cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57745/npp6ym/uhapnr
title: haute_alpes_05_asp.gpkg

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DOI: 10.57745/npp6ym/aqdn0u
title: cher_18_asp.gpkg

description: :unav
DOI: 10.57745/npp6ym/geyiv3
title: CultureRPG.tab

description: :unav
DOI: 10.57745/npp6ym/v8nsew
title: ListeCulture.tab

description: :unav
DOI: 10.57745/szpysa
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DOI: 10.57745/3hke4f
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DOI: 10.57745/flkqvm
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DOI: 10.57745/kqi3ej
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DOI: 10.57745/mrgewe
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DOI: 10.57745/vqcjms
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DOI: 10.57745/3jxvst
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DOI: 10.57745/azzjeb
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DOI: 10.57745/natnc2
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DOI: 10.57745/uqn8ms
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description: :unav
DOI: 10.57745/axvwom
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DOI: 10.57745/d1crgw
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DOI: 10.57745/mlsnnh
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description: :unav
DOI: 10.57745/qexjg2
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DOI: 10.57745/skutdi
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description: :unav
DOI: 10.57745/c2bzia
title: d18.gpkg

description: :unav
DOI: 10.57745/laiml1
title: d16.gpkg

description: :unav
DOI: 10.57745/rm5obz
title: d14.gpkg

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DOI: 10.57745/yzss41
title: d15.gpkg

description: :unav
DOI: 10.57745/bd3tnp
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DOI: 10.57745/ktvvaf
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DOI: 10.57745/sajgwz
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DOI: 10.57745/sk4ef5
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DOI: 10.57745/zfprqc
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DOI: 10.57745/5omnuz
title: d29.gpkg

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DOI: 10.57745/cmw8n8
title: d26.gpkg

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DOI: 10.57745/j8ek3x
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description: :unav
DOI: 10.57745/miyhg3
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DOI: 10.57745/psbouc
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DOI: 10.57745/9afu7o
title: d35.gpkg

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DOI: 10.57745/a8dpgt
title: d39.gpkg

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DOI: 10.57745/svrmfs
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DOI: 10.57745/xao28q
title: d37.gpkg

description: :unav
DOI: 10.57745/5mptyf
title: d31.gpkg

description: :unav
DOI: 10.57745/dbyqzt
title: d36.gpkg

description: :unav
DOI: 10.57745/fdfvmn
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DOI: 10.57745/jla429
title: d38.gpkg

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DOI: 10.57745/ldagtm
title: d34.gpkg

description: :unav
DOI: 10.57745/dhvjuu
title: d45.gpkg

description: :unav
DOI: 10.57745/ivizvz
title: d32.gpkg

description: :unav
DOI: 10.57745/w2eztr
title: d48.gpkg

description: :unav
DOI: 10.57745/xd3gx1
title: d44.gpkg

description: :unav
DOI: 10.57745/2fc6c9
title: d42.gpkg

description: :unav
DOI: 10.57745/c7x94w
title: d41.gpkg

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DOI: 10.57745/i2g380
title: d43.gpkg

description: :unav
DOI: 10.57745/td97it
title: d46.gpkg

description: :unav
DOI: 10.57745/udm55p
title: d40.gpkg

description: :unav
DOI: 10.57745/0hhpnw
title: d51.gpkg

description: :unav
DOI: 10.57745/7naxip
title: d64.gpkg

description: :unav
DOI: 10.57745/cycebc
title: d69.gpkg

description: :unav
DOI: 10.57745/kvs8ob
title: d47.gpkg

description: :unav
DOI: 10.57745/xg8u9m
title: d49.gpkg

description: :unav
DOI: 10.57745/cj6zmh
title: d58.gpkg

description: :unav
DOI: 10.57745/cwkfw7
title: d57.gpkg

description: :unav
DOI: 10.57745/p6rbkh
title: d53.gpkg

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DOI: 10.57745/qawrx9
title: d61.gpkg

description: :unav
DOI: 10.57745/ywgrxy
title: d52.gpkg

description: :unav
DOI: 10.57745/ipbcpu
title: d62.gpkg

description: :unav
DOI: 10.57745/m4hwnt
title: d59.gpkg

description: :unav
DOI: 10.57745/molk2o
title: d67.gpkg

description: :unav
DOI: 10.57745/uzgqbo
title: d63.gpkg

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DOI: 10.57745/1pygvi
title: d66.gpkg

description: :unav
DOI: 10.57745/ifot1c
title: d65.gpkg

description: :unav
DOI: 10.57745/nbozd6
title: d60.gpkg

description: :unav
DOI: 10.57745/vydcz3
title: d55.gpkg

description: :unav
DOI: 10.57745/w8tlgh
title: d68.gpkg

description: :unav
DOI: 10.57745/c8j9b1
title: d76.gpkg

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DOI: 10.57745/favxwl
title: d50.gpkg

description: :unav
DOI: 10.57745/fushbd
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DOI: 10.57745/njkeud
title: d56.gpkg

description: :unav
DOI: 10.57745/8rf1ik
title: d74.gpkg

description: :unav
DOI: 10.57745/mp8dsd
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DOI: 10.57745/nulmvc
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DOI: 10.57745/ur43gi
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DOI: 10.57745/yvapjx
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DOI: 10.57745/jyvzyv
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description: :unav
DOI: 10.57745/kucgm5
title: d78.gpkg

description: :unav
DOI: 10.57745/q21hvz
title: d70.gpkg

description: :unav
DOI: 10.57745/usfaps
title: d82.gpkg

description: :unav
DOI: 10.57745/xoxumr
title: d73.gpkg

description: :unav
DOI: 10.57745/4c1zxf
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description: :unav
DOI: 10.57745/6gjqff
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description: :unav
DOI: 10.57745/hjulre
title: d84.gpkg

description: :unav
DOI: 10.57745/mlg4cu
title: d80.gpkg

description: :unav
DOI: 10.57745/haavul
title: d83.gpkg

description: :unav
DOI: 10.57745/i5e0ms
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description: :unav
DOI: 10.57745/ohwkty
title: d93.gpkg

description: :unav
DOI: 10.57745/teuq6c
title: d89.gpkg

description: :unav
DOI: 10.57745/uilahx
title: d81.gpkg

description: :unav
DOI: 10.57745/1tkntg
title: d95.gpkg

description: :unav
DOI: 10.57745/58yvrd
title: d91.gpkg

description: :unav
DOI: 10.57745/kzcexp
title: d01.gpkg

description: :unav
DOI: 10.57745/lapjec
title: d94.gpkg

description: :unav
DOI: 10.57745/lem6ly
title: d90.gpkg

description: :unav
DOI: 10.57745/ae5qpv
title: d04.gpkg

description: :unav
DOI: 10.57745/cjzq3z
title: d05.gpkg

description: :unav
DOI: 10.57745/tnxaxp
title: d07.gpkg

description: :unav
DOI: 10.57745/utksva
title: d09.gpkg

description: :unav
DOI: 10.57745/zaswq2
title: d06.gpkg

description: :unav
DOI: 10.57745/43ydlz
title: d11.gpkg

description: :unav
DOI: 10.57745/mgmug9
title: d03.gpkg

description: :unav
DOI: 10.57745/otzqgv
title: d02.gpkg

description: :unav
DOI: 10.57745/rggnoa
title: d08.gpkg

description: :unav
DOI: 10.57745/zty67p
title: d14.gpkg

description: :unav
DOI: 10.57745/nihz2n
title: d12.gpkg

description: :unav
DOI: 10.57745/noohya
title: d15.gpkg

description: :unav
DOI: 10.57745/ouu7l7
title: d10.gpkg

description: :unav
DOI: 10.57745/rl5lkl
title: d13.gpkg

description: :unav
DOI: 10.57745/fkdylp
title: d22.gpkg

description: :unav
DOI: 10.57745/flygac
title: d16.gpkg

description: :unav
DOI: 10.57745/urjrjz
title: d19.gpkg

description: :unav
DOI: 10.57745/xehsm3
title: d18.gpkg

description: :unav
DOI: 10.57745/ylxdxs
title: d17.gpkg

description: :unav
DOI: 10.57745/cqxx9f
title: d26.gpkg

description: :unav
DOI: 10.57745/lme0ww
title: d23.gpkg

description: :unav
DOI: 10.57745/nrrhw1
title: d21.gpkg

description: :unav
DOI: 10.57745/vhe5t3
title: d24.gpkg

description: :unav
DOI: 10.57745/wmwlfh
title: d25.gpkg

description: :unav
DOI: 10.57745/hxaqsx
title: d2b.gpkg

description: :unav
DOI: 10.57745/m5zfnz
title: d28.gpkg

description: :unav
DOI: 10.57745/oxof6m
title: d27.gpkg

description: :unav
DOI: 10.57745/uwrv9e
title: d2a.gpkg

description: :unav
DOI: 10.57745/7lz9ce
title: d29.gpkg

description: :unav
DOI: 10.57745/clg45m
title: d31.gpkg

description: :unav
DOI: 10.57745/cujnrx
title: d34.gpkg

description: :unav
DOI: 10.57745/geg0on
title: d32.gpkg

description: :unav
DOI: 10.57745/klkurl
title: d35.gpkg

description: :unav
DOI: 10.57745/udyvfg
title: d33.gpkg

description: :unav
DOI: 10.57745/5fttyl
title: d36.gpkg

description: :unav
DOI: 10.57745/k9ygpa
title: d37.gpkg

description: :unav
DOI: 10.57745/ltwoa1
title: d30.gpkg

description: :unav
DOI: 10.57745/yif5mo
title: d38.gpkg

description: :unav
DOI: 10.57745/er2kfj
title: d44.gpkg

description: :unav
DOI: 10.57745/hgp1zb
title: d42.gpkg

description: :unav
DOI: 10.57745/p0golw
title: d41.gpkg

description: :unav
DOI: 10.57745/xpta6p
title: d45.gpkg

description: :unav
DOI: 10.57745/3tppkp
title: d46.gpkg

description: :unav
DOI: 10.57745/clmhcf
title: d47.gpkg

description: :unav
DOI: 10.57745/r0pwgb
title: d43.gpkg

description: :unav
DOI: 10.57745/ueesr8
title: d48.gpkg

description: :unav
DOI: 10.57745/wfnqhk
title: d40.gpkg

description: :unav
DOI: 10.57745/18qffi
title: d56.gpkg

description: :unav
DOI: 10.57745/vbwfdp
title: d49.gpkg

description: :unav
DOI: 10.57745/vqmsll
title: d52.gpkg

description: :unav
DOI: 10.57745/vurrjf
title: d59.gpkg

description: :unav
DOI: 10.57745/wcoofa
title: d54.gpkg

description: :unav
DOI: 10.57745/i7cknt
title: d53.gpkg

description: :unav
DOI: 10.57745/pfsprr
title: d58.gpkg

description: :unav
DOI: 10.57745/ry2x9g
title: d51.gpkg

description: :unav
DOI: 10.57745/vbigf4
title: d55.gpkg

description: :unav
DOI: 10.57745/xtpwxh
title: d50.gpkg

description: :unav
DOI: 10.57745/00hkhh
title: d69.gpkg

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DOI: 10.57745/a1ukm7
title: d63.gpkg

description: :unav
DOI: 10.57745/g1tofx
title: d57.gpkg

description: :unav
DOI: 10.57745/qd9azw
title: d60.gpkg

description: :unav
DOI: 10.57745/tmcshs
title: d65.gpkg

description: :unav
DOI: 10.57745/1jk59q
title: d61.gpkg

description: :unav
DOI: 10.57745/jqhvo8
title: d67.gpkg

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DOI: 10.57745/oqfjev
title: d66.gpkg

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DOI: 10.57745/qm5o50
title: d64.gpkg

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DOI: 10.57745/39kn8m
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DOI: 10.57745/8knhfv
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description: :unav
DOI: 10.57745/k3mxeo
title: d71.gpkg

description: :unav
DOI: 10.57745/ndyuns
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DOI: 10.57745/u0c5dt
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DOI: 10.57745/7ygo9y
title: d76.gpkg

description: :unav
DOI: 10.57745/ahmw0m
title: d73.gpkg

description: :unav
DOI: 10.57745/q3hwbp
title: d78.gpkg

description: :unav
DOI: 10.57745/rvrzrc
title: d72.gpkg

description: :unav
DOI: 10.57745/9qf2yt
title: d82.gpkg

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DOI: 10.57745/omfjiz
title: d77.gpkg

description: :unav
DOI: 10.57745/rxgi5h
title: d81.gpkg

description: :unav
DOI: 10.57745/xc6sv5
title: d74.gpkg

description: :unav
DOI: 10.57745/3vpnkf
title: d89.gpkg

description: :unav
DOI: 10.57745/4iofv4
title: d87.gpkg

description: :unav
DOI: 10.57745/hnqydg
title: d86.gpkg

description: :unav
DOI: 10.57745/rdjdmb
title: d85.gpkg

description: :unav
DOI: 10.57745/veg26s
title: d88.gpkg

description: :unav
DOI: 10.57745/gjqd3b
title: d39.gpkg

description: :unav
DOI: 10.57745/lov19d
title: d95.gpkg

description: :unav
DOI: 10.57745/tozydd
title: d84.gpkg

description: :unav
DOI: 10.57745/ufswlr
title: d83.gpkg

description: :unav
DOI: 10.57745/zgtwmx
title: d80.gpkg

description: :unav
DOI: 10.57745/30q0ad
title: d91.gpkg

description: :unav
DOI: 10.57745/9vu5lz
title: d94.gpkg

description: :unav
DOI: 10.57745/lspaaj
title: d93.gpkg

description: :unav
DOI: 10.57745/mvnst5
title: d90.gpkg

description: :unav
DOI: 10.34820/fk2/eskchv/2gttlr
title: 7456-28845-1-PB-1.pdf

description: :unav
DOI: 10.34820/fk2/eskchv
title: David vs. Goliath: Uncovering The Future of Traditional Markets in Indonesia

description: The main purpose of this study is to address the attributes that explain younger generations’ (Generation Y) store loyalty to a shop in the traditional marketplace in Indonesia. This paper acts as a preliminary study with the primary goal of producing hypotheses on Generation Y store loyalty for traditional marketplace shopping in Indonesia. The result of the study will be useful for further research with statistical support, with the final goal to help local merchants in shaping their upcoming fate. The findings of this study suggest that from the analysis and synthesis of the research, there are several hypotheses that can be drawn and will be useful for future research. The attributes of accessibility, merchandise, reputation, in-store service, store atmosphere, public facilities, price, and security are all responsible for supporting younger generations’ (Generation Y) store loyalty to shopping in the traditional marketplace in Indonesia. This study has successfully generated the relevant categories in influencing the store loyalty attributes for Generation Y when they are shopping in the traditional marketplace in Indonesia. The results of this study will be useful as a benchmark for future research concerning Indonesian traditional marketplace consumers in general, and younger generations (Generation Y) consumer behavior when shopping in the traditional marketplace in Indonesia.
DOI: 10.6084/m9.figshare.19525615
title: ALL original search titles for data depository.csv

description: Initial litearture search on impacts on participants of conservation volunteers
DOI: 10.6084/m9.figshare.21030238.v1
title: PRISMA-P checklist for ‘Assessing the impacts of conservation volunteering on participant well-being: a simplified systematic review protocol

description: Prisma P Checklist
DOI: 10.34740/kaggle/dsv/4237064
title: 2021 World Population (updated daily)
DOI: 10.5281/zenodo.7104954
title: Appendix for "Is JavaScript Call Graph Extraction Solved Yet? A Comparative Study of Static and Dynamic Tools"

description: <strong>Abstract</strong> The popularity and wide adoption of JavaScript both at the client and server-side makes its code analysis more essential than ever before. Most of the algorithms for vulnerability analysis, coding issue detection, or type inference rely on the call graph representation of the underlying program. Luckily, there are quite a few tools to get this job done already. However, their performance in vitro and especially in vivo has not yet been extensively compared and evaluated. In this paper, we systematically compare five static and two dynamic approaches for building JavaScript call graphs on 26 WebKit SunSpider benchmark programs and two static and two dynamic methods on 12 real-world Node.js modules. The tools under examination using static techniques were <em>npm call graph</em>, <em>IBM WALA</em>, <em>Google Closure Compiler</em>, <em>Approximate Call Graph</em>, and <em>Type Analyzer for JavaScript</em>. We performed dynamic analyzes relying on the <em>nodejs-cg</em> tool (a customized Node.js runtime) and the <em>NodeProf </em>instrumentation and profiling framework. We provide a quantitative evaluation of the results, and a result quality analysis based on 941 manually validated call edges. On the SunSpider programs, which do not take any inputs, so dynamic extraction could be complete, all the static tools also performed well. For example, TAJS found 93% of all edges while having a 97% precision compared to the precise dynamic call graph. When it comes to real-world Node.js modules, our evaluation shows that static tools struggle with parsing the code and fail to detect a significant amount of call edges that dynamic approaches can capture. Nonetheless, a significant number of edges not detected by dynamic approaches are also reported. Among these, however, there are also edges that are real, but for some reason the unit tests did not execute the branches in which these calls were included.
DOI: 10.5281/zenodo.7104953
title: Appendix for "Is JavaScript Call Graph Extraction Solved Yet? A Comparative Study of Static and Dynamic Tools"

description: <strong>Abstract</strong> The popularity and wide adoption of JavaScript both at the client and server-side makes its code analysis more essential than ever before. Most of the algorithms for vulnerability analysis, coding issue detection, or type inference rely on the call graph representation of the underlying program. Luckily, there are quite a few tools to get this job done already. However, their performance in vitro and especially in vivo has not yet been extensively compared and evaluated. In this paper, we systematically compare five static and two dynamic approaches for building JavaScript call graphs on 26 WebKit SunSpider benchmark programs and two static and two dynamic methods on 12 real-world Node.js modules. The tools under examination using static techniques were <em>npm call graph</em>, <em>IBM WALA</em>, <em>Google Closure Compiler</em>, <em>Approximate Call Graph</em>, and <em>Type Analyzer for JavaScript</em>. We performed dynamic analyzes relying on the <em>nodejs-cg</em> tool (a customized Node.js runtime) and the <em>NodeProf </em>instrumentation and profiling framework. We provide a quantitative evaluation of the results, and a result quality analysis based on 941 manually validated call edges. On the SunSpider programs, which do not take any inputs, so dynamic extraction could be complete, all the static tools also performed well. For example, TAJS found 93% of all edges while having a 97% precision compared to the precise dynamic call graph. When it comes to real-world Node.js modules, our evaluation shows that static tools struggle with parsing the code and fail to detect a significant amount of call edges that dynamic approaches can capture. Nonetheless, a significant number of edges not detected by dynamic approaches are also reported. Among these, however, there are also edges that are real, but for some reason the unit tests did not execute the branches in which these calls were included.
DOI: 10.5281/zenodo.7019663
title: Raw data for the article "Copper-Catalyzed Alkynylation of Hydrazides: An Easy Access to Functionalized Azadipeptides"

description: Raw NMR, IR and MS data for the article "Copper-Catalyzed Alkynylation of Hydrazides: An Easy Access to Functionalized Azadipeptides" published in Organic Letters, DOI: https://doi.org/10.1021/acs.orglett.2c02625 The number of the folders correspond to compounds numbers in the article. All details concerning conditions and equipment for measurements can be found in the supporting information of the article.
DOI: 10.5281/zenodo.7019662
title: Raw data for the article "Copper-Catalyzed Alkynylation of Hydrazides: An Easy Access to Functionalized Azadipeptides"

description: Raw NMR, IR and MS data for the article "Copper-Catalyzed Alkynylation of Hydrazides: An Easy Access to Functionalized Azadipeptides" published in Organic Letters, DOI: https://doi.org/10.1021/acs.orglett.2c02625 The number of the folders correspond to compounds numbers in the article. All details concerning conditions and equipment for measurements can be found in the supporting information of the article.
DOI: 10.6084/m9.figshare.20457177
title: Shark TNFSF repertoire data

description: Data from: Sharks Provide Evidence for a Highly Complex TNFSF Repertoire in the Jawed Vertebrate Ancestor. <br> https://www.jimmunol.org/content/early/2022/09/16/jimmunol.2200300
DOI: 10.6084/m9.figshare.20457177.v1
title: Shark TNFSF repertoire data

description: Data from: Sharks Provide Evidence for a Highly Complex TNFSF Repertoire in the Jawed Vertebrate Ancestor. <br> https://www.jimmunol.org/content/early/2022/09/16/jimmunol.2200300
DOI: 10.5281/zenodo.7104956
title: PredictION: A predictive model to establish the performance of Oxford sequencing reads of SARS-CoV-2

description: Dataset (1) that included 1461 samples and a dataset (2) with 471 samples that was a subset of dataset 1 that included Number of sequenced reads per genome, CT (Cycle threshold) value [N2 target gene], mean coverage depth, coverage genome (percentage), and quantification cDNA (ng/µl).
DOI: 10.5281/zenodo.7104955
title: PredictION: A predictive model to establish the performance of Oxford sequencing reads of SARS-CoV-2

description: Dataset (1) that included 1461 samples and a dataset (2) with 471 samples that was a subset of dataset 1 that included Number of sequenced reads per genome, CT (Cycle threshold) value [N2 target gene], mean coverage depth, coverage genome (percentage), and quantification cDNA (ng/µl).
DOI: 10.5523/bris.za3bxfhxpyct2qsy8ceeriksh
title: Data from Dong et al. 2022. Internal anatomy of a fossilized embryonic stage of the Cambrian-Ordovician scalidophoran Markuelia. Royal Society Open Science

description: This dataset contains 7 Synchrotron Radiation X-ray Tomographic Microscopy (SRXTM) datasets based on specimens of Markuelia hunanensis from the upper Cambrian (Furongian) Bitiao Formation at Wangcun Section, Yongshun County, Hunan Province, South China.
DOI: 10.6084/m9.figshare.21187024
title: Phenology and plasticity can prevent adaptive clines in thermal tolerance across temperate mountains: the importance of the elevation-time axis

description: Rparvi_alt...txt Idem abovein Fstat format ained in methods Rparvi_alt...Fstat.txt Idem above in Fstat format Rparvi_alt...Genepop.txt Idem above in Genepop format Ecol Evol Gutiérrez-Pesquera et Rparv CTs pops.csv CTmax and CTmin for 11 populations Ecol Evol Gutiérrez-Pesquera et CTs Acclimation 5 Pops.csv Acclimation CTmax and CTmin for 5 populations Ecol Evol Gutiérrez-Pesquera et DAILY tmax tmin tmean dr pond micro Hobo 11 Pops.xls Daily pond temperatures for 11 populations <br> <br>
DOI: 10.6084/m9.figshare.21187024.v1
title: Phenology and plasticity can prevent adaptive clines in thermal tolerance across temperate mountains: the importance of the elevation-time axis

description: Rparvi_alt...txt Idem abovein Fstat format ained in methods Rparvi_alt...Fstat.txt Idem above in Fstat format Rparvi_alt...Genepop.txt Idem above in Genepop format Ecol Evol Gutiérrez-Pesquera et Rparv CTs pops.csv CTmax and CTmin for 11 populations Ecol Evol Gutiérrez-Pesquera et CTs Acclimation 5 Pops.csv Acclimation CTmax and CTmin for 5 populations Ecol Evol Gutiérrez-Pesquera et DAILY tmax tmin tmean dr pond micro Hobo 11 Pops.xls Daily pond temperatures for 11 populations <br> <br>
DOI: 10.34740/kaggle/dsv/4237159
title: Politiche 2022 - 34 interviews 20220908-20220921
DOI: 10.6084/m9.figshare.21187183
title: Data_FWB.xlsx

description: We focused on the macrophyte communities living in particular freshwater ecosystems <em>i.e.</em> the ponds of the Iles Kerguelen, in the sub-Antarctic region. This model of freshwater ecosystem is especially abiotically constrained (cold climate), and its plant communities are remarkably species-poor, simplifying the study of plant-plant interactions. Specifically, we measured several abiotic variables of the ponds as well as species spatial patterns, interspecific interactions using the Log Response Ratio metric, and the functional composition of the community using aerial, root and clonal traits. We also determined the biomass of the whole macrophyte community.
DOI: 10.34740/kaggle/dsv/4237280
title: COVID Vaccination in World (updated daily)
DOI: 10.6073/pasta/42dc991ce0f931d06ead9b65bb3dc505
title: Microbial soil data from KBS pilot rainout shelter experiment 2019

description: Growing season drought can be devastating to crop yields. Soil microbial communities have the potential to buffer yield loss under drought through increasing plant drought tolerance and soil water retention. Microbial inoculation on agricultural fields has been shown to increase plant growth, but few studies have examined the impact of microbial inoculation on plant and soil microbial drought tolerance. We conducted a rainout shelter experiment and subsequent greenhouse experiment to explore three objectives. First, we evaluated the performance of a large rainout shelter design for studying drought in agricultural fields. Second, we tested how crop (corn vs. soybean) and microbial inoculation alter the response of soil microbial composition, diversity, and biomass to drought. Third, we tested whether field inoculation treatments and drought exposure altered microbial communities in ways that promote plant drought tolerance in future generations. In our field experiment, the effects of drought on soil bacterial composition depended on crop type, while drought decreased bacterial diversity in corn plots and drought decreased microbial biomass carbon in soybean plots. Microbial inoculation did not alter overall microbial community composition, plant growth or drought tolerance, despite our efforts to address common barriers to inoculation success. Still, a history of inoculation affected growth of future plant generations in the greenhouse. Our study demonstrates the importance of plant species in shaping microbial community responses to drought and the importance of legacy effects of microbial inoculation.