cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc25c9b6
title: CCDC 2006857: Experimental Crystal Structure Determination

description: Related Article: Lele Tang, Jie Zan, Hao Peng, Xi Yan, Ye Tao, Dan Tian, Qingqing Yang, Huanhuan Li, Qiushui Chen, Wei Huang, Runfeng Chen|2020|Chem.Commun.|56|13559|doi:10.1039/D0CC05389A
DOI: 10.5517/ccdc.csd.cc25jgqx
title: CCDC 2011829: Experimental Crystal Structure Determination

description: Related Article: Amanda B. Chung, Daniel N. Huh, Joseph W. Ziller, William J. Evans|2020|Inorg.Chem.Front.|7|4445|doi:10.1039/D0QI00746C
DOI: 10.25952/5fb6dfbf5a486
title: Efficacy of Psychological Interventions for Selective Mutism in Children: A Meta-Analysis of Randomized Controlled Trials

description: This dataset is the basis for a meta-analysis done on Comprehensive Meta-Analysis software version 2.2.064. The related abstract of the study follows.<br><b>Objective:</b> Selective mutism is a rare childhood anxiety disorder characterized by a consistent failure to speak in certain social situations where speech is expected, despite fluent speech in other situations. The purpose of this meta-analysis was to investigate the efficacy of psychological interventions for selective mutism in randomized controlled trials (RCTs). <b>Method:</b> Five RCTs with a total of 233 participants were analyzed using a random-effects model. A quality assessment of the included studies revealed that psychometrically-sound measures and treatment manuals were used across all studies. <b>Conclusions:</b> The results of the analyses showed psychological interventions to be more effective than no treatment, with the overall weighted effect size of g = 0.87, indicating a large mean treatment effect. This effect did not significantly differ whether only selective mutism specific or non-selective mutism specific measures were included in the analysis. These findings provide support for the efficacy of psychological treatment for selective mutism. Future research could examine the effects of the successful treatments identified in this meta-analysis when compared with a psychological placebo or another bona fide treatment.
DOI: 10.5281/zenodo.4281434
title: CellO Data Sets

description: <strong>Overview</strong> A permanent archive of the datasets used in the CellO manuscript (https://www.biorxiv.org/content/10.1101/634097v2). <strong>Expression data</strong> Quantified gene expression for all bulk RNA-seq samples used in this study are available as an HDF5 file (in log transcripts per million): <strong>bulk_log_tpm.h5</strong> Each bulk RNA-seq experiment accession is mapped to a set of cell type labels from the Cell Ontology: <strong>bulk_labels.json</strong> The single-cell data used in this study are also available as an HDF5 file (in log transcripts per million): <strong>single_cell_log_tpm.h5</strong> Each single-cell experiment accession is mapped to a set of cell type labels from the Cell Ontology: <strong>single_cell_labels.json</strong> <strong>Dataset partitions</strong> We partitioned the bulk RNA-seq data into several subsets that were used for various purposes in the study: The list of bulk RNA-seq samples used for training the classifier for evaluation on the bulk validation-set (i.e. the pre-taining set): <strong>training_bulk_experiments.json</strong> The list of bulk RNA-seq samples in the validation-set:<strong> test_bulk_experiments.json</strong> The list of single-cell experiments in the test set used for evaluting CellO. These are all samples with cell type terms that also appear in the bulk RNA-seq data (i.e. the training data): <strong>test_single_cell_experiments.json</strong> <strong>Technical variable annotations</strong> We annotated 27,097 RNA-seq samples in the Sequence Read Archive (SRA) with technical variables in order to derive a set of primary, healthy, untreated samples (i.e. the datasets above). Our annotations were based on a custom label-hierarchy of technical variables: <strong>tags.json</strong> The mapping from each SRA experiment accession to its set of technical variable labels: <strong>experiment_tags.json</strong> <strong>Trained model coefficients</strong> After training the binary classifiers for each cell type, the model coefficients can be used to investigate up and downregulated genes in each cell type. Below, we post the model coefficients for the one-versus-rest trained binary classifiers (used in the Isotonic Regression and True Path Rule algorithms) as well as the coefficients for the classifiers in the Cascaded Logistic Regression algorithm. Each model was trained on the full set of bulk RNA-seq samples used in the study. Each algorithm's cell type model coefficients are available in a tab-separated-value file: One-versus-rest classifier coefficients: <strong>one_vs_rest_coefficients.tsv.gz</strong> Cascaded logistic regression coefficients: <strong>cascaded_logistic_regression_coefficients.tsv.gz</strong>
DOI: 10.21227/1470-8h76
title: Aerial images for pile fire detection using drones (UAVs)

description: Wildfires are one of the deadliest and dangerous natural disasters in the world. Wildfires burn millions of forests and they put many lives of humans and animals in danger. Predicting fire behavior can help firefighters to have better fire management and scheduling for future incidents and also it reduces the life risks for the firefighters. Recent advance in aerial images shows that they can be beneficial in wildfire studies. Among different methods and technologies for aerial images, Unmanned Aerial Vehicles (UAVs) and drones are beneficial to collect information regarding the fire. This study provides an aerial imagery dataset using drones during a prescribed pile fire in Northern Arizona, USA. This dataset consists of different repositories including raw aerial videos recorded by drones' cameras and also raw heatmap footage recorded by an infrared thermal camera. To help researchers, two well-known studies; fire classification and fire segmentation are defined based on the dataset. For approaches such as Neural Networks (NNs) and fire classification, 39,375 frames are labeled ("Fire" vs "Non-Fire") for the training phase. Also, another 8,617 frames are labeled for the test data. 2,003 frames are considered for the fire segmentation and regarding that, 2,003 masks are generated for the purpose of Ground Truth data with pixel-wise annotation.More information about this study and the two machine learning challenges that we used is available here:https://github.com/AlirezaShamsoshoara/Fire-Detection-UAV-Aerial-Image-Classification-Segmentation-UnmannedAerialVehicle
DOI: 10.35013/me9h-wm91
title: Argentine Chamber of Deputies, 1904 - 1955: roll call records and member characteristics
DOI: 10.21227/qscq-mr51
title: Stages of maturity of the tomato

description: It is a dataset that contains six categories of tomato maturity using the criteria established by the USDA.
DOI: 10.21227/ayfv-ha18
title: Stages of maturity of the tomato

description: It is a dataset that contains six categories of tomato maturity using the criteria established by the USDA.
DOI: 10.21227/b708-9f64
title: Stages of maturity of the tomato

description: It is a dataset that contains six categories of tomato maturity using the criteria established by the USDA.
DOI: 10.21227/sz68-qj45
title: Stages of maturity of the tomato

description: It is a dataset that contains six categories of tomato maturity using the criteria established by the USDA.
DOI: 10.21227/t6q8-mt34
title: Stages of maturity of the tomato

description: It is a dataset that contains six categories of tomato maturity using the criteria established by the USDA.
DOI: 10.7289/v5gx48vs
title: Archival and Discovery of December 23, 1854 Tsunami Event on Marigrams
DOI: 10.7289/v5c827kj
title: Archival and Discovery of August 27, 1883 Tsunami Event on Marigrams
DOI: 10.7289/v57h1gw8
title: Archival and Discovery of June 15, 1896 Tsunami Event on Marigrams
DOI: 10.7289/v5tm78d3
title: Archival and Discovery of March 2, 1933 Tsunami Event on Marigrams
DOI: 10.7289/v598856f
title: Archival and Discovery of April 1, 1946 Tsunami Event on Marigrams
DOI: 10.5281/zenodo.4279547
title: TempSense Calibration and Testing Data

description: Data from ice-point and boiling-point calibrations of TempSense temperature sensor and data from demonstration of an aluminum bar with heated at one end with spaced thermocouples.
DOI: 10.5281/zenodo.4279546
title: TempSense Calibration and Testing Data

description: Data from ice-point and boiling-point calibrations of TempSense temperature sensor and data from demonstration of an aluminum bar with heated at one end with spaced thermocouples.
DOI: 10.7289/v55h7dgq
title: Archival and Discovery of November 4, 1952 Tsunami Event on Marigrams
DOI: 10.7289/v51r6nqj
title: Archival and Discovery of May 22, 1960 Tsunami Event on Marigrams
DOI: 10.7289/v54x564t
title: Archival and Discovery of May 16, 1968 Tsunami Event on Marigrams
DOI: 10.35013/4ysy-zp14
title: Health and Wellness of the Elderlyin Bogotá, Colombia, 2012.
DOI: 10.35013/44qa-1c25
title: Summary of Study of Microcomputers in Small- and Medium-Sized Colombian Firms, 1985-1986
DOI: 10.35013/dwwt-gx05
title: Belgian Municipal Accounts for 16 Governments, 1870 - 1933
DOI: 10.35013/6pea-nn93
title: Financial Characteristics of Cities in the United States, 1905 - 1930
DOI: 10.5281/zenodo.4281507
title: swash17/NCHRP-17-65: NCHRP 17-65 Resources

description: Field data from CA, ID, MT, NC, OR
DOI: 10.5281/zenodo.4281506
title: swash17/NCHRP-17-65: NCHRP 17-65 Resources

description: Field data from CA, ID, MT, NC, OR
DOI: 10.35013/wka2-fe16
title: Argentine Election of 1946 and Census of 1947: raw and transformed datasets
DOI: 10.35013/1w46-t422
title: Wisconsin Study of Families and Work 1990 - 2012
DOI: 10.35013/ad1a-yw50
title: Medicaid Waiver Dataset: Coverage for Childless Adults 1996 – 2014
DOI: 10.35013/y8gt-8577
title: National Survey of Families and Households (NSFH), Wave 3 (2001-2003)
DOI: 10.5281/zenodo.3558774
title: Temperature and rainfall datasets for the paper "STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for weather forecasting"

description: This page includes spatiotemporal datasets used in the paper STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for weather forecasting. ARIMA and deep learning models use datasets, as follow: ARIMA models baseline-chirps-1981-2019.nc (rainfall data)<br> baseline-ucar-1979-2015.nc (temperature data) Deep learning models: <em>5-step ahead:</em> dataset-chirps-1981-2019-seq5-ystep5.nc (rainfall data)<br> dataset-ucar-1979-2015-seq5-ystep5.nc (temperature data) <em>15-step ahead:</em> dataset-chirps-1981-2019-seq5-ystep15.nc (rainfall data)<br> dataset-ucar-1979-2015-seq5-ystep15.nc (temperature data)
DOI: 10.5281/zenodo.3558773
title: Temperature and rainfall datasets for the paper "STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for weather forecasting"

description: This page includes spatiotemporal datasets used in the paper STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for weather forecasting. ARIMA and deep learning models use datasets, as follow: ARIMA models baseline-chirps-1981-2019.nc (rainfall data)<br> baseline-ucar-1979-2015.nc (temperature data) Deep learning models: <em>5-step ahead:</em> dataset-chirps-1981-2019-seq5-ystep5.nc (rainfall data)<br> dataset-ucar-1979-2015-seq5-ystep5.nc (temperature data) <em>15-step ahead:</em> dataset-chirps-1981-2019-seq5-ystep15.nc (rainfall data)<br> dataset-ucar-1979-2015-seq5-ystep15.nc (temperature data)
DOI: 10.21227/0d2n-j565
title: For paper "A Reduced Electrically-Equivalent Model of the IEEE European Low Voltage Test Feeder"

description: This dataset contains the data associated with the electrically equivalent model of the IEEE Low Voltage (LV) test feeder for use of the distribution network studies. This dataset is for the letter entitled:" A Reduced Electrically-Equivalent Model ofthe IEEE European Low Voltage Test Feeder".
DOI: 10.21227/g213-s236
title: For paper "A Reduced Electrically-Equivalent Model of the IEEE European Low Voltage Test Feeder"

description: This dataset contains the data associated with the electrically equivalent model of the IEEE Low Voltage (LV) test feeder for use of the distribution network studies. This dataset is for the letter entitled:" A Reduced Electrically-Equivalent Model ofthe IEEE European Low Voltage Test Feeder".
DOI: 10.21227/temy-a694
title: For paper "A Reduced Electrically-Equivalent Model of the IEEE European Low Voltage Test Feeder"

description: This dataset contains the data associated with the electrically equivalent model of the IEEE Low Voltage (LV) test feeder for use of the distribution network studies. This dataset is for the letter entitled:" A Reduced Electrically-Equivalent Model ofthe IEEE European Low Voltage Test Feeder".
DOI: 10.6084/m9.figshare.13262873
title: Readout Scan Tutorial Data

description: Simulated noisy readout data for ReCirq data collection and analysis guide
DOI: 10.6084/m9.figshare.13262873.v1
title: Readout Scan Tutorial Data

description: Simulated noisy readout data for ReCirq data collection and analysis guide
DOI: 10.5281/zenodo.4281515
title: Wiktionary taxonomic data (ru and en)

description: Wiktionary taxonomic data (ru and en) about synonyms, hypernyms and meanings
DOI: 10.5281/zenodo.4281514
title: Wiktionary taxonomic data (ru and en)

description: Wiktionary taxonomic data (ru and en) about synonyms, hypernyms and meanings
DOI: 10.21227/wvz3-3p32
title: For paper "A Reduced Electrically-Equivalent Model of the IEEE European Low Voltage Test Feeder"

description: This dataset contains the data associated with the electrically equivalent model of the IEEE Low Voltage (LV) test feeder for use of the distribution network studies. This dataset is for the letter entitled:" A Reduced Electrically-Equivalent Model ofthe IEEE European Low Voltage Test Feeder".
DOI: 10.21227/6g0g-3458
title: Stages of maturity of the tomato

description: It is a dataset that contains six categories of tomato maturity using the criteria established by the USDA.
DOI: 10.21227/z30m-pp50
title: Stages of maturity of the tomato

description: It is a dataset that contains six categories of tomato maturity using the criteria established by the USDA.
DOI: 10.25625/trtodx/dug5ah
title: dualhrnet_gupta_multi.pt
DOI: 10.25625/trtodx/irvccf
title: dualhrnet_gupta_multiswa.pt
DOI: 10.25625/trtodx/jrhplk
title: dualhrnet_gupta_plain.pt
DOI: 10.25625/trtodx/k7uic7
title: dualhrnet_gupta_swa.pt
DOI: 10.25625/trtodx/tnz94m
title: dualhrnet_ood1_plain.pt
DOI: 10.25625/trtodx/uyprdp
title: dualhrnet_ood1_swa.pt
DOI: 10.25625/trtodx/tizqmq
title: dualhrnet_ood2_multi.pt
DOI: 10.25625/trtodx/3gmkek
title: dualhrnet_ood2_multiswa.pt
DOI: 10.25625/trtodx/qqqqgu
title: dualhrnet_ood2_plain.pt
DOI: 10.25625/trtodx/qr8iec
title: dualhrnet_ood2_swa.pt
DOI: 10.25625/trtodx/jpt81g
title: dualhrnet_ood3_multi.pt
DOI: 10.25625/trtodx/pio9ux
title: dualhrnet_ood3_multiswa.pt
DOI: 10.25625/trtodx/rbjtbn
title: dualhrnet_ood3_plain.pt
DOI: 10.25625/trtodx/lxe64j
title: dualhrnet_ood3_swa.pt
DOI: 10.25625/trtodx/79moyd
title: twostream-resnet50_all_plain.pt
DOI: 10.25625/trtodx/fvnvri
title: twostream-resnet50_gupta_multi.pt
DOI: 10.25625/trtodx/y7bwac
title: twostream-resnet50_gupta_multiswa.pt
DOI: 10.25625/trtodx/0lzk39
title: twostream-resnet50_gupta_plain.pt
DOI: 10.25625/trtodx/wxh34a
title: twostream-resnet50_gupta_swa.pt
DOI: 10.25625/trtodx/qiba7x
title: twostream-resnet50_ood1_multi.pt
DOI: 10.25625/trtodx/rddvlx
title: twostream-resnet50_ood1_multiswa.pt
DOI: 10.25625/trtodx/fv0qpj
title: twostream-resnet50_ood1_plain.pt
DOI: 10.25625/trtodx/mhbpt0
title: twostream-resnet50_ood1_swa.pt
DOI: 10.25625/trtodx/7q9yxy
title: twostream-resnet50_ood2_multi.pt
DOI: 10.25625/trtodx/se8bmo
title: twostream-resnet50_ood2_multiswa.pt
DOI: 10.25625/trtodx/rs0tec
title: twostream-resnet50_ood2_plain.pt
DOI: 10.25625/trtodx/dzkaeg
title: twostream-resnet50_ood2_swa.pt
DOI: 10.25625/trtodx/n4zfxf
title: twostream-resnet50_ood3_multi.pt
DOI: 10.25625/trtodx/ceygju
title: twostream-resnet50_ood3_multiswa.pt
DOI: 10.25625/trtodx/wtwhdz
title: twostream-resnet50_ood3_plain.pt
DOI: 10.25625/trtodx/ga453w
title: twostream-resnet50_ood3_swa.pt
DOI: 10.25625/trtodx/9wofez
title: dualhrnet_ood1_multiswa.pt

description: :unav
DOI: 10.25625/trtodx/emqwrj
title: dualhrnet_ood1_multi.pt

description: :unav
DOI: 10.25625/trtodx
title: Replication Data for: Assessing out-of-domain generalization for robust building damage detection

description: Model weights for Benson Ecker (2020): Assessing out-of-domain generalization for robust building damage detection.
DOI: 10.21227/j3gy-ft70
title: For paper "A Reduced Electrically-Equivalent Model of the IEEE European Low Voltage Test Feeder"

description: This dataset contains the data associated with the electrically equivalent model of the IEEE Low Voltage (LV) test feeder for use of the distribution network studies. This dataset is for the letter entitled:" A Reduced Electrically-Equivalent Model of the IEEE European Low Voltage Test Feeder".
DOI: 10.5517/ccdc.csd.cc25vbft
title: CCDC 2021306: Experimental Crystal Structure Determination

description: Related Article: Lena Knauer, Carsten Strohmann|2020|Chem.Commun.|56|13543|doi:10.1039/D0CC05547F
DOI: 10.5517/ccdc.csd.cc25vbhw
title: CCDC 2021308: Experimental Crystal Structure Determination

description: Related Article: Lena Knauer, Carsten Strohmann|2020|Chem.Commun.|56|13543|doi:10.1039/D0CC05547F
DOI: 10.17632/6xg84mfmct.1
title: Data for: Macroscopic noise up to 100 kHz in the IPEN/MB-01 reactor

description: Auto Power Spectral Density (APSD) dependent on frequency (Hz) for the cases of 286.8 ppm of natural boron.
DOI: 10.17632/dpvg74xp25.1
title: Data for: Experimental investigation of scour around circular arrangement pile groups

description: The file of data_for_Fig4 is the scour depth development for Fig. 4 in the revised manuscript. The file of Scour_depth_development is the development of maximum scour depth with time for each case mentioned in the revised manuscript.
DOI: 10.21227/mrz1-tt82
title: Aerial images for pile fire detection using drones (UAVs)

description: Wildfires are one of the deadliest and dangerous natural disasters in the world. Wildfires burn millions of forests and they put many lives of humans and animals in danger. Predicting fire behavior can help firefighters to have better fire management and scheduling for future incidents and also it reduces the life risks for the firefighters. Recent advance in aerial images shows that they can be beneficial in wildfire studies. Among different methods and technologies for aerial images, Unmanned Aerial Vehicles (UAVs) and drones are beneficial to collect information regarding the fire. This study provides an aerial imagery dataset using drones during a prescribed pile fire in Northern Arizona, USA. This dataset consists of different repositories including raw aerial videos recorded by drones' cameras and also raw heatmap footage recorded by an infrared thermal camera. To help researchers, two well-known studies; fire classification and fire segmentation are defined based on the dataset. For approaches such as Neural Networks (NNs) and fire classification, 39,375 frames are labeled ("Fire" vs "Non-Fire") for the training phase. Also, another 8,617 frames are labeled for the test data. 2,003 frames are considered for the fire segmentation and regarding that, 2,003 masks are generated for the purpose of Ground Truth data with pixel-wise annotation.More information about this study and the two machine learning challenges that we used is available here:https://github.com/AlirezaShamsoshoara/Fire-Detection-UAV-Aerial-Image-Classification-Segmentation-UnmannedAerialVehicleA sample video is available on YouTube:https://www.youtube.com/watch?v=vW17c-7zGek
DOI: 10.25345/c5720c
title: MassIVE MSV000086488 - Untargeted Metabolomics of Bovine Coronary Artery Endothelial Cells Under Simulated Diabetes
title: Bovine Coronary Artery Endothelial Cells (BCAEC) were cultured under simulated diabetes (High glucose+ High insulin) for 12 days. Intracellular metabolites were extracted by a solvent mixture of ethanol:methanol (1:1). Metabolites were separated and detected by an Eksigent nanoLC 400 system coupled to a TripleTOF5600 MS. Data Dependent Acquisition under positive ionization mode was employed, collecting MS2 data for the 20 most intense precursor ions/cycle.
DOI: 10.5281/zenodo.4281587
title: KM1709_Mesoscope_B12_Incubations_HILICMode

description: KM1709 Mesoscope B12 Incubations HILIC (Positive and Negative). These are targeted metabolite peak areas, representative of abundance values taken.The samples were taken from each of the two dipoles of a mesoscale eddy near Station Aloha and incubated for four days. Incubation #1 was the cyclonic dipole, while Incubation #2 was the anticyclonic dipole. Six conditions were created for each of the samples. T0: In situ condition at the start of the experiment, aka time 0. Water that was filtered, not incubated, and frozen. Control (Tfinal): 100% in situ water from 25 m, incubated and then filtered. Deep Sea Water mix: 10% Deep Sea Water (DSW) taken from 700 m, filtered and evenly mixed with 90% in situ water from 25 m. This mixture was then incubated and filtered. +B12: 25 m water spiked with 800 ul of B12 for a final concentration of ~240 pM, incubated, filtered. - B12: 25 m water spiked with trace metals (iron, Si, PO4, NO3) and no B12, incubated, filtered. +DMB, -B12: 25 m water with the same conditions of the -B12 state, but with an added spike of 5,6-dimethylbenzimidazole (DMB). +DMB: 25 m water spiked with 800 ul of DMB for a final concentration of 240 pm, incubated, filtered. Samples from KM1709 (MESO-SCOPE 2017) were collected and produced by the Ingalls Lab as part of the Simons Gradients project with support from the Simons Foundation.
description: B12, nutrients, biology, chemistry, cobalamin, DMB, 5,6-dimethylbenzimidazole, cruise, KM1709, Kilo Moana, MESOSCOPE, MS, Ingalls Lab, nutrients, University of Washington, UW, Metabolites, LC-MS, mass spectrometry
DOI: 10.5281/zenodo.4281586
title: KM1709_Mesoscope_B12_Incubations_HILICMode

description: KM1709 Mesoscope B12 Incubations HILIC (Positive and Negative). These are targeted metabolite peak areas, representative of abundance values taken.The samples were taken from each of the two dipoles of a mesoscale eddy near Station Aloha and incubated for four days. Incubation #1 was the cyclonic dipole, while Incubation #2 was the anticyclonic dipole. Six conditions were created for each of the samples. T0: In situ condition at the start of the experiment, aka time 0. Water that was filtered, not incubated, and frozen. Control (Tfinal): 100% in situ water from 25 m, incubated and then filtered. Deep Sea Water mix: 10% Deep Sea Water (DSW) taken from 700 m, filtered and evenly mixed with 90% in situ water from 25 m. This mixture was then incubated and filtered. +B12: 25 m water spiked with 800 ul of B12 for a final concentration of ~240 pM, incubated, filtered. - B12: 25 m water spiked with trace metals (iron, Si, PO4, NO3) and no B12, incubated, filtered. +DMB, -B12: 25 m water with the same conditions of the -B12 state, but with an added spike of 5,6-dimethylbenzimidazole (DMB). +DMB: 25 m water spiked with 800 ul of DMB for a final concentration of 240 pm, incubated, filtered. Samples from KM1709 (MESO-SCOPE 2017) were collected and produced by the Ingalls Lab as part of the Simons Gradients project with support from the Simons Foundation.
description: B12, nutrients, biology, chemistry, cobalamin, DMB, 5,6-dimethylbenzimidazole, cruise, KM1709, Kilo Moana, MESOSCOPE, MS, Ingalls Lab, nutrients, University of Washington, UW, Metabolites, LC-MS, mass spectrometry
DOI: 10.3886/e125701
title: Replication for "The Changing Geography of Social Mobility" (Proceedings of the National Academy of Sciences)

description: This repository contains all major (allowable) data sources and scripts that can be shared for replication purposes for the article entitled "The Changing Geography of Social Mobility."<br><br>