I brought a large (6.2K) library from papers2 into Paperpile. I number of pdfs with well-defined ris entries lost their metadata and are marked as “incomplete”. This was especially true with books.

Paperpile should really have a “get metadata from url” or some other method for updating these (besides the long-form, manual grid to fill in). I have about 120 of these and there doesn’t seem to be a way to auto-update them effectively

That’s strange. If the meta data is present in the RIS file exported from Papers2 then Paperpile should pick it up readily. Could you please paste some of the entries that were not fully transfered?

Here is an example of a book where the PDF was imported but none of the metadata:

TY - BOOK
ID - 21659
BT - Introduction to Probability Simulation and Gibbs Sampling with R
A1 - Suess, Eric A
A1 - Trumbo, Bruce E
Y1 - 2010/00/15
N2 - The first seven chapters use R for probability simulation and computation, including random number generation, numerical and Monte Carlo integration, and finding limiting distributions of Markov Chains with both discrete and continuous states. Applications include coverage probabilities of binomial confidence intervals, estimation of disease prevalence from screening tests, parallel redundancy for improved reliability of systems, and various kinds of genetic modeling. These initial chapters can be used for a non-Bayesian course in the simulation of applied probability models and Markov Chains. Chapters 8 through 10 give a brief introduction to Bayesian estimation and illustrate the use of Gibbs samplers to find posterior distributions and interval estimates, including some examples in which traditional methods do not give satisfactory results. WinBUGS software is introduced with a detailed explanation of its interface and examples of its use for Gibbs sampling for Bayesian estimation. No previous experience using R is required. An appendix introduces R, and complete R code is included for almost all computational examples and problems (along with comments and explanations). Noteworthy features of the book are its intuitive approach, presenting ideas with examples from biostatistics, reliability, and other fields; its large number of figures; and its extraordinarily large number of problems (about a third of the pages), ranging from simple drill to presentation of additional topics. Hints and answers are provided for many of the problems. These features make the book ideal for students of statistics at the senior undergraduate and at the beginning graduate levels.
SP - 320
PB - Springer
SN - 038740273X
UR - http://www.amazon.com/Introduction-Probability-Simulation-Gibbs-Sampling/dp/038740273X%3FSubscriptionId%3D1V7VTJ4HA4MFT9XBJ1R2%26tag%3Dmekentosjcom-20%26linkCode%3Dxm2%26camp%3D2025%26creative%3D165953%26creativeASIN%3D038740273X
L1 - file://localhost/Users/kuhna03/Dropbox/Papers2/Books/2010/Suess/2010_Suess.pdf
L3 - papers2://publication/uuid/B24F3DFC-42E1-4EC4-A297-AFBA2CE390EF
ER -

This is an article that is listed as incomplete and the PDF did not come along

TY - JOUR
ID - 47405
T1 - Robust and efficient estimation of effective dose
A1 - Karunamuni, Rohana J
A1 - Tang, Qingguo
A1 - Zhao, Bangxin
N2 - Abstract In dose–response studies, experimenters are often interested in estimating the effective dose E D p , the dose at which the probability of response is p , 0 < p < 1 . For instance, in pharmacology studies one is typically interested in estimating E D 0.5 , whereas in toxicology studies the main interest is E D p for smaller values of p . In this context, methods based on parametric, semiparametric, and nonparametric models have been developed. Traditional estimators based on parametric models are generally efficient but are not robust to model misspecification. On the other hand, nonparametric estimators are robust to model misspecification but are less efficient. Semiparametric methods are a compromise. Two new parametric methods are presented in this paper for estimating E D p using minimum-distance techniques. It is shown that the proposed estimators are efficient under the model and simultaneously have some desirable robustness properties. The asymptotic properties such as consistency and asymptotic normality are studied. Small-sample and robustness properties of the proposed estimators are examined and are compared with traditional estimators using Monte Carlo studies. Two real-data examples are also analyzed.
T2 - Computational Statistics and Data Analysis
J2 - Computational Statistics and Data Analysis
PB - Elsevier Science Publishers BV
VL - 90
IS - 0
SP - 47
EP - 60
UR - http://www.sciencedirect.com/science/article/pii/S0167947315000985
L1 - file://localhost/Users/kuhna03/Dropbox/Papers2/Articles/Unknown/Karunamuni/Computational_Statistics_and_Data_Analysis__Karunamuni.pdf
L3 - papers2://publication/uuid/DE7C0FDE-1E4D-4826-97EC-A572D64ABDE6
ER -