx000-yy000.cm-1.xz: compressed data files for Ames-2000K (line list component #1+#2)<br><br></li> <li>n2o.iso1-12.levels.Ames-1.dat.xz: energy levels of 12 N<sub>2</sub>O isotopologues on Ames-1 PES, input file required by n2olist.f90<br><br></li> <li>n2o.446.B1b-PES.levels.xz: energy levels of <sup>14</sup>N<sub>2</sub><sup>16</sup>O computed on Ames-B1b PES, input file to n2olist.f90<br><br></li> <li>n2o.446.B1b-dmsG10K.1000K-3000K.Eup16K.0-10Kcm-1.compressed.xz: data file for <sup>14</sup>N<sub>2</sub><sup>16</sup>O hot list on Ames-B1b and dms 2023-G10K (component #3), input file required by n2olist.f90<br>n2o.446.B1b-dmsG10K.1000K-3000K.Eup16K.0-10Kcm-1.dat.xz: independent line list (component #3)<br><br></li> <li>n2o.iso1-12.levels.ABG-IMRHT.dat.xz: energy levels in ABG-IMRHT list, with empirical corrections included, input file required by n2olist.f90</li> <li>n2o.296K.ABG-IMRHT.20250602.dat.v5.corrected.xz: Latest Ames-296K line list with empirical energy level corrections (component #4), input file for n2olist.f90<br>n2o.296K.ABG-IMRHT.20250602.with.broadening.dat.v5.corrected.xz: same as above, in HITRAN format, including line-broadening parameters<br><br></li> <li>n2o.296K.ABG-IMRHT.20250602.dat.v5corrected.100pct-abundance.xz: Latest Ames-296K line list with empirical energy level corrections, including the full set of IR transitions for <strong>12 stable isotopologues, assuming 100% abundannce </strong>for every isotopologue, and 1E-31 cm/molecule intensity cut-off at 296 K.</li> <li>n2o.296K.ABG-IMRHT.20250602.with.broadening.dat.v5corrected.100pct-abundance.xz: same as above, in HITRAN format, including line-broadening parameters<br><br></li> <li>ames.n2o.intensity.xz: line count and intensity sum (original, selected, iso #1 and iso #2-12) in each 0.01 cm<sup>-1</sup> bins at 296 K, 1000 K, 1500 K, 2000 K, and 3000 K, for reference and statistics, optional input file to n2olist.f90<br><br></li> <li>ames.n2o.sint.reductions.xz: A check-point file during Ames-2000K file generation, for reference only. including # of lines (total & selected), intensity sum (total, iso #1, iso #2-12), and intensity retention ratio in each 0.01 cm<sup>-1</sup> bins for total, iso #1, and iso #2-12.<br><br></li> </ul> <p><strong>4. List of 12 isotopologues</strong>, abundances adopted, and number of lines in ABG-IMRHT and Ames-2000K line list, <br>[updated 2025-06-17 v2.0: the #lines in ABG-IMRHT are updated to S(296K) cut-off at 1E-31 cm/molecule, instead of 5E-31]</p> <table> <tbody> <tr> <td>#</td> <td>ISO</td> <td>abundance</td> <td># lines in ABG-IMRHT</td> <td># lines in Ames-2000K</td> </tr> <tr> <td>1</td> <td>446</td> <td>0.990333</td> <td>1388017</td> <td>3014643103</td> </tr> <tr> <td>2</td> <td>456</td> <td>3.64093E-3</td> <td>375541</td> <td>97932924</td> </tr> <tr> <td>3</td> <td>546</td> <td>3.64093E-3</td> <td>411353</td> <td>118432059</td> </tr> <tr> <td>4</td> <td>448</td> <td>1.98582E-3</td> <td>377552</td> <td>88169721</td> </tr> <tr> <td>5</td> <td>447</td> <td>3.69280E-4</td> <td>238921</td> <td>40212389</td> </tr> <tr> <td>6</td> <td>556</td> <td>1.33858E-5</td> <td>93933</td> <td>7534736</td> </tr> <tr> <td>7</td> <td>548</td> <td>7.30080E-6</td> <td>93674</td> <td>5583963</td> </tr> <tr> <td>8</td> <td>458</td> <td>7.30080E-6</td> <td>86446</td> <td>4995485</td> </tr> <tr> <td>9</td> <td>547</td> <td>1.35765E-6</td> <td>55360</td> <td>2588347</td> </tr> <tr> <td>10</td> <td>457</td> <td>1.35765E-6</td> <td>50754</td> <td>2275332</td> </tr> <tr> <td>11</td> <td>558</td> <td>2.68412E-8</td> <td>15814</td> <td>500190</td> </tr> <tr> <td>12</td> <td>557</td> <td>4.99134E-9</td> <td>8537</td> <td>274623</td> </tr> <tr> <td> </td> <td>Total</td> <td>1.00000069</td> <td>3195902</td> <td>3383142872</td> </tr> </tbody> </table> <p>5. This project is funded by NASA Grant 18-APRA18-0013 through NASA/SETI Institute Co-operative Agreement 80NSSC20K1358. Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.</p>
Monthly and Annual contour lines of the zero and the positive maximum of the Wind Stress Curl over Western North Atlantic during 1980-2019 and the Gulf Stream path during 1993-2019.
<p>This dataset includes multiple fields: (i) files for monthly and annual fields for the max curl line and the zero curl line at 0.1 degree longitudinal resolutions; (ii) files for monthly and annual GS path obtained from Altimetry and originally processed by Andres (2016) at 0.1 degree longitudinal resolution. The maximum curl line (MCL) and the zero curl line (ZCL) calculations are briefly described here and are based on the original wind data (at 1.25 x 1.25 degree) provided by the Japanese reanalysis (JRA-55; Kobayashi et al., 2015) and available at https://zenodo.org/record/8200832 (Gifford et al. 2023). For details see Gifford, 2023. </p> <p>The wind stress curl (WSC) fields used for the MCL and ZCL calculations extend from 80W to 45W and 30N to 45N at the 1.25 by 1.25-degree resolution. The MCL is defined as the maximum WSC values greater than zero within the domain per 1.25 degree longitude. As such, it is a function of longitude and is not a constant WSC value unlike the zero contour. High wind stress curl values that occurred near the coast were not included within this calculation. After MCL at the 1.25 resolution was obtained the line was smoothed with a gaussian smoothing and interpolated on to a 0.1 longitudinal resolution. The smoothed MCL lines at 0.1 degree resolution are provided in separate files for monthly and annual averages (2 files). Similarly, 2 other files (monthly and annual) are provided for the ZCL. </p> <p>Like the MCL, the ZCL is a line derived from 1.25 degree longitude throughout the domain under the condition that it's the line of zero WSC. The ZCL is constant at 0 and does not vary spatially like the MCL. If there are more than one location of zero curl for a given longitude the first location south of the MCL is selected. Similar to the MCL, the ZCL was smoothed with a gaussian smoothing and interpolated on to a 0.1 longitudinal resolution. </p> <p>The above files span the years from 1980 through 2019. So, the monthly files have 480 months starting January 1980, and the annual files have 40 years of data. The files are organized with each row being a new time step and each column being a different longitude. Therefore, the monthly MCL and ZCL files are each 480 x 351 for the 0.1 resolution data. Similarly, the annual files are 40 x 351 for the 0.1 degree resolution data. </p> <p><strong>Note that the monthly MCLs and ZCLs are obtained from the monthly wind-stress curl fields. The annual MCLs and ZCLs are obtained from the annual wind-stress curl fields.</strong></p> <p>Since the monthly curl fields preserves more atmospheric mesoscales than the annual curl fields, the 12-month average of the monthly MCLs and ZCLs will not match with the annual MCLs and ZCLs derived from the annual curl field. The annual MCLs and ZCLs provided here are obtained from the annual curl fields and representative metrics of the wind forcing on an annual time-scale. </p> <p>Furthermore, the monthly Gulf Stream axis path (25 cm isoheight from Altimeter, reprocessed by Andres (2016) technique) from 1993 through 2019 have been made available here. A total of 324 monthly paths of the Gulf Stream are tabulated. In addition, the annual GS paths for these 27 years (1993-2019) of altimetry era have been put together for ease of use. The monthly Gulf Stream paths have been resampled and reprocessed for uniqueness at every 0.1 degree longitude from 75W to 50W and smoothed with a 100 km (10 point) running average via matlab. The uniqueness has been achieved by using Consolidator algorithm (D’Errico, 2023). </p> <p>Each monthly or annual GS path has 251 points between 75W to 50W at 0.1 degree resolution. </p>
Bonanza Creek LTER: Annual Active Layer Depths from 2002 to Present in the Survey Line Fire near Fairbanks, Alaska
In 2001 the Survey Line Fire burned an area of black spruce forest along the Tanana River adjacent to the Bonanza Creek Experimental Forest. In 2002 two research sites were established within the burn, one in a dry area and one in a wet area. When wildfire burns through a northern black spruce forest there is usually a subsequent increase in depth of thaw, due to the reduction in the depth of the organic layer. Thaw depth is being measured annually at tweny points within each of these sites.
Grassland Vegetation Line-Intercept Transects at the Sevilleta National Wildlife Refuge, New Mexico
In 1989, line-intercept transects were installed to evaluate temporal and spatial dynamics across vegetation transition zones. Currently, a 400m transect is sampled at a grassland site (Deep Well) which is dominated by Bouteloua eriopoda (black grama) and, near the southern end of the transect, B. gracilis (blue grama). A second grassland site (Five Points), dominated by B. eriopoda and, to the south, Larrea tridentata (creosote), is also sampled. Both sites are sampled twice a year, in May/June and September/October, and measurements are taken at a one-centimeter resolution. The biannual sampling protocol detects potential responses in both cool and warm season plants as well as pre- and post-monsoon dynamics. Several transects have been discontinued and data archived within SEV200.
Bulk and single-cell gene expression profiling of SARS-CoV-2 infected human cell lines identifies molecular targets for therapeutic intervention
<p>Single cell RNA seq datasets used for analysis in the Bulk and single-cell gene expression profiling of SARS-CoV-2 infected human cell lines identifies molecular targets for therapeutic intervention</p>
Production line dataset for task scheduling and energy optimization - Demand Response Participation
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> it was simulated an announcement of a demand response program at period 757, describing a demand response event from period 937 (Friday at 21:00h) to 960 (Friday at 23:00h) , where each period represents five minutes. The demand response program imposed a limit consumption, during its event, of 2.5 kWh. The announcement of the demand response allowed the use of the proposed solution to limit the energy consumption. For that, the algorithm described in section 3.3 was executed at period 769 (Friday at 7:00h).</p> <p>The API can be found at <<a href="http://www.gecad.isep.ipp.pt/api/spear/%3E">http://www.gecad.isep.ipp.pt/api/spear/</a>></p> <p>File Description:</p> <ul> <li>Input_JSON_Demand_Response_Optimization - JSON input data for the demand response participation</li> <li>Output_JSON_Demand_Response_Optimization - JSON output data for the demand response participation</li> <li>Output_Statistics_Demand_Response_Optimization - Excel output demand response participation statistics</li> <li>Comparison_Output_Statistics_Demand_Response - Excel output statistics comparing the before and after the demand response participation</li> </ul>
Production line dataset for task scheduling and energy optimization - Schedule Optimization
<p>The case study of this dataset uses real production data, provided by a textile company that manufactures hang tags. Their working schedule is from 7h00 of Monday to 23h00 of Saturday. This dataset uses a period of 5 minutes for all task durations and energy data. The case study considers a six-day period from 7h00 of Monday to 23h00 of Saturday. The scheduling algorithm was used for three machines that share the same cell.<br> <br> The API can be found at <http://www.gecad.isep.ipp.pt/api/spear/><br> <br> File Description:</p> <ul> <li>Input_JSON_Schedule_Optimization - JSON input data for the schedule optimization</li> <li>Output_JSON_Schedule_Optimization - JSON output data for the schedule optimization</li> <li>Output_Statistics_Schedule_Optimization - Excel output schedule optimization statistics</li> </ul>
Field line resonances observed by EMMA
<p>This data set contains the fundamental Field Line Resonance (FLR) frequencies estimated by spectral analysis of magnetic signals detected by four station pairs (SUW-BEL, TAR-BRZ, OUJ-HAN, MUO-PEL) of the European quasi-Meridional Magnetometer Array (EMMA), during the periods:</p> <p>2012/09/22-2012/12/01<br> 2013/03/13-2013/03/27<br> 2013/05/25-2013/06/11<br> 2014/02/14-2014/03/09<br> 2015/03/13-2015/03/31<br> 2015/06/18-2015/06/27<br> 2017/05/26-2017/06/02</p> <p>The original geomagnetic field data are available from <a href="https://zenodo.org/record/3387216">https://zenodo.org/record/3387216</a>, and information on the method used to derive the FLR frequencies are described by <a href="https://www.annalsofgeophysics.eu/index.php/annals/article/view/7751">Del Corpo et al., (2019)</a>.</p> <p>The assumptions made to derive the FLR frequencies could be not valid during nighttime, so the estimated frequencies could be unreliable. They are included in this data set to stimulate further investigation and validation. To facilitate the individuation of trusted frequencies, information about the sunrise and sunset time of the midpoint between the station pair and of the conjugate point is provided. The data set includes also the cross-phase spectra from which the FLR frequencies are derived.</p> <p> </p>
Introduction to Ancient Metagenomics Textbook (Edition 2025): Introduction to the Command Line
<p>Data and conda software environment file for the chapter 'Introduction to the Command Line' of the SPAAM Community's textbook: Introduction to Ancient Metagenomics (https://www.spaam-community.org/intro-to-ancient-metagenomics-book).</p>
Characterization of a loss-offunction NSF attachment protein beta mutation in monozygotic triplets affected with epilepsy and autism using cortical neurons from proband-derived and CRISPR-corrected induced pluripotent stem cell lines
<p>RNA-seq data of matured cortical neurons (8-weeks old) derived from the induced pluripoent stem cells (iPSC) of control parents (CtrlF and CtrlM) and corrected proband. There are three replicates (Rep1, Rep2, Rep3) for each sample with Forwad read (R1_001.fastq.gz)</p> <p>CtrlF: Control Father sample</p> <p>CtrlM: Control mother sample</p> <p>NDD_01_Corr_Het: Heterozygous correction of NAPB mutation (c.354+2T>G) in NDD_01 proband</p> <p>NDD_05_Corr_Hom: Homozygous correction of NAPB mutation (c.354+2T>G) in NDD_05 proband</p>
The data for "Accurate Infrared Line Lists for 20 Isotopologues of Carbon Disulfide (CS2) at Room Temperature"
<p>[<strong>Updates on 2025-03-02</strong>: energy levels and line lists of CS2 323 and 333 isotopologues are corrected; energy levels of 224, 223, and 232 isotopologues are extended to 0.1 au (ZPE included); partition function (Q) of first 4 isotopologues by direct summation up to 4000 K; number of 323 and 333 iso lines in natural line list are updated; natural line lists are updated]</p> <p>The paper was published online at <a href="https://iopscience.iop.org/article/10.3847/1538-4365/ad3809">ApJS</a> with open access to public, DOI: 10.3847/1538-4365/ad3809</p> <p>First-generation data product and IR line lists for Carbon Disulfide (CS2), including an isotopologue-independent <em>ab initio</em> PES of Carbon Disulfide refined with selected HITRAN energy levels below 7000 cm-1, an <em>ab initio </em>DMS fitted with CCSD(T)/aug-cc-pV(T/Q/5+d)Z dipoles computed up to 20,000 cm-1 above potential minimum and extrapolated to one-electron basis set limit, room temperature IR line lists for 20 individual isotopologues of 12/13C and 32/33/34/36S, denoted Ames-296K, and a "natural" CS2 list with intensities scaled by their terrestrial abundances. This project is funded by NASA Grant 18-2XRP18_2-0046 through NASA/SETI Institute Co-operative Agreement 80NSSC19M0121. See https://huang.seti.org/CS2/cs2.html for data format and abundance information. Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center. The line profile parameters and room temperature simulations are supported through 80NSSC20K1596.</p> <p><strong>List of Files</strong>, supplement to article " Accurate IR Line Lists for CS2 and Isotopologues at Room Temperature"</p> <ol> <li>Ames-1.PES.zip: Ames-0 and Ames-1 PES subroutine & coefficient files;<br>PES.refinement.files.zip: PES refinement related files including reference energy level list and refinement output.</li> <li><em>J</em>=0-200 energy level lists of 12C32S2 and 19 minor isotopologues, computed on the Ames-1 PES. The .zip file contains 20 compressed .tgz (or .xz) files, and partition function of 222, 224, 223, and 232 isotopologues.</li> <li>Ames-1.DMS.zip: Ames-1 DMS subroutine & coefficient files, and <em>ab initio</em> data;</li> <li>cs2.xxx.Ames-1.296K.1E-31.dat.tgz (or .xz) : 20 files, "xxx" is the S-C-S isotope mass unit number. These are the Ames-296K IR line lists for 12C32S2 and 19 minor isotopologues, each with 100% abundance. Computed using Ames-1 DMS and rovibrational wavefunctions for those energy levels acquired on Ames-1 PES;</li> <li>cs2.20iso.Ames.natural.296K.1E-31.10Kcm-1.dat.updated.tgz: A "natural" Ames-296K IR line list for CS2, including 10,018,977 transitions from all 20 isotopologues with their 296K intensities scaled by terrestrial abundances, covering the range of 0 - 10,000 cm-1. Computed on the Ames-1 PES and DMS.</li> <li>cs2.222.A+I.296K.ames+heff.natural.tgz: A(mes)+I(AO).296K line list for the main isotopologue 222, with terrestrial abundance. Ames-296K intensity prediction is combined with the more accurate energy levels (and line positions) from Effective Hamiltonian model.</li> <li>cs2.iso2-20.Ames-1.natural.1E-31.dat.iso2-4_use_HITRAN2020_purified.v5.xz: the Ames "natural" line list for minor isotopologues #2 - #20, in which the energy levels of 224, 223 and 232 are replaced with reliable values in HITRAN2020. Therefore, the final composite line list = 6) + 7)</li> <li>Heff.and.HITRAN.energy.level.matches.and.line.list.update.zip: the short FORTRAN programs for energy level matches between Ames-1 PES levels and Heff model levels, and the subroutines to use Heff and HITRAN energy levels and line positions. Lists of matched Ames vs HITRAN/H_eff levels are also included.</li> <li>ORIGIN project file for related analysis and figures. Use Origin Viewer to open on PC and MAC, <a href="https://www.originlab.com/viewer/dl.aspx">https://www.originlab.com/viewer/dl.aspx</a></li> <li>a Python program to generate line-broadening parameters for rovibrational CS2 molecule</li> <li>CS2 cross-section data of PNNL, HITRAN and Ames line lists. </li> </ol> <p><strong> # Iso #Lines #in"natural" abundance </strong><br> 1 222 1,903,882 1,856,648 0.892811 <br> 2 224 3,983,009 2,159,579 0.0792103 <br> 3 223 3,745,299 1,328,631 0.0140944 <br> 4 232 1,925,377 658,645 0.100306 <br> 5 424 1,940,490 439,254 1.207E-3 <br> 6 234 4,211,645 698,654 8.151E-4 <br> 7 324 3,671,708 589,323 6.510E-4 <br> 8 226 4,155,423 558,540 3.566E-4 <br> 9 233 3,918,753 410,542 1.439E-4 <br>10 323 1,937,950 290,799 5.142E-5 <br>11 434 2,080,912 138,209 1.692E-5 <br>12 426 3,852,415 223,268 1.976E-5 <br>13 334 4,062,765 172,858 6.773E-6 <br>14 236 4,742,198 158,941 4.630E-6 <br>15 326 4,113,292 137,128 3.075E-6 <br>16 333 1,988,991 80,862 6.803E-7 <br>17 436 4,434,008 58,478 1.361E-7 <br>18 626 1,986,823 21,692 3.572E-8 <br>19 336 4,613,173 32,681 3.528E-8 <br>20 636 2,194,332 4,245 4.28E-10 </p> <p><strong>Line List Data Format: </strong>(CS2 is the 53rd molecule in HITRAN, we use iso# from table below, e.g., 1 - 222; 2 - 224; ...; 10 - 323; ....; 20 - 636)</p> <ol> <li>in the original line list files: cs2.xxx.Ames-1.296K.1E-31.dat<br>iso wavenumber S(Ames) A21(Ames) E"(cm-1) <em>v1v2l2v3' v1v2l2v3" JPS' #root' JPS" #root" J' J" e/f</em>_symmetry<br> 2 6.165375 1.492856E-30 4.851961E-12 876.91172 0 2 2 0 0 2 2 0 29 1 2 4 28 2 2 4 29 28 e e</li> <li>in cs2.iso2-20.Ames-1.natural.1E-31.dat.iso2-4_use_HITRAN2020_purified.v5, original Ames-1 line position and the difference = Heff - Ames are appended to the end of each line of iso #2 (224), iso #3 (223), and iso #4 (232). </li> <li>in cs2.222.AI-296K.ames+heff.natural.dat.v2, two integers are added to each line to keep the record for the number of cycles after which a match (or no match) was made for upper and lower levels, "0-41" for "matched", '99' for "not matched", "-1" for out of range, i.e. > 9000 cm-1. The differences between the original Ames and corrected/replaced transition wavenumber, E', and E" are also appended at the end. The relation is wv/E'/E" (Heff) + diff = wv/E'/E" (Ames). For example, in the transition below, E''(Ames) = 3445.4018+0.7865 = 3446.1883 cm-1. <br><em> 1 36.666580 1.538246E-31 2.306988E-07 3445.40177 0 4 2 1 1 6 2 0 57 1 2 31 58 2 2 34 57 58 e e 3 3 -0.7207 0.0658 0.7865</em></li> </ol> <p> </p>
Artificial Intelligence for Quality Control of manufacturing operations: Macro-mechanical milling in the Pilot Line GAMHE 5.0.
<p>Quality is defined as the extent to which a product conforms to the design specifications and how it complies with the requirements of component functionality. For some industries, such as automotive and aeronautical, the quality of their parts is very important given the high requirements to which they are subject. However, difficulties arise from the fact that a measure of quality can only be evaluated ‘‘out-of-process”, resulting in losses because there is no alternative to removing defective parts from the production line. Therefore, it is necessary to apply Artificial Intelligence-based kits/solutions that provide in-process estimation to predict quality from some measured variables. </p> <p>The main goal of these datasets is to monitor the final quality of the manufactured components or parts by estimating surface roughness from vibration signals and cutting parameters information using Artificial Intelligence-based solutions. Surface roughness is an essential feature in quality control defined by the deviation in the direction of the normal vector of a real surface from its ideal form. Because the roughness measurement is an offline and post process procedure, being able to estimate this value online brings a series of benefits in terms of time and cost reduction in manufacturing lines, energy efficiency, unnecessary wear of tools and machines, etc. Once a part has been detected with a surface quality below what is desired, a series of corrective measures can be applied for the following operations, such as: reducing the feed rate percentage, increasing the percentage of spindle speed or reducing the axial depth per pass, etc.</p>
Artificial Intelligence for quality control in manufacturing operations: Micro-mechanical milling in the Pilot Line GAMHE 5.0
<p>Quality is defined as the extent to which a product conforms to the design specifications and how it complies with the requirements of component functionality. For some industries, such as automotive and aeronautical, the quality of of manufactured parts is very important due to the high requirements. However, difficulties arise from the fact that a measure of quality can only be evaluated ‘‘out-of-process”, resulting in losses because there is no alternative to removing defective parts from the production line. Therefore, it is necessary to incorporate AI-based kits/solutions that provide in-process estimation to predict quality from some measured variables.</p> <p>The main goal of these datasets is to enable monitoring of final quality of the manufactured components or parts by estimating surface roughness from vibration signals and cutting parameters information. Surface roughness is an essential feature in quality control defined by the deviation in the direction of the normal vector of a real surface from its ideal form. Because the roughness measurement is an offline and post process procedure, being able to estimate this value online brings a series of benefits in terms of time and cost reduction in manufacturing lines, energy efficiency, unnecessary wear of tools and machines, etc. Once a part has been detected with a surface quality below what is desired, a series of corrective measures can be applied for the following operations, such as: reducing the feed rate percentage, increasing the percentage of spindle speed or reducing the axial depth per pass, etc.</p> <p>Workstation 4 (WS4) of the GAMHE 5.0 pilot line is a Kern Evo high-precision machining centre, with a maximum spindle speed of 50 000 rpm and Blum laser system and is used to run micro-milling and micro-drilling operations. In this experimental dataset, five cutting parameters were considered in the processes: spindle speed, <em>n</em>; feed rate, <em>f</em>; and axial depth of cut, <em>a<sub>P</sub></em>. The radial depth of cut, <em>a<sub>e</sub></em>; was equal to the mill tool radius, <em>r</em>, in all of the slots.</p> <p>These experiments were micro-milling operations with 0.3 mm, 0.5 mm, 0.8 mm and 1 mm-diameter mills on a sintered tungsten-copper alloy (W78Cu22). The data collected for each micro milling operation was the rms and peak value of the vibrations in the three-machine axis. In addition, five cutting parameters were also collected: position in <em>X</em> of the last point of the sample, feed rate, spindle speed, tool radius and axial depth.</p>
DATASET: characterization of the seed coat extractable phenolic profile and color in 308 common bean lines of the Spanish Diversity Panel
<p>Characterizarion of the seed coat extractable phenolic profile and color in 308 common bean lines of the Spanish Diversity Panel</p>
Shipboard Conductivity–Temperature–Depth (CTD) and dissolved oxygen profile data collected during hypoxia surveys along six hydrographic sampling lines within Olympic Coast National Marine Sanctuary, 2004–2015
<p>This data set includes Conductivity-Temperature-Depth (CTD) and dissolved oxygen profile data that were collected along Washington State’s outer coast within Olympic Coast National Marine Sanctuary (OCNMS). Measurements were made along six cross-shelf hydrographic sampling lines during a series of hypoxia survey cruises from 2004 – 2015. The 398 CTD profiles were acquired using Sea-Bird Scientific 19 SeaCAT or 19plus SeaCAT CTD profilers with associated SBE-43 (Sea-Bird Electronics) or Beckman or YSI-type (Yellow Springs Instruments) dissolved oxygen sensors. The data were processed via Sea-Bird Scientific’s SBE Data Processing application using six of the modules in the following order: Data Conversion, Filter, Align CTD, Loop Edit, Derive, and Bin Average. These processing steps and associated methods are the same as those used to process CTD data collected during OCNMS mooring maintenance cruises (<a href="https://www.sciencedirect.com/science/article/pii/S2352340924001422">Risien et al., 2024</a>) and along the Newport Hydrographic Line (<a href="https://www.sciencedirect.com/science/article/pii/S2352340922001342">Risien et al., 2022</a>) located off the central Oregon coast.</p> <table> <tbody> <tr> <td><strong>Station Name </strong></td> <td><strong>Latitude</strong></td> <td><strong>Longitude</strong></td> <td><strong>Water Depth (m, MLLW)</strong></td> </tr> <tr> <td><strong>Cape Alava (CA)</strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>CA010</td> <td>48.1661oN</td> <td>124.7540oW</td> <td>10</td> </tr> <tr> <td>CA020</td> <td>48.1661oN</td> <td>124.7598oW</td> <td>20</td> </tr> <tr> <td>CA030</td> <td>48.1659oN</td> <td>124.7783oW</td> <td>30</td> </tr> <tr> <td>CA040</td> <td>48.1659oN</td> <td>124.7852oW</td> <td>40</td> </tr> <tr> <td>CA045</td> <td>48.1659oN</td> <td>124.8335oW</td> <td>45</td> </tr> <tr> <td>CA050</td> <td>48.1658oN</td> <td>124.8578oW</td> <td>50</td> </tr> <tr> <td>CA060</td> <td>48.1659oN</td> <td>124.8843oW</td> <td>60</td> </tr> <tr> <td>CA070</td> <td>48.1655oN</td> <td>124.9011oW</td> <td>70</td> </tr> <tr> <td>CA080</td> <td>48.1657oN</td> <td>124.9141oW</td> <td>80</td> </tr> <tr> <td>CA090</td> <td>48.1659oN</td> <td>124.9247oW</td> <td>90</td> </tr> <tr> <td>CA100</td> <td>48.1658oN</td> <td>124.9319oW</td> <td>100</td> </tr> <tr> <td><strong>Teahwhit Head (TH)</strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>TH030</td> <td>47.8759oN</td> <td>124.6481oW</td> <td>30</td> </tr> <tr> <td>TH035</td> <td>47.8761oN</td> <td>124.7024oW</td> <td>35</td> </tr> <tr> <td>TH040</td> <td>47.8760oN</td> <td>124.7281oW</td> <td>40</td> </tr> <tr> <td>TH050</td> <td>47.8761oN</td> <td>124.7567oW</td> <td>50</td> </tr> <tr> <td>TH060</td> <td>47.8765oN</td> <td>124.7822oW</td> <td>60</td> </tr> <tr> <td>TH070</td> <td>47.8765oN</td> <td>124.8084oW</td> <td>70</td> </tr> <tr> <td>TH080</td> <td>47.8768oN</td> <td>124.8415oW</td> <td>80</td> </tr> <tr> <td>TH090</td> <td>47.8769oN</td> <td>124.8868oW</td> <td>90</td> </tr> <tr> <td>TH100</td> <td>47.8769oN</td> <td>124.9182oW</td> <td>100</td> </tr> <tr> <td><strong>Hoh Head (HH)</strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>HH025</td> <td>47.7688oN</td> <td>124.5605oW</td> <td>25</td> </tr> <tr> <td>HH042</td> <td>47.7688oN</td> <td>124.6428oW</td> <td>42</td> </tr> <tr> <td>HH065</td> <td>47.7688oN</td> <td>124.7401oW</td> <td>65</td> </tr> <tr> <td><strong>Raft River (RR)</strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>RR015</td> <td>47.4632oN</td> <td>124.3748oW</td> <td>15</td> </tr> <tr> <td>RR020</td> <td>47.4644oN</td> <td>124.4510oW</td> <td>20</td> </tr> <tr> <td>RR042</td> <td>47.4632oN</td> <td>124.5199oW</td> <td>42</td> </tr> <tr> <td>RR065</td> <td>47.4629oN</td> <td>124.6074oW</td> <td>65</td> </tr> <tr> <td><strong>Cape Elizabeth (CE)</strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>CE010</td> <td>47.3541oN</td> <td>124.3347oW</td> <td>10</td> </tr> <tr> <td>CE020</td> <td>47.354oN</td> <td>124.3608oW</td> <td>20</td> </tr> <tr> <td>CE030</td> <td>47.3538oN</td> <td>124.3913oW</td> <td>30</td> </tr> <tr> <td>CE040</td> <td>47.3534oN</td> <td>124.4678oW</td> <td>40</td> </tr> <tr> <td>CE050</td> <td>47.3532oN</td> <td>124.5064oW</td> <td>50</td> </tr> <tr> <td>CE060</td> <td>47.3529oN</td> <td>124.5510oW</td> <td>60</td> </tr> <tr> <td>CE070</td> <td>47.3528oN</td> <td>124.5823oW</td> <td>70</td> </tr> <tr> <td>CE080</td> <td>47.3527oN</td> <td>124.6158oW</td> <td>80</td> </tr> <tr> <td>CE090</td> <td>47.3526oN</td> <td>124.6491oW</td> <td>90</td> </tr> <tr> <td>CE100</td> <td>47.3522oN</td> <td>124.6754oW</td> <td>100</td> </tr> <tr> <td><strong>Moclips (MO)</strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>MO010</td> <td>47.2214oN</td> <td>124.2394oW</td> <td>10</td> </tr> <tr> <td>MO015</td> <td>47.2214oN</td> <td>124.2599oW</td> <td>15</td> </tr> <tr> <td>MO020</td> <td>47.2214oN</td> <td>124.2791oW</td> <td>20</td> </tr> <tr> <td>MO030</td> <td>47.2195oN</td> <td>124.3347oW</td> <td>30</td> </tr> <tr> <td>MO042</td> <td>47.2195oN</td> <td>124.3958oW</td> <td>42</td> </tr> </tbody> </table>
Supplementary material for the paper "DS Andromedae, A Detached Eclipsing Double-Lined Spectroscopic Binary in the Galactic Cluster NGC 752
<p>Supplementary material supporting the paper "DS Andromedae: A Detached Eclipsing Double-Lined Spectroscopic</p> <p>Binary in the Galactic Cluster NGC 752" by E. F. Milone, S. J. Schiller, Th. Mellergaard Amby, and S. Frandsen.</p> <p>It includes:</p> <p>A Read-me file in three formats (docx, rtf, pdf); Unabridged Section 3 with extended modeling details (pdf);</p> <p>Extended spreadsheet version of Table 3 of adjusted parameters (pdf); Extended spreadsheet version of Table 8 of absolute</p> <p>parameters (pdf); and Complete Table 15 of photometric data (txt): and a sample DC input file (for Model 41, used in the</p> <p>DS And modeling) in dat format.</p>
Three Annotated Anomaly Detection Datasets for Line-Scan Algorithms
<h1>Summary</h1> <p>This dataset contains two hyperspectral and one multispectral anomaly detection images, and their corresponding binary pixel masks. They were initially used for real-time anomaly detection in line-scanning, but they can be used for any anomaly detection task.</p> <p>They are in .npy file format (will add tiff or geotiff variants in the future), with the image datasets being in the order of (height, width, channels). The SNP dataset was collected using sentinelhub, and the Synthetic dataset was collected from AVIRIS. The Python code used to analyse these datasets can be found at: https://github.com/WiseGamgee/HyperAD</p> <h1>How to Get Started</h1> <p>All that is needed to load these datasets is Python (preferably 3.8+) and the NumPy package. Example code for loading the Beach Dataset if you put it in a folder called "data" with the python script is:</p> <pre><code>import numpy as np # Load image file hsi_array = np.load("data/beach_hsi.npy") n_pixels, n_lines, n_bands = hsi_array.shape print(f"This dataset has {n_pixels} pixels, {n_lines} lines, and {n_bands}.") # Load image mask mask_array = np.load("data/beach_mask.npy") m_pixels, m_lines = mask_array.shape print(f"The corresponding anomaly mask is {m_pixels} pixels by {m_lines} lines.")</code></pre> <h1>Citing the Datasets</h1> <p>If you use any of these datasets, please cite the following paper:</p> <pre><code>@article{garske2024erx,</code><br><code> title={ERX - a Fast Real-Time Anomaly Detection Algorithm for Hyperspectral Line-Scanning},</code><br><code> author={Garske, Samuel and Evans, Bradley and Artlett, Christopher and Wong, KC},</code><br><code> journal={arXiv preprint arXiv:2408.14947},</code><br><code> year={2024},</code><br><code>}</code></pre> <div> <pre>If you use the beach dataset please cite the following paper as well (original source):</pre> </div> <pre><code>@article{mao2022openhsi, title={OpenHSI: A complete open-source hyperspectral imaging solution for everyone}, author={Mao, Yiwei and Betters, Christopher H and Evans, Bradley and Artlett, Christopher P and Leon-Saval, Sergio G and Garske, Samuel and Cairns, Iver H and Cocks, Terry and Winter, Robert and Dell, Timothy}, journal={Remote Sensing}, volume={14}, number={9}, pages={2244}, year={2022}, publisher={MDPI} }</code></pre>
Observation files of CG-5 gravity meters for the Zhetygen calibration line for five years
<p>Observation files of three Scintrex CG-5 gravimeters obtained during six field campaigns for calibration of these meters. Measurements were carried out at seven points of the Zhetygen calibration line, located 20 km north of Almaty in Kazakhstan. Also attached is a file with the coordinates of the stations.</p>
HI line observations of 290 evolved stars made with the Nancay Radio Telescope - I. Data: online Tables
<p>--- Table B.1: Clear NRT HI detections - basic data </p> <p>Description of the columns:</p> <p>(1) Name: common catalogue name of the target. <br> An ^n after a name indicates that it is clearly not an AGB star, <br> a ^d that we consider its classification as an AGB to be dubious, <br> and a ^* indicates that notes on the object can be found in Appendix A;<br>(2,3) RA,DEC: literature right ascension and declination of the target from Gaia EDR 3, <br> for epoch J2000.0;<br>(4) Type: target type. <br> Primarily the variability type as listed in Version 5.1 of the General Catalogue of Variable Stars, <br> GCVS (A description of GCVS types is given in https://cdsarc.u-strasbg.fr/ftp/cats/B/gcvs/vartype.txt),<br> but if an object is not included in the GCVS, other identifiers are listed in brackets: <br> HPM = high proper motion star, (OH/IR) = OH/IR maser, LPVc = long-period variable candidate, <br> PN = planetary nebula, pPN = proto-planetary nebula, and post-AGB star;<br>(5,6) Spec & ref: spectral type of the star, followed by its literature reference, <br> as retrieved from the SIMBAD database. If none was listed there, the reference is noted as 'SIMBAD';<br>(7,8) Teff & ref: effective temperature of the star, in K, followed by its literature reference; <br>(9) d: distance of the target, based on its parallax (mainly from the Gaia EDR3), in pc.<br> If no Gaia parallax was available a reference to the distance we adopted is given in Appendix A<br> (for RAFGL 3099, mu Cep, and V Peg);<br>(10) Vlit: published radial velocity of the target in the LSR reference frame, in km/s;<br>(11) Vexp: literature expansion velocity measured from CO or OH 1612 MHz line observations, in km/s. <br> If a pair of values was published for a two-velocity component CO line fit, the largest value is listed here;<br>(12) ref: literature references to the published Vlit and Vexp values; <br>(13) line: spectral line on which the published radial velocity measurement (Vlit) was based;<br>(14,15) Mdot & ref: literature mass loss rates, in solar masses per year, <br> followed by its literature reference.</p> <p>Notes to Table B.1:</p> <p>References: see Table B.1 in the Astronomy & Astrophysics paper.</p> <p><br>--- Table B.2: Clear NRT HI detections - HI data </p> <p>Description of the columns:</p> <p>(1) Name: common catalogue name of the target. <br> A ^T after a name indicates that HI line parameters are based on a 'total' spectrum, whereas <br> a ^P indicates that a 'peak' spectrum was used. <br> An ^n indicates that it is clearly not an AGB star, <br> a ^d that we consider its classification as an AGB to be dubious, <br> and a ^* indicates that notes on the object can be found in Appendix A;<br>(2) VHI: our central radial velocity in the LSR reference frame of the Gaussian fitted <br> to the HI profile, in km/s.<br>(3) FWHM: our full width half maximum of the Gaussian fitted to the HI line profile, in km/s;<br>(4) Speak: our peak flux density of the HI line profile, in Jy;<br>(5) diam: our estimated angular size of the HI CSE in the east-west direction, in arcmin;<br>(6) FHI: our integrated line flux of the HI profile, in Jy km/s;<br>(7) MHI: our total HI mass, in Msun;<br>(8) HI ref: references to previously published HI studies,<br> see Table B.2 in the Astronomy & Astrophysics paper.</p> <p><br>--- Table B.3: Possible NRT HI detections - basic data</p> <p>Description of the columns:</p> <p>(1) Name: common catalogue name of the target. <br> An ^n after a name indicates that it is clearly not an AGB star, <br> a ^d that we consider its classification as an AGB to be dubious, <br> and a ^* indicates that notes on the object can be found in Appendix A;<br>(2,3) RA,DEC: literature right ascension and declination of the target from Gaia EDR 3, <br> for epoch J2000.0;<br>(4) Type: target type. <br> Primarily the variability type as listed in Version 5.1 of the General Catalogue of Variable Stars, <br> GCVS (A description of GCVS types is given in https://cdsarc.u-strasbg.fr/ftp/cats/B/gcvs/vartype.txt),<br> but if an object is not included in the GCVS, other identifiers are listed in brackets: <br> HPM = high proper motion star, (OH/IR) = OH/IR maser, LPVc = long-period variable candidate, <br> PN = planetary nebula, pPN = proto-planetary nebula, and post-AGB star;<br>(5,6) Spec & ref: spectral type of the star, followed by its literature reference, <br> as retrieved from the SIMBAD database. If none was listed there, the reference is noted as 'SIMBAD';<br>(7,8) Teff & ref: effective temperature of the star, in K, followed by its literature reference; <br>(9) d: distance of the target, based on its parallax (mainly from the Gaia ED33), in pc.<br> If no Gaia parallax was available a reference to the distance we adopted is given in Appendix A<br> (for RAFGL 3099, mu Cep, and V Peg);<br>(10) Vlit: published radial velocity of the target in the LSR reference frame, in km/s;<br>(11) Vexp: literature expansion velocity measured from CO or OH 1612 MHz line observations, in km/s. <br> If a pair of values was published for a two-velocity component CO line fit, the largest value is listed here;<br>(12) Mdot : literature mass loss rates, in solar masses per year, <br>(13) line: spectral line on which the published radial velocity measurement (Vlit) was based;<br>(14) ref: literature references to the published Vlit, Vexp and Mdot values; </p> <p>Notes to Table B.3:</p> <p>References: see Table B.1 in the Astronomy & Astrophysics paper.</p> <p><br>--- Table B.4: Possible NRT HI detections - HI data</p> <p>Description of the columns:</p> <p>See the description of the columns of Table B.2.</p> <p><br>--- Online only Table 5: Upper limits to NRT HI lines </p> <p>Description of the columns:</p> <p>(1) Name: common catalogue name of the target. <br> An ^n after a name indicates that it is clearly not an AGB star, <br> a ^d that we consider its classification as an AGB to be dubious, <br> and a ^* indicates that notes on the object can be found in Appendix A;<br>(2,3) RA,DEC: literature right ascension and declination of the target from Gaia EDR 3, <br> for epoch J2000.0;<br>(4) Type: target type. <br> Primarily the variability type as listed in Version 5.1 of the General Catalogue of Variable Stars, <br> GCVS (A description of GCVS types is given in https://cdsarc.u-strasbg.fr/ftp/cats/B/gcvs/vartype.txt),<br> but if an object is not included in the GCVS, other identifiers are listed in brackets: <br> HPM = high proper motion star, (OH/IR) = OH/IR maser, LPVc = long-period variable candidate, <br> PN = planetary nebula, pPN = proto-planetary nebula, and post-AGB star;<br>(5,6) spec & ref: spectral type of the star, followed by its literature reference, <br> as retrieved from the SIMBAD database. If none was listed there, the reference is noted as 'SIMBAD';<br>(7,8) Teff & ref: effective temperature of the star, in K, followed by its literature reference; <br>(9) d: distance of the target, based on its parallax (mainly from the Gaia EDR3, in pc.<br> If no Gaia parallax was available a reference to the distance we adopted is given in Appendix A<br> (for RAFGL 3099, mu Cep, and V Peg);<br>(10) Vlit: published radial velocity of the target in the LSR reference frame, in km/s;<br>(11) Vexp: literature expansion velocity measured from CO or OH 1612 MHz line observations, in km/s. <br> If a pair of values was published for a two-velocity component CO line fit, the largest value is listed here;<br>(12) Mdot : literature mass loss rates, in solar masses per year, <br>(13) ref: literature references for Vlit, Vexp and Mdot, as applicable;<br>(14) line: spectral line on which the published radial velocity measurement (Vlit) was based;<br>(15) Speak: peak flux density of our HI line profile, in Jy;<br>(16) notes: 'old data' indicates objects observed only in 1992/1993, before the renovation of the NRT; <br> 'blue/red side' indicates that either the blue or red side of the HI profile could be used to measure <br> an upper limit to the line flux;<br>(17) HI ref: references to previously published HI studies;</p> <p>Notes to online only Table 5: </p> <p>HI references: see Table B.2 in the Astronomy & Astrophysics paper.<br>Other references: see Table B.1 in the Astronomy & Astrophysics paper.</p> <p><br>--- Online only Table 6: Confused NRT HI spectra</p> <p>Description of the columns:</p> <p>(1) Name: common catalogue name of the target. <br>(2,3) RA, DEC: literature right ascension and declination of the target from Gaia EDR3, <br> for epoch J2000.0; <br>(4) type: target type. Primarily the variability type as listed in Version 5.1 of the <br> General Catalogue of Variable Stars, GCVS; but if an object is not included in the GCVS, <br> other identifiers are listed in brackets: HPM = high proper motion star, (OH/IR) = OH/IR maser, <br> LPVc = long-period variable candidate, PN = planetary nebula, pPN = proto-planetary nebula, <br> and post-AGB star; <br>(5,6) spec & ref spectral type of the star, followed by its literature reference, <br> as retrieved from the SIMBAD database. If none was listed there, the reference is noted as 'SIMBAD'; <br>(7,8) Vlit & ref: published radial velocity of the target in the LSR reference frame, in km/s; <br>(9) line: spectral line on which the published radial velocity measurement (Vlit) was based; <br>(10) notes: 'old data' denotes objects observed only in 1992/1993, before the renovation of the NRT<br> (see Section 3), for which no observations in digital form are available; <br>(11) HI ref: references to previously published HI studies.</p> <p>Notes to online only Table 6:</p> <p>HI references: see Table B.2 in the Astronomy & Astrophysics paper.<br>Other references: see Table B.1 in the Astronomy & Astrophysics paper.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.