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Mendeley readers
Attention Score in Context
| Title |
LIMS for Lasers 2015 for achieving long‐term accuracy and precision of δ2H, δ17O, and δ18O of waters using laser absorption spectrometry
|
|---|---|
| Published in |
Rapid Communications in Mass Spectrometry, October 2015
|
| DOI | 10.1002/rcm.7372 |
| Pubmed ID | |
| Authors |
Tyler B Coplen, Leonard I Wassenaar |
| Abstract |
Although laser absorption spectrometry (LAS) instrumentation is easy to use, its incorporation into laboratory operations is not easy, owing to extensive offline manipulation of comma-separated-values files for outlier detection, between-sample memory correction, nonlinearity (δ-variation with water amount) correction, drift correction, normalization to VSMOW-SLAP scales, and difficulty in performing long-term QA/QC audits. A Microsoft Access relational-database application, LIMS (Laboratory Information Management System) for Lasers 2015, was developed. It automates LAS data corrections and manages clients, projects, samples, instrument-sample lists, and triple-isotope (δ(17) O, δ(18) O, and δ(2) H values) instrumental data for liquid-water samples. It enables users to (1) graphically evaluate sample injections for variable water yields and high isotope-delta variance; (2) correct for between-sample carryover, instrumental drift, and δ nonlinearity; and (3) normalize final results to VSMOW-SLAP scales. Cost-free LIMS for Lasers 2015 enables users to obtain improved δ(17) O, δ(18) O, and δ(2) H values with liquid-water LAS instruments, even those with under-performing syringes. For example, LAS δ(2) HVSMOW measurements of USGS50 Lake Kyoga (Uganda) water using an under-performing syringe having ±10 % variation in water concentration gave +31.7 ± 1.6 ‰ (2-σ standard deviation), compared with the reference value of +32.8 ± 0.4 ‰, after correction for variation in δ value with water concentration, between-sample memory, and normalization to the VSMOW-SLAP scale. LIMS for Lasers 2015 enables users to create systematic, well-founded instrument templates, import δ(2) H, δ(17) O, and δ(18) O results, evaluate performance with automatic graphical plots, correct for δ nonlinearity due to variable water concentration, correct for between-sample memory, adjust for drift, perform VSMOW-SLAP normalization, and perform long-term QA/QC audits easily. Published in 2015. This article is a U.S. Government work and is in the public domain in the USA. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 36 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 17% |
| Student > Ph. D. Student | 4 | 11% |
| Student > Master | 3 | 8% |
| Student > Doctoral Student | 2 | 6% |
| Student > Bachelor | 2 | 6% |
| Other | 5 | 14% |
| Unknown | 14 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Environmental Science | 4 | 11% |
| Earth and Planetary Sciences | 4 | 11% |
| Medicine and Dentistry | 2 | 6% |
| Engineering | 2 | 6% |
| Physics and Astronomy | 2 | 6% |
| Other | 5 | 14% |
| Unknown | 17 | 47% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 October 2015.
All research outputs
#16,047,334
of 25,374,647 outputs
Outputs from Rapid Communications in Mass Spectrometry
#3,002
of 4,966 outputs
Outputs of similar age
#155,003
of 291,306 outputs
Outputs of similar age from Rapid Communications in Mass Spectrometry
#8
of 65 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,966 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 291,306 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 65 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.