dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion In this paper we have introduced new internationally comparable measures of macroeconomic uncertainty for a large set of countries using data revisions in real GDP that are bound to the system of national accounts. We have set up an econometric model and constructed a new real-time data set of real GDP for 39 countries that serves as the basis for our estimations. Using real-time data permits us to obtain accurate estimates of forecast error based uncertainty that an economic agent experienced at any given point in time, whereas existing measures of macroeconomic uncertainty base on forecast errors that are constructed with non real-time data. In order to obtain real-time uncertainty estimates, we extended the data revision model proposed by Jacobs and van Norden (2011) such that it allows us to extract the volatility of the unpredictable part of future releases of the news component that forms our measure of macroeconomic uncertainty.
S. GDP release. Both forecasting exercises use the econometric framework outlined in Section 2 in  Jurado et al. (2015)  to forecast last release of GDP. The first forecasting exercises uses the 132 macroeconomic variables and the 147 financial variables proposed by  Jurado et al. (2015)  to forecast the final GDP release. In order to conduct this forecast, we use the original dataset provided by the authors in their Online-Appendix. As the dataset contains monthly time series, but we need to forecast quarterly GDP, we aggregate the monthly series to quarterly series by taking quarterly averages. Real-time GDP for the U. S. starts in 1965Q1. The dataset by  Jurado et al. (2015)  provides data up to 2011Q4. We conduct pseudo out-of-sample 35  forecasts for the quarter 1976Q4 to 2011Q4. In the second exercise, we repeat the first exercise, but use the first release as an additional predictor.
This analysis shows that for the United States the first release of GDP appears to be a good forecast for the final GDP. We can show that when using established forecast routines and one of the most comprehensive dataset about the U. S. economy, we cannot beat the forecast performance of the first release (see Table  5 ). Anesti et al. (2020)  conduct a similar exercise to study real-time data vintages of Great Britain. The authors propose a releaseaugmented dynamic factor model (RA-DFM) and use a comprehensive and mixed-frequency dataset to quantify the role of a country's data flow in nowcasting both early GDP releases, and subsequent revisions of official estimates. While the authors show that they are consistently able to outperform alternative forecasting models, their method cannot improve over a no-change forecast at all horizons.
F. 1. Selected high and low EPL countries In order to empirically evaluate the effect of employment protection legislation (EPL) on the propagation of uncertainty shocks, we estimate a VAR for countries with stricter EPL and a VAR for countries with a lower degree of EPL. As mentioned in the main text, we use the OECD Employment Protection Database to split countries into two groups. The groups selected in Table  3  mirror our expectations with Anglo-Saxon economies displaying a low degree and continental European countries showing higher degree of employment protection standards. The OECD Employment Protection Database attests Switzerland and Japan have a very similar degree of employment protection legislation. In our baseline specification, we include Switzerland in the group with low labor protection and Japan in the group of high EPL countries.
Berger, Todd E. Clark, and Haroon   Mumtaz for making available their global uncertainty estimates. We further thank  Stefan Neuwirth  and  Roberto Golinelli  for providing us with historic German and Italian GDP real time data. Financial support from the  French National Agency for Research (ANR)  via the grant  ANR-20-CE26-0013  is gratefully acknowledged. The project leading to this publication has also received funding from the  french government  under the '' France 2030 '' investment plan managed by the  French National Research Agency  (reference:  ANR-17-EURE-0020 ) and from  Excellence Initiative of Aix-Marseille University -A*MIDEX, France.
and S. Sarferaz Table 1 Data Source of Uncertainty Indicators. Countries MEI Code Datasource prior to 1999 Available since Australia AUS Real-Time Macroeconomic Database (U. Melbourne) Q3 Austria AUT FED Dallas Q3 Belgium BEL no data Q1 Brazil BRA no data Q2 Canada CAN FED Dallas & Bank of Canada Q3 Chile CHL no data Q1 Czech Republic CZE no data Q1 Denmark DNK FED Dallas Q2 Estonia EST no data Q3 Finland FIN FED Dallas Q4 France FRA FED Dallas Q2 Germany DEU FED Dallas & Boysen-Hogrefe and Neuwirth (2012) Q4 Great Britain GBR FED Dallas Q4 Hungary HUN no data Q2 Greece GRC no data Q4 Iceland ISL no data Q4 India IND no data Q4 Indonesia IDN no data Q4 Ireland IRL no data Q2 Israel ISR no data Q2 Italy ITA FED Dallas & Golinelli and Parigi (2008) Q3 Japan JPN FED Dallas Q3 Korea KOR FED Dallas Q1 Luxembourg LUX no data Q4 Mexico MEX FED Dallas Q2 Netherlands NLD FED Dallas Q3 New Zealand NZL Reserve Bank of New Zealand Q4 Norway NOR FED Dallas Q3 Poland POL no data Q2 Portugal PRT FED Dallas Q3 Russia RUS no data Q3 Slovakia SVK no data Q3 Slovenia SVN no data Q1 South Africa ZAF no data Q4 Spain ESP FED Dallas Q1 Sweden SWE FED Dallas Q4 Switzerland CHE FED Dallas Q2 Turkey TUR FED Dallas Q1 USA USA FED Philadelphia Q4 Notes: Central data source is the Original Release Data and Revisions Database.
The column Countries depicts the country and MEI Code the country code from the Original Release Data and Revisions Database. The column Datasource prior to 1999 describes the data source of releases prior to 1999. The column Available since states beginning of a country's uncertainty indicator. Rows in green highlight countries with data available prior to 1990Q1, rows in gray depict countries with data availablility starting between 1990Q1 and 2000Q4 and rows in red represent countries with data available only from 2001Q1 onward.
We conducted a meta-analysis based on 879 observations from treatment, we extracted the data from the treatment with no pollutant (e. g. , cadmium  (Jia et al. , 2021) , chlorpyrifos  (Kollah et al. , 2019)  or tropospheric ozone  (Kanerva et al. , 2006) ). Data were extracted for the control and the elevated CO 2 treatments (when several elevated levels of CO 2 were manipulated, data were extracted for each high CO 2 treatment, see  Maxwell et al. , 2022). If other global change factors than elevated CO 2 were manipulated among elevated temperature, increased precipitation, drought, and N addition, data for the control and the elevated CO 2 treatments were extracted for each level of the other manipulated global change factor. Overall, a total of 879 observations (including 456 observations of nitrification and denitrification rates and abundances of nitrifying and denitrifying functional genes) taken from 58 papers (Table  S1 ) and 46 independent elevated CO 2 experiments were reported.
Negative values of N 2 O emission rates and gross N transformation rates were excluded, as they do not enable to compute an effect size based on the natural log of the re- 2 | MATERIAL We searched journal articles using the Web of Science Core sponse ratio. When several sampling dates, sampling depths, or sam-pling modalities (e. g. , rhizosphere versus bulk soil) were available in a single study, we extracted all data and considered them as multiple outcomes from the same elevated CO 2 experiment. Similarly, when several plant species or plant community composition, or different Collection database and the search equation ((elevat* OR rising OR species management were manipulated in a single study, we consid- enrich*) NEAR/2 (CO2 OR "carbon dioxide")) AND (*nitrif* OR amoA OR ered them as multiple outcomes from the same elevated CO 2 ex- AOA OR AOB OR nirK OR nirS OR nosZ* OR "functional genes") in the periment.
When pollutants were manipulated in addition to the CO 2 title, abstract and keywords (349 results on July 18, 2022). Then, pa- pers meeting the following criteria were selected: (i) research papers (not a review or a book chapter), (ii) including both control and CO 2 enrichment treatments with uninterrupted CO 2 application across the duration of the experiment, (iii) elevated CO 2 experiment con- ducted in a terrestrial ecosystem on a plant-soil system, (iv) at least one of the following variables was measured: gross or potential nitri- fication or denitrification rates, abundances of nitrifying and denitri- fying functional genes (amoA-AOA, amoA-AOB, nirK, nirS, or nosZ), and (v) means, standard deviations or standard errors or confidence intervals, and sample sizes are available or extractable, either from tables, figures or the dataset when provided by the authors.
Data were extracted manually from tables or graphically from fig- ures using WebPlotDigitizer 4. 5 (Rohatgi, 2021), or were computed based on the dataset when provided by the authors for gross or po- tential nitrification or denitrification rates, abundances of nitrifying and denitrifying functional genes (amoA-AOA, amoA-AOB, nirK, nirS, or nosZ), N 2 O emission rates and putative drivers of nitrifying and.
A consensus protocol for functional connectivity analysis in the rat brain Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers.
Human neuroimaging sharing initiatives have led to a standardization of fMRI acquisition protocols that aid in the dissemination, aggregation and reuse of data  4, 7, 8. In contrast, preclinical neuroimaging essentially remains without harmonizing guidelines  9. Animal data acquisitions are performed under diverse protocols that span different strains, restraint and anesthesia conditions, radiofrequency coil designs and magnetic field strengths. These impact the generalization of the results and conclusions. Efforts to propose acquisition and/or pre-processing protocols rarely extend beyond the confines of single laboratories, thus limiting interoperability and widespread adoption  9, 10. Because of the potential of fMRI in rodents to study the biological basis for connectivity phenomena across the whole brain longitudinally  11  , an optimized consensus protocol could potentiate future scientific discoveries.
In this pre-registered study, we set out to aggregate and make publicly available representative datasets with various fMRI acquisition protocols in the rat and identify experimental parameters associated with robust and reliable FC detection. We curated the MultiRat_rest collection (646 rats from 65 datasets) representing protocols used     image acquisitions (magnetic field strength, sequence and sequence parameters; Fig. 1d, f, g ). Notably, there was a large sex bias in favor of males (Fig. 1a ). Similarly, the distribution of anesthesia protocols was also in line with current trends in the field (Fig. 1c )  9. Despite the heterogeneous distribution of the acquisition parameters and ensuing image quality (Fig. 1g ,h,i), 638 of 646 of the scans passed pre-processing quality control (one scan with excessive motion, one empty scan and six scans with failed image registrations; Fig.
1j ,k and Extended Data Fig. 2 ). As further quality controls, we described temporal signal-to-noise (Extended Data Fig. 3 ) and motion parameters (Extended Data Fig. 4 ). Overall, we found that the aggregated datasets represent current rodent fMRI acquisition trends  9. Moreover, given the low exclusion rate due to mis-registration, we concluded that the RABIES toolbox can be effectively employed to pre-process rat datasets despite widely varying acquisition parameters  14. This paves the way for reproducible and interoperable data processing across sites. We focused on examining FC in the sensory cortex, as sensory networks are robust to assess anesthesia effects in the anesthesia depth range typically used in fMRI  15. More specifically, we evaluated the specificity of the connectivity of the S1 barrel field area (S1bf) using two complementary criteria as indexes of accurate FC identification (Fig.
2c ). The global signal regression (GSR) nuisance model was the one that performed the best for specific connectivity detection (40. 8% of the animals with specific connectivity, 11. 8% as non-specific, 13. 6% as spurious and 33. 9% as containing no detectable FC; Fig. 2c, d, g ). To test the generalizability of the specificity metric to other networks, we implemented the same quality control metric to the cingulate cortex, a node of the rodent default mode network, which uncovered lower connectivity specificity (22. 1%; Extended Data Fig. 5 ). Because network inference is often assessed at the group level rather than at the individual level, we performed a one-sample t-test per dataset to estimate the incidence of contralateral connectivity detected within groups, relative to the S1bf seed as well as three other seeds (Fig.
3 ). Up to 70% of the 65 datasets presented limited evidence of contralateral connectivity relative to the seeds, and 50% of the datasets captured the features of a larger sensory network at the group level. We conclude that rat datasets do not capture FC equally, similar to what we reported in the mouse  12. We also found that GSR enhances the incidence of specific connectivity of S1bf seeds, so we decided to use this confound model for the remainder of our analysis. Our observations underline the need for an improved acquisition protocol to maximize individual-level inferences to potentiate discovery in experimental network neuroscience. To address this, we evaluated parameters associated with increased specificity incidence in the MultiRat_rest collection. Because our analysis relied on categorical data, we tested for differences against the expected frequency distribution of the four connectivity categories as a function of anesthesia and sequence categories using the χ 2 test.
Medetomidine/ isoflurane anesthesia combination condition was enriched in scans categorized as specific (Fig. 2e ; 92/187 scans, χ 2 test connectivity category ~ anesthesia: φ = 0. 27, degrees of freedom (dof) = 15, g = 92. 38, P = 3. 5 × 10 -13 ). The use of gradient echo imaging sequence was also associated with higher specificity incidence (Fig. 2f ; 241/568 scans, χ 2 test connectivity category ~ sequence: φ = 0. 11, dof = 3, g = 16. 00, P = 0. 001). Based on these observations, we devised an anesthesia protocol derived from dataset ds01031 (9/10 specific scans) and an imaging sequence based on ds01028 (8/10 specific scans), acquired on a 4. 7T mid-field system. The sequence selection is justified as it allows for the detection of specific connectivity on one of the lower-field systems used in this collection and should also perform well for higher-field systems.
We optimized relevant sequence parameters (echo time, flip angle and bandwidth) as a function of field strength (Supplementary Table  1 ). We hypothesized that this protocol would enhance functional specificity while being compatible with lower-field systems that continue to represent a relevant share of the systems used (Fig. 1d )  9. Using this consensus protocol, we curated the StandardRat collection of 21 datasets obtained across 20 centers. This consisted of n = 209 rats (93/116 female/male), mainly Wistar (189/209), aged ~2 months (Extended Data Fig. 6 ). Dataset acquisitions were performed at magnetic field strengths ranging from 4. 7T to 17. 2T. Pre-processing.
Resource https://doi. org/10. 1038/s41593-023-01286-8 was performed similarly to the unstandardized dataset. In total, 207 of 209 scans passed quality assurance (two discards due to image mis-registration). Interestingly, despite the same anesthesia protocol being used, the respiratory rates reported at the start of fMRI acquisition differed as a function of rat strain (Fig. 4a, b ; analysis of variance breathing rate ~ rat strain, η 2 = 0. 24, F 195,2 = 31. 17, P = 1. 8 × 10 -12 ). Finally, there was only a negligible effect on the temporal signal-to-noise ratio as a function of magnetic field strength (Fig. 4c ; linear regression temporal signal-to-noise ratio ~ field strength, coefficient = 0. 53 [-0. 23, 1. 30], r 2 = 0. 01, dof = 201, T = 1. 37, P = 0. 17). The objective of the StandardRat study was to improve the detection of specific connectivity in the individual datasets.
We found that 61. 8% of the scans were categorized as exhibiting specific connectivity (Fig. 4d ) against 40. 8% in the MultiRat_rest dataset with unstandardized acquisitions (Extended Data Fig. 8a ) when using GSR (χ 2 test connectivity category ~ dataset collection: φ = 0. 13, dof = 3, g = 33. 01, P = 3. 2 × 10 -7 ). The difference remained when we compared datasets from centers that contributed to both collections exclusively (χ 2 test connectivity category ~ dataset collection: φ = 0. 17, dof = 3, g = 28. 37, P = 3. 0 × 10 -6 ). This is going against the notion that the StandardRat collection outperforms due to characteristics of the contributing laboratories (for example, magnet type or strength and greater experience in data collection). Intriguingly, we could not establish a field strength effect on connectivity specificity (χ 2 test connectivity category ~ field strength: φ = 0.
19, dof = 12, g = 14. 89, P = 0. 25), suggesting that acquisition systems are not the limiting factor in this protocol. We conclude that the newly standardized protocol outperforms, on average, previously used protocols within the community for the detection of biologically plausible connectivity patterns. Finally, to explore whole-brain connectivity patterns, we examined connectivity incidence at the dataset level for four selected seeds (Extended Data Fig. 7 ) and group independent component analysis (Fig. 4e  and Extended Data Fig. 10 ). We found improvement in distal connectivity detection at the dataset level compared to the MultiRat_rest dataset as well as evidence for previously described rodent networks spanning across the whole brain  20. Intriguingly, there remained differences in the connectivity patterns between datasets from the StandardRat collection.
Indeed, five of 21 datasets achieved 90% specificity or higher. This underlines the potential of our protocol but also the need to understand what factors hamper other datasets. We next sought to identify the variables associated with greater incidences of specific connectivity patterns. Notably, we could not establish strain, sex or magnetic field strength effects, suggesting that the protocol is applicable for a large range of conditions (Supplementary Table  3 ). Next, we examined breathing rate and temporal signal-to-noise ratio as indicators of acquisition variability (Extended Data Fig. 9 ). Overall, scans with breathing rates ranging from 84 to 114 breaths per minute (bpm) and cortical temporal signal-to-noise ratio >53 achieved higher incidences of connectivity specificity among the StandardRat collection.
Discussion In summary, we curated two dataset collections (MultiRat_rest and StandardRat), analyzed them and made them an open-access resource. To our knowledge, these are the largest rodent fMRI datasets currently available. We developed and deployed a pre-processing and confound correction strategy generalizable to most scans and every dataset. Using information from the MultiRat_rest collection, we provide useful population parameter estimates to enhance the comparison of rat fMRI datasets. We proposed and evaluated a new standardized protocol and found that this consensus acquisition and pre-processing pipeline outperformed the previous acquisitions for connectivity specificity. To allow replication and to inspire new analyses, we release all raw and processed data to the broader community.
On average, the standardized protocol yielded improvements over previous acquisitions gathered in the MultiRat_rest collection. However, individual-level inferences remain limited to 61. 8% of the scans acquired. For comparison, we estimated 55% specificity in the 7T Human Connectome Project dataset (based on 184 scans) using the same quality assurance metrics  5. This underlines the importance of implementing sound quality assurance metrics based on assumptions of biologically plausible FC. Improving output quality, through understanding the factors leading to successful acquisitions, enhanced protocols, pre-processing or nuisance regression models, would lead to tangible outcomes capable of further potentiating future data acquisitions and reducing animal use by reducing discards. Notably, our new protocol relies on light sedation to restrain the animals.
Although optimized for fMRI, this protocol may not generalize to other procedures, such as electrophysiology. We also found that existing awake restraining protocols, on average, lead to a lower incidence of specific connectivity patterns (Extended Data Fig. 8b ). A previous report indicated similar values in a dataset in awake rats  21  , which we confirmed with pre-processing using the RABIES pipeline (Extended Data Fig. 8b ). Due to the impact of anesthesia on networks  9, 10  , it remains central to develop awake imaging as an alternative. However, these protocols should be examined through the lens of quality control metrics to ensure that plausible connectivity patterns are achieved consistently. Furthermore, physiological factors, such as heart rate, were scarcely reported. This limited our ability to examine the possible contribution of these factors on connectivity outcomes.
We take this opportunity to encourage the community to acquire these data and report them in ensuing publications. Finally, the acquisition sequence in StandardRat is designed to run on many systems. The effectiveness of new sequences should be examined against the current protocol-for example, isotropic resolution  22  or multiband acquisition 23. Our project's methodological and conceptual advancements are the first step toward large multi-site rat neuroimaging acquisitions. Coordinated open-science projects in neuroimaging and other disciplines are transforming the scientific landscape 24. Through the concerted efforts of our centers and potentiated by a substantially improved protocol, rat functional brain imaging is set to tackle urgent questions in neuroscience and mental health research.
Pre-registration This study was pre-registered (https://doi. org/10. 17605/OSF. IO/ EMQ4B). The following are notable deviations. We used the SIGMA rat template 25 instead of the Papp et al. template  26  , owing to fewer artifacts, additional relevant assets and improved in vivo contrast. Some datasets had field of view cropped to ease image registration. Some datasets had time series cropped to ease the computational load. We used temporal signal-to-noise instead of signal-to-noise ratio, as these were shown to be correlated  12   For comparison, 224 scans (n = 39 rats) from a single-center awake rat dataset were included  21. Exclusions were based on the following criteria: image misregistration (Extended Data Fig. 2 ), excessive motion and missing or corrupted data.