This record contains the data and R code for the paper "Radiocarbon evidence that tree mycorrhizal type modulates mean soil carbon age across depths but not organic matter fractions".Soils were sampled in June 2021 in Yale-Myers Forest, Connecticut, USA. Each of three stands contained one arbuscular mycorrhizal (AM)-dominated plot and one ectomycorrhizal (EcM)-dominated plot. Each plot contained two subplots, one with and one without the ericoid mycorrhizal shrub. In each subplot, three horizons were sampled: the Oa; the surface mineral horizon, 0–10 cm below the Oa; and the subsurface mineral horizon, to 30 cm cumulative depth including the Oa. Each sample was separated into particulate (POM, >53 µm) and mineral-associated (MAOM, <53 µm) organic matter. This gives 72 fraction-level observations from 36 soil samples.For each fraction: Δ14C, δ13C, δ15N, C and N concentrations (within the fraction and on a whole-soil basis), and C:N ratio. For each bulk soil sample: pH and 14-day C mineralisation. For each plot: tree basal area and the share of AM and EcM trees. For each subplot: soil moisture and C-stock-weighted whole-profile Δ14C. The data also include summary statistics of radiocarbon analytical uncertainty, standards and blanks. data_dictionary.csv defines every column, with units and derivations.run_all.R runs the full workflow in about 1 minute and writes every table, figure, fitted model and log to output/. It reproduces data-based figures and tables in the paper and its supplementary information. The deposited output/ was produced with R 4.4.1, and the workflow was also checked with R 4.3.3. README.md describes the sampling design, models, file layout and how to check a run. We used the large language model Claude to assist with code workflow editing during revisions; all code, analyses, and outputs were reviewed by the authors.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
The ongoing work building a Raspberry Pi cluster consisting of 300 nodes is presented, with potential use cases being an inexpensive and green test bed for cloud computing research and a robust and mobile data center for operating in adverse environments.
P. Abrahamsson, S. Helmer, Nattakarn Phaphoom et al.· IEEE International Conferenc...· 110 citations· ⚡7
The results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains, and 219 factors representing causes of unhappiness while developing software are identified.
D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.· International Conference on...· 84 citations· ⚡6
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026